Deep learning (DL) systems present unique challenges in software engineering, especially concerning quality attributes like correctness and resource efficiency. While DL models excel in specific tasks, engineering DL systems is still essential. The effort, cost, and potential diminishing returns of continual improvements must be carefully evaluated, as software engineers often face the critical decision of when to stop refining a system relative to its quality attributes. This experience paper explores the role of MLOps practices―such as monitoring and experiment tracking―in creating transparent and reproducible experimentation environments that enable teams to assess and justify the impact of design decisions on quality attributes. Furthermore, we report on experiences addressing the quality challenges by embedding domain knowledge into the design of a DL model and its integration within a larger system. The findings offer actionable insights into the benefits of domain knowledge and MLOps and the strategic consideration of when to limit further optimizations in DL projects to maximize overall system quality and reliability.

Background: As empirical software engineering evolves, more studies adopt data strategies-approaches that investigate digital artifacts such as models, source code, or system logs rather than relying on human subjects. Synthesizing results from such studies introduces new methodological challenges. Aims: This study assesses the effects of model quantization on correctness and resource efficiency in deep learning (DL) systems. Additionally, it explores the methodological implications of aggregating evidence from empirical studies that adopt data strategies. Method: We conducted a research synthesis of six primary studies that evaluate model quantization. We applied the Structured Synthesis Method (SSM) to aggregate the findings, which combines qualitative and quantitative evidence through diagrammatic modeling. A total of 19 evidence models were extracted and aggregated. Results: The aggregated evidence indicates that model quantization weakly negatively affects correctness metrics while consistently improving resource efficiency metrics, including storage size, inference latency, and GPU energy consumption-a manageable trade-off for many DL deployment contexts. Evidence across quantization techniques remains fragmented, underscoring the need for more focused empirical studies per technique. Conclusions: Model quantization offers substantial efficiency benefits with minor trade-offs in correctness, making it a suitable optimization strategy for resource-constrained environments. This study also demonstrates the feasibility of using SSM to synthesize findings from data strategy-based research.

Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative, neutral), fine-grained emotion classification in app reviews remains underexplored. Fine-grained emotion classification is thus needed to better understand users’ affective responses and support downstream tasks such as feature-emotion analysis, user-oriented release planning, and issue triaging. This paper addresses this gap by identifying and addressing the challenges and limitations in fine-grained emotion analysis in the context of app reviews. Our study adapts Plutchik’s emotion taxonomy to app reviews by developing a structured annotation framework and dataset. Through an iterative human annotation process, we define clear annotation guidelines and document key challenges in emotion classification. Additionally, we evaluate the feasibility of automating emotion annotation using large language models, assessing their cost-effectiveness and agreement with human-labelled data. Our findings reveal that while large language models significantly reduce manual effort and maintain substantial agreement with human annotators, full automation remains challenging due to the complexity of emotional interpretation. This work contributes to opinion mining in requirements engineering by providing structured guidelines, an annotated dataset, and insights for developing automated pipelines to capture the complexity of emotions in app reviews.

Context: Large Language Model (LLM) agents are becoming widely used for various Requirements Engineering (RE) tasks. Research on improving their accuracy mainly focuses on prompt engineering, model fine-tuning, and retrieval augmented generation. However, these methods often treat models as isolated black boxes - relying on single-pass outputs without iterative refinement or collaboration, limiting robustness and adaptability. Objective: We propose that, just as human debates enhance accuracy and reduce bias in RE tasks by incorporating diverse perspectives, different LLM agents debating and collaborating may achieve similar improvements. Our goal is to investigate whether Multi-Agent Debate (MAD) strategies can enhance RE performance. Method: We conducted a systematic study of existing MAD strategies across various domains to identify their key characteristics. To assess their applicability in RE, we implemented and tested a preliminary MAD-based framework for RE classification. Results: Our study identified and categorized several MAD strategies, leading to a taxonomy outlining their core attributes. Our preliminary evaluation demonstrated the feasibility of applying MAD to RE classification. Conclusions: MAD presents a promising approach for improving LLM accuracy in RE tasks. This study provides a foundational understanding of MAD strategies, offering insights for future research and refinements in RE applications.

The latest advancements in machine learning, specifically in foundation models, are revolutionizing the frontiers of existing software engineering (SE) processes. This is a bi-directional phenomenon, where (1) software systems are now challenged to provide AI-enabled features to their users, and (2) AI is used to automate tasks within the software development lifecycle. In an era where sustainability is a pressing societal concern, our community needs to adopt a long-term plan enabling a conscious transformation that aligns with environmental sustainability values. In this article, we reflect on the impact of adopting environmentally friendly practices to create AI-enabled software systems and make considerations on the environmental impact of using foundation models for software development.

Automated program repair (APR) aims to automatize the process of repairing software bugs in order to reduce the cost of maintaining software programs. While APR accuracy has significantly improved in recent years, its energy impact remains unstudied. The field of green software research aims to measure the energy consumption required to develop, maintain, and use software products. Our main goal is to define the foundation for measuring the energy consumption of the APR activity. We state that an environmentally sustainable (or green) APR tool achieves a good balance between the ability to correctly repair bugs and the amount of energy consumed during such process. We measure the energy consumption of 10 traditional APR tools for Java and 11 fine-tuned large-language models (LLM) trying to repair real bugs from Defects4J. The results of this study show the existing tradeoff between energy consumption and repairability. In particular, APR tools such as TBar and RepairLlama repair more bugs than other approaches at the expense of a higher energy consumption. Other tools, such as SimFix and the LLM CodeT5-large, provide a good tradeoff between energy consumption and repairability. We also present guidelines consisting of a set of recommendations for developing greener APR.

Hugging Face (HF) has established itself as a crucial platform for the development and sharing of machine learning (ML) models. This repository mining study, which delves into more than 380,000 models using data gathered via the HF Hub API, aims to explore the community engagement, evolution, and maintenance around models hosted on HF - aspects that have yet to be comprehensively explored in the literature. We first examine the overall growth and popularity of HF, uncovering trends in ML domains, framework usage, authors grouping and the evolution of tags and datasets used. Through text analysis of model card descriptions, we also seek to identify prevalent themes and insights within the developer community. Our investigation further extends to the maintenance aspects of models, where we evaluate the maintenance status of ML models, classify commit messages into various categories (corrective, perfective, and adaptive), analyze the evolution across development stages of commits metrics and introduce a new classification system that estimates the maintenance status of models based on multiple attributes. This study aims to provide valuable insights about ML model maintenance and evolution that could inform future model development strategies on platforms like HF.

Change-proneness of software artifacts has been mainly related to the design characteristics and their previous history of changes. While these two aspects are essential and contribute significantly to the prediction, they leave out a critical factor: how the artifacts are being used. In the context of web APIs, consumers represent one of the main drivers of the change. Therefore, we propose a methodology for predicting the change-proneness of web API endpoint interfaces, taking into account not only design and change history but also their usage. Since the evolution of web APIs is and should be usage-driven, the way consumers use an API affects the future changes implemented by providers. Consequently, consumers' usage behavior contains essential information that contributes to identifying endpoints that are more prone to change. By considering the reasons behind changes, we introduce a set of metrics comprising design and usage aspects to be used as variables in prediction. To demonstrate the usefulness of the approach we perform an initial evaluation using a real-world web API. We quantify the introduced metrics using web API documentation, code, and usage logs in order to build a classifier able to predict with 82 % accuracy if an endpoint will change based on its design, history of changes, and usage characteristics.

Mobile app reviews are a large-scale data source for software-related knowledge generation activities, including software maintenance, evolution and feedback analysis. Effective extraction of features (i.e., functionalities or characteristics) from these reviews is key to support analysis on the acceptance of these features, identification of relevant new feature requests and prioritization of feature development, among others. Traditional methods focus on syntactic pattern-based approaches, typically context-agnostic, evaluated on a closed set of apps, difficult to replicate and limited to a reduced set and domain of apps. Mean-while, the pervasiveness of Large Language Models (LLMs) based on the Transformer architecture in software engineering tasks lays the groundwork for empirical evaluation of the performance of these models to support feature extraction. In this study, we present T-FREX, a Transformer-based, fully automatic approach for mobile app review feature extraction. First, we collect a set of ground truth features from users in a real crowdsourced software recommendation platform and transfer them automatically into a dataset of app reviews. Then, we use this newly created dataset to fine-tune multiple LLMs on a named entity recognition task under different data configurations. We assess the performance of T- FREX with respect to this ground truth, and we complement our analysis by comparing T- FREX with a baseline method from the field. Finally, we assess the quality of new features predicted by T- FREX through an external human evaluation. Results show that T- FREX outperforms on average the traditional syntactic-based method, especially when discovering new features from a domain for which the model has been fine-tuned.

Abstract Context Nowadays software-development organizations are urged to exploit their data for empowering their decision-making processes. Such data may be used to monitor the status of meaningful software indicators (e.g., software quality, productivity and on-time delivery) that are relevant for their decision-making processes. Forecasting the values of such indicators may provide evidence of a potentially high risk or opportunity that could help to anticipate actions accordingly. Most of the existing forecasting proposals in software engineering use open-source data rather than data from industrial projects. Therefore, there is a lack of evidence on how these proposals fit the particular needs of a software-development organization and how they can be automated into the organization’s infrastructure. Objective To enable software indicators´ forecasting in a software-development organization (Modeliosoft). Method We designed an industry-academia collaboration based on Action Design Research (ADR) to address Modeliosoft’s forecasting challenges. Results A tool-supported method called FOSI (Forecasting Of Software Indicators) for enabling forecasting in Modeliosoft. We obtained positive results regarding its suitability and technical feasibility in a pilot project of the organization. In addition, we provide details and reflections on the potential usefulness of the method for addressing similar field problems. Conclusions The procedures and results detailed in this paper are valuable to: 1) address Modeliosoft’s forecasting challenges 2) inspire other software-development organizations on how to deal with similar problems and even reuse some procedures and software support tools resulted from this work, 3) promote the win-win benefits of industry-academia collaborations.

Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Despite its empirical vocation, RE research has given limited attention to replication of NLP for RE studies. Replication is hampered by several factors, including the context specificity of the studies, the heterogeneity of the tasks involving NLP, the tasks’ inherent hairiness , and, in turn, the heterogeneous reporting structure. To address these issues, we propose a new artifact, referred to as ID-Card , whose goal is to provide a structured summary of research papers emphasizing replication-relevant information. We construct the ID-Card through a structured, iterative process based on design science. In this article: (i) we report on hands-on experiences of replication; (ii) we review the state-of-the-art and extract replication-relevant information: (iii) we identify, through focus groups, challenges across two typical dimensions of replication: data annotation and tool reconstruction; and (iv) we present the concept and structure of the ID-Card to mitigate the identified challenges. This study aims to create awareness of replication in NLP for RE. We propose an ID-Card that is intended to foster study replication but can also be used in other contexts, e.g., for educational purposes.

Background: The rise of machine learning (ML) systems has exacerbated their carbon footprint due to increased capabilities and model sizes. However, there is scarce knowledge on how the carbon footprint of ML models is actually measured, reported, and evaluated. Aims: This paper analyzes the measurement of the carbon footprint of 1,417 ML models and associated datasets on Hugging Face. Hugging Face is the most popular repository for pretrained ML models. We aim to provide insights and recommendations on how to report and optimize the carbon efficiency of ML models. Method: We conduct the first repository mining study on the Hugging Face Hub API on carbon emissions and answer two research questions: (1) how do ML model creators measure and report carbon emissions on Hugging Face Hub?, and (2) what aspects impact the carbon emissions of training ML models? Results: Key findings from the study include a stalled proportion of carbon emissions-reporting models, a slight decrease in reported carbon footprint on Hugging Face over the past 2 years, and a continued dominance of NLP as the main application domain reporting emissions. The study also uncovers correlations between carbon emissions and various attributes, such as model size, dataset size, ML application domains and performance metrics. Conclusions: The results emphasize the need for software measurements to improve energy reporting practices and the promotion of carbon-efficient model development within the Hugging Face community. To address this issue, we propose two classifications: one for categorizing models based on their carbon emission reporting practices and another for their carbon efficiency. With these classification proposals, we aim to encourage transparency and sustainable model development within the ML community.

Nowadays, AI-based systems have achieved outstanding results and have outperformed humans in different domains. However, the processes of training AI models and inferring from them require high computational resources, which pose a significant challenge in the current energy efficiency societal demand. To cope with this challenge, this research project paper describes the main vision, goals, and expected outcomes of the GAISSA project. The GAISSA project aims at providing data scientists and software engineers tool-supported, architecture-centric methods for the modelling and development of green AI-based systems. Although the project is in an initial stage, we describe the current research results, which illustrate the potential to achieve GAISSA objectives.

Current research in the computer vision field mainly focuses on improving Deep Learning (DL) correctness and inference time performance. However, there is still little work on the huge carbon footprint that has training DL models. This study aims to analyze the impact of the model architecture and training environment when training greener computer vision models. We divide this goal into two research questions. First, we analyze the effects of model architecture on achieving greener models while keeping correctness at optimal levels. Second, we study the influence of the training environment on producing greener models. To investigate these relationships, we collect multiple metrics related to energy efficiency and model correctness during the models’ training. Then, we outline the trade-offs between the measured energy efficiency and the models’ correctness regarding model architecture, and their relationship with the training environment. We conduct this research in the context of a computer vision system for image classification. In conclusion, we show that selecting the proper model architecture and training environment can reduce energy consumption dramatically (up to 98.83%) at the cost of negligible decreases in correctness. Also, we find evidence that GPUs should scale with the models’ computational complexity for better energy efficiency.

As the use of Application Programming Interfaces (APIs) is increasingly growing, their evolution becomes more challenging in terms of the service provided according to consumers’ needs. In this paper, we address the role of consumers’ needs in WAPIs evolution and introduce a process mining pattern-based method to support providers in WAPIs evolution by analyzing and understanding consumers’ behavior, imprinted in WAPI usage logs. We take the position that WAPIs’ evolution should be mainly usage-based, i.e., the way consumers use them should be one of the main drivers of their changes. We start by characterizing the structural relationships between endpoints, and next, we summarize these relationships into a set of behavioral patterns (i.e., usage patterns whose occurrences indicate specific consumers’ behavior like repetitive or consecutive calls), that can potentially imply the need for changes (e.g., creating new parameters for endpoints, merging endpoints). We analyze the logs and extract several metrics for the endpoints and their relationships, to then detect the patterns. We apply our method in two real-world WAPIs from different domains, education, and health, respectively the WAPI of Barcelona School of Informatics at the Polytechnic University of Catalonia (Facultat d’Informàtica de Barcelona, FIB, UPC), and District Health Information Software 2 (DHIS2) WAPI. The feedback from consumers and providers of these WAPIs proved the effectiveness of the detected patterns and confirmed the promising potential of our approach.

Abstract Requirements specification is a core activity in the requirements engineering phase of a software development project. Researchers have contributed extensively to the field of requirements specification, but the extent to which their proposals have been adopted in practice remains unclear. We gathered evidence about the state of practice in requirements specification by focussing on the artefacts used in this activity, the application of templates or guidelines, how requirements are structured in the specification document, what tools practitioners use to specify requirements, and what challenges they face. We conducted an interview-based survey study involving 24 practitioners from 12 different Swedish IT companies. We recorded the interviews and analysed these recordings, primarily by using qualitative methods. Natural language constitutes the main specification artefact but is usually accompanied by some other type of instrument. Most requirements specifications use templates or guidelines, although they seldom follow any fixed standard. Requirements are always structured in the document according to the main functionalities of the system or to project areas or system parts. Different types of tools, including MS Office tools, are used, either individually or combined, in the compilation of requirements specifications. We also note that challenges related to the use of natural language (dealing with ambiguity, inconsistency, and incompleteness) are the most frequent challenges that practitioners face in the compilation of requirements specifications. These findings are contextualized in terms of demographic factors related to the individual interviewees, the organization they are affiliated with, and the project they selected to discuss during our interviews. A number of our findings have been previously reported in related studies. These findings show that, in spite of the large number of notations, models and tools proposed from academia for improving requirements specification, practitioners still mainly rely on plain natural language and general-purpose tool support. We expect more empirical studies in this area in order to better understand the reason of this low adoption of research results.

Issue trackers, such as Jira, have become the prevalent collaborative tools in software engineering for managing issues, such as requirements, development tasks, and software bugs. However, issue trackers inherently focus on the lifecycle of single issues, although issues have and express dependencies on other issues that constitute issue dependency networks in large complex collaborative projects. The objective of this study is to develop supportive solutions for the improved management of dependent issues in an issue tracker. This study follows the Design Science methodology, consisting of eliciting drawbacks and constructing and evaluating a solution and system. The study was carried out in the context of The Qt Company's Jira, which exemplifies an actively used, almost two-decade-old issue tracker with over 100,000 issues. The drawbacks capture how users operate with issue trackers to handle issue information in large, collaborative, and long-lived projects. The basis of the solution is to keep issues and dependencies as separate objects and automatically construct an issue graph. Dependency detections complement the issue graph by proposing missing dependencies, while consistency checks and diagnoses identify conflicting issue priorities and release assignments. Jira's plugin and service-based system architecture realize the functional and quality concerns of the system implementation. We show how to adopt the intelligent supporting techniques of an issue tracker in a complex use context and a large data-set. The solution considers an integrated and holistic system view, practical applicability and utility, and the practical characteristics of issue data, such as inherent incompleteness.

In despite of agile and rapid software development (ARSD) being researched and applied extensively, managing quality requirements (QRs) are still challenging. As ARSD processes produce a large amount of data, measurement has become a strategy to facilitate QR management. This study aims to survey the literature related to QR management through metrics in ARSD, focusing on: bibliometrics, QR metrics, and quality-related indicators used in quality management. The study design includes the definition of research questions, selection criteria, and snowballing as search strategy. We selected 61 primary studies (2001–2019). Despite a large body of knowledge and standards, there is no consensus regarding QR measurement. Terminology is varying as are the measuring models. However, seemingly different measurement models do contain similarities. The industrial relevance of the primary studies shows that practitioners have a need to improve quality measurement. Our collection of measures and data sources can serve as a starting point for practitioners to include quality measurement into their decision-making processes. Researchers could benefit from the identified similarities to start building a common framework for quality measurement. In addition, this could help researchers identify what quality aspects need more focus, e.g., security and usability that have surprisingly few metrics reported.

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image- and speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.

The significant momentum and importance of Mining Software Repositories (MSR) in Software Engineering (SE) has fostered new opportunities and challenges for extensive empirical research. However, MSR researchers seem to struggle to characterize the empirical methods they use into the existing empirical SE body of knowledge. This is especially the case of MSR experiments. To provide evidence on the special characteristics of MSR experiments and their differences with experiments traditionally acknowledged in SE so far, we elicited the hallmarks that differentiate an experiment from other types of empirical studies and characterized the hallmarks and types of experiments in MSR. We analyzed MSR literature obtained from a small-scale systematic mapping study to assess the use of the term experiment in MSR. We found that 19% of the papers claiming to be an experiment are indeed not an experiment at all but also observational studies, so they use the term in a misleading way. From the remaining 81% of the papers, only one of them refers to a genuine controlled experiment while the others stand for experiments with limited control. MSR researchers tend to overlook such limitations, compromising the interpretation of the results of their studies. We provide recommendations and insights to support the improvement of MSR experiments.

Context. The use of standards is considered a vital part of any engineering discipline. So one could expect that standards play an important role in Requirements Engineering (RE) as well. However, little is known about the actual knowledge and use of RE-related standards in industry.Objective. In this article, we investigate to which extent standards and related artifacts such as templates or guidelines are known and used by RE practitioners.Method. To this end, we have conducted a questionnaire-based online survey. We could analyze the replies from 90 RE practitioners using a combination of closed and open-text questions.Results. Our results indicate that the knowledge and use of standards and related artifacts in RE is less widespread than one might expect from an engineering perspective. For example, about 47% of the respondents working as requirements engineers or business analysts do not know the core standard in RE, ISO/IEC/IEEE 29148. Participants in our study mostly use standards by personal decision rather than being imposed by their respective company, customer, or regulator. Beyond insufficient knowledge, we also found cultural and organizational factors impeding the widespread adoption of standards in RE.Conclusions. Overall, our results provide empirically informed insights into the actual use of standards and related artifacts in RE practice and – indirectly – about the value that the current standards create for RE practitioners.

Context: The relevance of Requirements Engineering (RE) research to practitioners is vital for a long-term dissemination of research results to everyday practice. Some authors have speculated about a mismatch between research and practice in the RE discipline. However, there is not much evidence to support or refute this perception.Objective: This article presents the results of a study aimed at gathering evidence from practitioners about their perception of the relevance of RE research and at understanding the factors that influence that perception.Method: We conducted a questionnaire-based survey of industry practitioners with expertise in RE. The participants rated the perceived relevance of 435 scientific papers presented at five top RE-related conferences.Results: The 153 participants provided a total of 2,164 ratings. The practitioners rated RE research as essential or worthwhile in a majority of cases. However, the percentage of non-positive ratings is still higher than we would like. Among the factors that affect the perception of relevance are the research's links to industry, the research method used, and respondents’ roles. The reasons for positive perceptions were primarily related to the relevance of the problem and the soundness of the solution, while the causes for negative perceptions were more varied. The respondents also provided suggestions for future research, including topics researchers have studied for decades, like elicitation or requirement quality criteria.Conclusions: The study is valuable for both researchers and practitioners. Researchers can use the reasons respondents gave for positive and negative perceptions and the suggested research topics to help make their research more appealing to practitioners and thus more prone to industry adoption. Practitioners can benefit from the overall view of contemporary RE research by learning about research topics that they may not be familiar with, and compare their perception with those of their colleagues to self-assess their positioning towards more academic research.

Context: Coordination in large-scale software development is critical yet difficult, as it faces the problem of dependency management and resolution. In this work, we focus on managing requirement dependencies that in Agile software development (ASD) come in the form of user stories. Objective: This work studies decisions of large-scale Agile teams regarding identification of dependencies between user stories. Our goal is to explain detection of dependencies through users’ behavior in large-scale, distributed projects. Method: We perform empirical evaluation on a large real-world dataset from an Agile software organization, provider of a leading software for Agile project management. We mine the usage data of the Agile Lifecycle Management (ALM) tool to extract large-scale development project data for more than 70 teams running over a five-year period. Results: Our results demonstrate that dependencies among user stories are not frequently observed (the problem affects around 10% of user stories), however, their implications on large-scale ASD are considerable. Dependencies have impact on software releases and increase work coordination complexity for members of different teams. Conclusion: Requirement dependencies undermine Agile teams’ autonomy and are difficult to manage at scale. We conclude that leveraging ALM monitoring data to automatically detect dependencies could help Agile teams address work coordination needs and manage risks related to dependencies in a timely manner.

Context: Tertiary studies in software engineering ([email protected]) are widely used to synthesise evidence on a research topic systematically. As part of their protocol, [email protected] define inclusion and exclusion criteria (IC/EC) aimed at selecting those secondary studies (SS) to be included in the analysis. Aims: To provide a state of the art on the definition and application of IC/EC in [email protected], and from the results of this analysis, we outline an emerging framework, TSICEC, to be used by SE researchers. Method: To provide the state of the art, we conducted a systematic mapping (SM) combining automatic search and snowballing over the body of SE scientific literature, which led to 50 papers after application of our own IC/EC. The extracted data was synthesised using content analysis. The results were used to define a first version of TSICEC. Results: The SM resulted in a coding schema, and a thorough analysis of the selected papers on the basis of this coding. Our TSICEC framework includes guidelines for the definition of IC/EC in [email protected] Conclusion: This paper is a step forward establishing a foundation for researchers in two ways. As authors, understanding the different possibilities to define IC/EC and apply them to select SS. As readers, having an instrument to understand the methodological rigor upon which [email protected] may claim their findings.

Ubiquitous digitalization has led to the continuous generation of large amounts of digital data, both in organizations and in society at large. In the requirements engineering community, there has been a growing interest in considering digital data as new sources for requirements elicitation, in addition to stake-holders. The volume, dynamics, and variety of data makes iterative requirements elicitation increasingly continuous, but also unstructured and complex, which current agile methods are unable to consider and manage in a systematic and efficient manner. There is also the need to support software evolution by enabling a synergy of stakeholder-driven requirements elicitation and management with data-driven approaches. In this study, we propose extension of agile requirements elicitation by applying situational method engineering. The research is grounded on two studies in the business domains of video games and online banking.

Abstract Agile methods can deliver software that fulfills customer needs rapidly and continuously. Quality requirements (QRs) are important in this regard; however, detailed studies on how companies applying agile methods to manage QRs are limited, as are studies on the rationale for choosing specific QR management practices and related challenges. The aim of this study was to address why practitioners manage QRs as they do and what challenges they face. We also analyzed how existing practices mitigate some of the found challenges. Lastly, we connect the contextual elements of the companies with their practices and challenges. We conducted 36 interviews with practitioners from four companies of varying sizes. Since each company operates in different domains, comparing QR management strategies and related challenges in different contexts was possible. We found that the companies apply proactive, reactive, and interactive strategies to manage QRs. Additionally, our study revealed 40 challenges in six categories that companies applying agile methods may face in QR management. We also identified nine contextual elements that affect QR management practice choices and which, importantly, can explain many related challenges. Based on these findings, we constructed a theoretical model about the connection between context, QR management practices, and challenges. Practitioners in similar contexts can learn from the practices identified in this study. Our preliminary theoretical model can help other practitioners identify what challenges they can expect to face in QR management in different developmental contexts as well as which practices to apply to mitigate these challenges.

Context: Managing Non-Functional Requirements (NFRs) in software projects is challenging, and projects that adopt Model-Driven Development (MDD) are no exception. Although several methods and techniques have been proposed to face this challenge, there is still little evidence on how NFRs are handled in MDD by practitioners. Knowing more about the state of the practice may help researchers to steer their research and practitioners to improve their daily work. Objective: In this paper, we present our findings from an interview-based survey conducted with practitioners working in 18 different companies from 6 European countries. From a practitioner's point of view, the paper shows what barriers and benefits the management of NFRs as part of the MDD process can bring to companies, how NFRs are supported by MDD approaches, and which strategies are followed when (some) types of NFRs are not supported by MDD approaches. Results: Our study shows that practitioners perceive MDD adoption as a complex process with little to no tool support for NFRs, reporting productivity and maintainability as the types of NFRs expected to be supported when MDD is adopted. But in general, companies adapt MDD to deal with NFRs. When NFRs are not supported, the generated code is sometimes changed manually, thus compromising the maintainability of the software developed. However, the interviewed practitioners claim that the benefits of using MDD outweight the extra effort required by these manual adaptations. Conclusion: Overall, the results indicate that it is important for practitioners to handle `NFRs in MDD, but further research is necessary in order to lower the barrier for supporting a broad spectrum of NFRs with MDD. Still, much conceptual and tool implementation work seems to be necessary to lower the barrier of integrating the broad spectrum of NFRs in practice.

Requirements Engineering (RE) represents a critical phase in the management and planning of software projects.One of the main reasons for project failure is missing or incomplete RE.In order to reduce the risk of project failure, there exists a high and urgent demand for applying intelligent technologies in RE.Since the RE process is mainly decision-and community-driven, Recommender Systems are supposed to be applied in this particular context to support stakeholders in decision-making and, hence, to increase the quality of the decisions taken by the stakeholders.This paper introduces a variety of innovative recommendation tools developed within the scope of the European Horizon 2020 research project OPENREQ.Moreover, we give an overview of user studies conducted to evaluate our approaches and present final results of selected studies.The study results indicate that the developed concepts have the potential to significantly improve the quality of requirements definition and requirements prioritization.

Application Programming Interfaces (APIs) are means of communication between applications, hence they can be seen as user interfaces, just with different kind of users, i.e., software or computers. However, the very first consumers of the APIs are humans, namely programmers. Based on the available documentation and the "ease of use" perception (sometimes led by corporate decisions and/or restrictions) they decide to use or not a specific API. In this paper, we propose a data-driven approach to measure web API usability, expressed through the predicted error rate. Following the reviewed state of the art in API usability, we identify a set of usability attributes, and for each of them we propose indicators that web API providers should refer to when developing usable web APIs. Our focus in this paper is on those indicators that can be quantified using the API logs, which indeed reflect the actual behaviour of programmers. Next, we define metrics for the aforementioned indicators, and exemplify them in our use case, applying them on the logs from the web API of District Health Information System (DHIS2) used at World Health Organization (WHO). Using these metrics as features, we build a classifier model to predict the error rate of API endpoints. Besides finding usability issues, we also drill down into the usage logs and investigate the potential causes of these errors.

Context: Incomplete or incorrect detection of requirement dependencies has proven to result in reduced release quality and substantial rework. Additionally, the extraction of dependencies is challenging since requirements are mostly documented in natural language, which makes it a cognitively difficult task. Moreover, with ever-changing and new requirements, a manual analysis process must be repeated, which imposes extra hardship even for domain experts. Objective: The three main objectives of this research are: 1) Proposing a new dependency extraction method using a variant of Active Learning (AL). 2) Evaluating this AL and Ontology-based Retrieval (OBR) as baseline methods for dependency extraction on the two industrial data sets. 3) Analyzing the value gained from integrating these diverse approaches to form two hybrid methods. Method: Building on the general AL, ensemble and semi-supervised machine learning, a variant of AL was developed, which was further integrated with OBR to form two hybrid methods (Hybrid1, Hybrid2) for extracting three types of dependencies (requires, refines, other): Hybrid1 used OBR as a substitute for human expert; Hybrid2 used dependencies extracted through the OBR as an additional input for training set in AL. Results: For two industrial case studies, AL extracted more dependencies than OBR. Hybrid1 showed improvement for both data sets. For one of them, F1 score increased to 82.6% compared to the AL baseline score of 49.9%. Hybrid2 increased the accuracy by 25% to the level of 75.8% compared to the AL baseline accuracy. OBR also complemented the AL approach by reducing 50% of the human effort.

Applications typically communicate with each other, accessing and exposing data and features by using Application Programming Interfaces (APIs). Even though API consumers expect APIs to be steady and well established, APIs are prone to continuous changes, experiencing different evolutive phases through their lifecycle. These changes are of different types, caused by different needs and are affecting consumers in different ways. In this paper, we identify and classify the changes that often happen to APIs, and investigate how all these changes are reflected in the documentation, release notes, issue tracker and API usage logs. The analysis of each step of a change, from its implementation to the impact that it has on API consumers, will help us to have a bigger picture of API evolution. Thus, we review the current state of the art in API evolution and, as a result, we define a classification framework considering both the changes that may occur to APIs and the reasons behind them. In addition, we exemplify the framework using a software platform offering a Web API, called District Health Information System (DHIS2), used collaboratively by several departments of World Health Organization (WHO).

Background: Requirement engineering is often considered a critical activity in system development projects. The increasing complexity of software as well as number and heterogeneity of stakeholders motivate the development of methods and tools for improving large-scale requirement engineering. Aims: The empirical study presented in this paper aim to identify and understand the characteristics and challenges of a platform, as desired by experts, to support requirement engineering for individual stakeholders, based on the current pain-points of their organizations when dealing with a large number requirements. Method: We conducted a multiple case study with three companies in different domains. We collected data through ten semi-structured interviews with experts from these companies. Results: The main pain-point for stakeholders is handling the vast amount of data from different sources. The foreseen platform should leverage such data to manage changes in requirements according to customers' and users' preferences. It should also offer stakeholders an estimation of how long a requirements engineering task will take to complete, along with an easier requirements dependency identification and requirements reuse strategy. Conclusions: The findings provide empirical evidence about how practitioners wish to improve their requirement engineering processes and tools. The insights are a starting point for in-depth investigations into the problems and solutions presented. Practitioners can use the results to improve existing or design new practices and tools.

Context: Software evolution ensures that software systems in use stay up to date and provide value for end-users. However, it is challenging for requirements engineers to continuously elicit needs for systems used by heterogeneous end-users who are out of organisational reach. Objective: We aim at supporting continuous requirements elicitation by combining user feedback and usage monitoring. Online feedback mechanisms enable end-users to remotely communicate problems, experiences, and opinions, while monitoring provides valuable information about runtime events. It is argued that bringing both information sources together can help requirements engineers to understand end-user needs better. Method/Tool: We present FAME, a framework for the combined and simultaneous collection of feedback and monitoring data in web and mobile contexts to support continuous requirements elicitation. In addition to a detailed discussion of our technical solution, we present the first evidence that FAME can be successfully introduced in real-world contexts. Therefore, we deployed FAME in a web application of a German small and medium-sized enterprise (SME) to collect user feedback and usage data. Results/Conclusion: Our results suggest that FAME not only can be successfully used in industrial environments but that bringing feedback and monitoring data together helps the SME to improve their understanding of end-user needs, ultimately supporting continuous requirements elicitation.

The relevance of Requirements Engineering (RE) research to practitioners is a prerequisite for problem-driven research in the area and key for a long-term dissemination of research results to everyday practice. To understand better how industry practitioners perceive the practical relevance of RE research, we have initiated the RE-Pract project, an international collaboration conducting an empirical study. This project opts for a replication of previous work done in two different domains and relies on survey research. To this end, we have designed a survey to be sent to several hundred industry practitioners at various companies around the world and ask them to rate their perceived practical relevance of the research described in a sample of 418 RE papers published between 2010 and 2015 at the RE, ICSE, FSE, ESEC/FSE, ESEM and REFSQ conferences. In this paper, we summarize our research protocol and present the current status of our study and the planned future steps.

Software release planning is the activity of deciding what is to be implemented, when and by who. It can be divided into two tasks: strategic planning (i.e., the what) and operational (i.e., the when and the who). Replan, the tool that we present in this demo, handles both tasks in an integrated and flexible way, allowing its users (typically software product managers and developer team leaders) to (re)plan the releases dynamically by assigning new features and/or modifying the available resources allocated at each release. A recorded video demo of Replan is available at https://youtu.be/PNK5EUTdqEg.

Continuous software engineering is a new trend that is gaining increasing attention of the research community in the last years. The main idea behind this trend is to tighten the connection between the software engineering lifecycle activities (e.g., development, planning, integration, testing, etc.). While the connection between development and integration (i.e., continuous integration) has been subject of research and is applied in industrial settings, the connection between other activities is still in a very early stage. We are contributing to this research topic by proposing our ideas towards connecting the software development and software release planning activities (i.e., continuous software release planning). In this paper we present our initial findings on this topic, how we envision to address the continuous software release planning, and a research agenda to fulfil our objectives.

Service-oriented computing and context-aware computing are two consolidated paradigms that are changing the way of providing and consuming software services. Whilst service-oriented computing is based on service-oriented architectures for providing flexible software services, context-aware computing articulates different phases of a context life cycle for changing the behavior of such services. The synergy between both paradigms provides the context to this study. This study analyzes the current state of the art of context models, specifically: (1) which are these proposals and how are they related; (2) what are their structural characteristics; (3) what context information is the most addressed; and (4) what are their most consolidated definitions. Given their dominance on the field, the study focuses on ontology-based approaches. We conducted a systematic mapping by establishing a review protocol that integrates automatic and manual searches from different sources. We applied a rigorous method to elicit the keywords from the research questions and selection criteria to retrieve the papers to evaluate. Overall, 138 primary studies were selected to answer our research questions. These proposals were studied in depth by analyzing: 1) distribution along time and their relationships; 2) size correlated with the number of classes and levels of the context model, and coverage of the definitions provided as indicator of quality provided; 3) most addressed context information; 4) most consolidated definitions of context information. The contribution of this survey is to make available a unified and consolidated body of knowledge on context for service-oriented computing that could be instantiated and used as starting point in a variety of use cases. This sweeping view on the anatomy of context models may help avoiding the postulation of new proposals not aligned with the current research.

Context: Several empirical studies investigated the benefits and drawbacks of acquiring a Software Reference Architecture (SRA) to construct a family of software systems with similar architectural needs. However, these empirical results have not been synthesized by any study yet. Such synthesized evidence is essential to make informed decisions whether or not to adopt an SRA in an organization. Goal: To aggregate existing empirically- grounded evidence about the benefits and drawbacks of SRAs, aiming at supporting organizations' decision making on their adoption. Method: To identify primary studies in the technical literature through a systematic literature review, and then, use the Structured Synthesis Method (SSM) to aggregate qualitative and quantitative evidence through the use of diagrammatic models. Results: From the five identified primary studies, five SRA benefits have considerably increased their belief value after aggregation: interoperability of software systems, reduced development costs, improved communication among stakeholders, reduced risk, and reduced time- to-market. Also, one drawback of SRAs has increased its belief value: the required learning curve for developers. Conclusions: The aggregated results consolidate knowledge and confidence on some of the studied SRA effects. The commonly reported effects showed a clear increment of their belief and pointed out to broader generalization. The effects that did not show any belief increment are important to detect areas requiring further evidence to reach a higher degree of consolidation. Practitioners might benefit from these results to support the decision of adopting an SRA in practice.

Software requirement patterns are an increasingly popular approach to knowledge reuse in the requirements engineering phase. Several research proposals have been formulated in the last years, and this technical briefing presents them. Beyond that, a report on the current adoption of these proposals (or any other ad-hoc approach) in industry is presented. This state of the practice will show that the need to pave the road to successful adoption still persists.

Few would deny today the importance of empirical studies in the field of Software Engineering (SE) and, indeed, an increasing number of studies are being conducted involving the software industry. While literature abounds on empirical procedures, relatively little is known about the dynamics and complexity of conducting empirical studies in the software industry. What are the impediments and how to best handle them? This driver underlies the organisation of the third in a series of workshops, CESI 2015. Apart from structured presentations and discussions from academic and industry participants, this workshop (like predecessor workshops) includes a "wall of ideas" session where all participants asynchronously post their ideas on the wall, literally, which are then analysed. As a tangible output, the workshop's discussions will be summarised in a post-workshop report.

Model-Driven Development (MDD) is no longer a novel development paradigm. It has become mature from a research perspective and recent studies show its adoption in industry. Still, some issues remain a challenge. Among them, we are interested in the treatment of non-functional requirements (NFRs) in MDD processes. Very few MDD approaches have been reported to deal with NFRs (and they do it in a limited way). However, it is clear that NFRs need to be considered somehow in the final product of the MDD process. To better understand how NFRs are integrated into the existing MDD approaches, we have initiated the NFR4MDD project, a multi-national empirical study, based on interviews with companies working on MDD projects. Our project aims at surveying the state of the practice for this topic. In this paper, we summarize our research protocol and present the current status of our study. The discussion will focus on the peculiarities of our study's context and organization involving about 20 researchers from 8 European countries.

Self-adaptive systems are capable of dealing with uncertainty at runtime handling complex issues as resource variability, changing user needs, and system intrusions or faults. If the requirements depend on context, runtime uncertainty will affect the execution of these contextual requirements. This work presents SACRE, a proof-of-concept implementation of an existing approach, ACon, developed by researchers of the Univ. of Victoria (Canada) in collaboration with the UPC (Spain). ACon uses a feedback loop to detect contextual requirements affected by uncertainty and data mining techniques to determine the best operationalization of contexts on top of sensed data. The implementation is placed in the domain of smart vehicles and the contextual requirements provide functionality for drowsy drivers.

Free Libre Open Source Software (FLOSS) has become a strategic asset in software development, and open source communities behind FLOSS are a key player in the field. The analysis of open source community dynamics is a key capability in risk management practices focused on the integration of FLOSS in all types of organizations. We are conducting research in developing methodologies for managing risks of FLOSS adoption and deployment in various application domains. This paper is about the ability to systematically capture, filter, analyze, reason about, and build theories upon, the behavior of an open source community in combination with the structured elicitation of expert opinions on potential organizational business risk. The novel methodology presented here blends together qualitative and quantitative information as part of a wider analytics platform. The approach combines big data analytics with automatic scripting of scenarios that permits experts to assess risk indicators and business risks in focused tactical and strategic workshops. These workshops generate data that is used to construct Bayesian networks that map data from community risk drivers into statistical distributions that are feeding the platform risk management dashboard. A special feature of this model is that the dynamics of an open source community are tracked using social network metrics that capture the structure of unstructured chat data. The method is illustrated with a running example based on experience gained in implementing our approach in an academic smart environment setting including Mood bile, a Mobile Learning for Moodle (www.moodbile.org). This example is the first in a series of planned experiences in the domain of smart environments with the ultimate goal of deriving a complete risk model in that field.

Context: Software reference architectures (SRA) have emerged as an approach to systematically reuse architectural knowledge and software elements in the development of software systems. Over the last years, research has been conducted to uncover the artifacts that SRAs provide in order to build software systems. However, empirical studies have not focused on providing industrial evidence about such artifacts. Aim: This paper investigates which artifacts constitute an SRA, how SRAs are designed, the potential reuse of SRA's artifacts, and how they are used in practice. Method: The study consists of a case study made in collaboration with a multinational consulting company that designs SRAs for diverse client organizations. A total of nine European client organizations that use an SRA participated in the study. We analyzed available documentation and contacted 28 practitioners. Results: In the nine analyzed projects, we observed that the artifacts that constitute an SRA are mainly software elements, guidelines and documentation. The design and implementation of SRAs are influenced by the reuse of artifacts from previous software system development and experiences, and the reuse of an SRA across different business domains may be possible when they are platform-oriented. Regarding SRAs usage, we observed that conformance checking is seldom performed. Conclusions: This study reports artifacts of SRAs as stated by practitioners in order to help software architects and scientists in the inception, design, and application of SRAs.

Background/Context: The objective of achieving higher sustainability in our lifestyles by information and communication technology has lead to a plethora of research activities in related fields. Consequently, Software Engineering for Sustainability (SE4S) has developed as an active area of research. Objective/Aim: Though SE4S gained much attention over the past few years and has resulted in a number of contributions, there is only one rigorous survey of the field. We follow up on this systematic mapping study from 2012 with a more in-depth overview of the status of research, as most work has been conducted in the last 4 years. Method: The applied method is a systematic mapping study through which we investigate which contributions were made, which knowledge areas are most explored, and which research type facets have been used, to distill a common understanding of the state-of-the-art in SE4S. Results: We contribute an overview of current research topics and trends, and their distribution according to the research type facet and the application domains. Furthermore, we aggregate the topics into clusters and list proposed and used methods, frameworks, and tools. Conclusion: The research map shows that impact currently is limited to few knowledge areas and there is need for a future roadmap to fill the gaps.

Nowadays services as those provided by smart cities, health smart services, as well as common services (e.g., telephonic services, e-mail services), have a great economic impact for organisations and represent an important mean to deliver value to their consumers. The malfunctions of both the services themselves as well as the entities responsible for their execution and consumption might cause economic losses, consumers' dissatisfaction and even shorten the service life cycle, among other risks. To avoid malfunctions beyond maintaining quality levels desired, it is important to take into account the widest possible context information that cause either positive or negative effects around services and entities involved in their provisioning and consumption. In this paper, we propose an upper ontology for service provisioning and consumption from a service-centric perspective. Specifically, we focus on software services, although we could argue for more generic applications. The contribution is the analysis, evaluation and reuse of existing proposals on context models to identify the strengths and weaknesses of its current status as well as to identify contexts not yet considered, and consolidate an integrated view of these proposals. The ultimate intention is to provide a well-defined and consolidated infrastructure of context information as a common body of knowledge, that could be instantiated on variety of use cases, for example, to be instantiated by monitors as context information useful to be monitored, or to be used as context information that allows knowing which contexts affect a service when a user consumes it, among others.

The quality of Open Source Software products is directly related to its community's health. To date, health analysis is made accessing available data repositories or using software management tools that are often too static or ad hoc. To address this issue, we propose to adopt principles and methods from the Service Oriented Computing field. Particularly, we propose to adapt the concepts of quality service and service level agreement, and propose to reuse the existing body of knowledge and techniques from SOC monitoring. To demonstrate the feasibility of the approach, we use a service monitoring framework called SALMonOSS as a proof of concept to realize the implementation of the proposal.

Service Level Agreements (SLAs) establish the Quality of Service (QoS) agreed between service-based systems consumers and providers. Since the violation of such SLAs may involve penalties, quality assurance techniques have been developed to supervise the SLAs fulfillment at runtime. However, existing proposals present some drawbacks: 1) the SLAs they support are not expressive enough to model real-world scenarios, 2) they couple the monitoring configuration to a given SLA specification, 3) the explanations of the violations are difficult to understand and even potentially inaccurate, 4) some proposals either do not provide an architecture, or present low cohesion within their elements. In this paper, we propose a comprehensive solution, from a conceptual reference model to its design and implementation, that overcomes these drawbacks. The resulting platform, SALMonADA, receives the SLA agreed between the parties as input and reports timely and comprehensive explanations of SLA violations. SALMonADA performs an automated monitoring configuration and it analyses highly expressive SLAs by means of a constraint satisfaction problems based technique. We have evaluated the impact of SALMonADA over the resulting service consumption time performance. The results are satisfactory enough to consider SALMonADA for SLA supervision because of its low intrusiveness.

The disciplines of requirements engineering (RE) and software architecture (SA) are fundamental to the success of software projects. Even though RE and SA are often considered separately, it has been argued that drawing a line between RE and SA is neither feasible nor reasonable as requirements and architectural design processes impact each other. Requirements are constrained by what is feasible technically and also by time and budget restrictions. On the other hand, feedback from the architecture leads to renegotiating architecturally significant requirements with stakeholders. The topic of bridging RE and SA has been discussed in both the RE and SA communities, but mostly independently. Therefore, the motivation for this ICSE workshop is to bring both communities together in order to identify key issues, explore the state of the art in research and practice, identify emerging trends, and define challenges related to the transition and the relationship between RE and SA.

Software requirements reuse becomes a fundamental activity for those IT organizations that conduct requirements engineering processes in similar settings. One strategy to implement this reuse is by exploiting a catalogue of software requirement patterns (SRPs). In this tutorial, we provide an introduction to the concept of SRP, summarise several existing approaches, and reflect on the consequences on several requirements engineering processes and activities. We take one of these approaches, the PABRE framework, as exemplar for the tutorial and analyse in more depth the catalogue of SRP that is proposed. We apply the concepts given on a practical exercise.

The quality of empirical studies is critical for the success of the Software Engineering (SE) discipline. More and more SE researchers are conducting empirical studies involving the software industry. While there are established empirical procedures, relatively little is known about the dynamics of conducting empirical studies in the complex industrial environments. What are the impediments and how to best handle them? This was the primary driver for organising CESI 2013. The goals of this workshop include having a dialogue amongst the participating practitioners and academics on the theme of this workshop with the aim to produce tangible output that will be summarised in a post-workshop report.

The significant wide impact of mobile technologies (e.g., smartphones, tablets) and the difficulty of mastering their complexity (due to factors like constant emergence and evolution) pose new challenges to many (if not all) software engineering disciplines. We particularly see these challenges when thinking of average citizens that carry out their daily activities in smart environments where mobile technologies and sensors installed provide many potential advantages to support them. Applications that could enable informed decision-making are currently beyond what software developers can provide. This position paper discusses challenges, and highlights current approaches available in order to support decision-making for thoughtful living. We present an initial version of a comprehensive framework to overcome the challenges identified and analyse which software engineering research lines may help to implement it. A motivating scenario is used to conduct the discussion.

By 2016 an estimated 95% of all commercial software packages will include Open Source Software (OSS). This extended adoption is yet not avoiding failure rates in OSS projects to be as high as 50%. Inadequate risk management has been identified among the top mistakes to avoid when implementing OSS-based solutions. Understanding, managing and mitigating OSS adoption risks is therefore crucial to avoid potentially significant adverse impact on the business. In this position paper we portray a short report of work in progress on risk management in OSS adoption processes. We present a risk-aware technical decision-making management platform integrated in a business-oriented decision-making framework, which together support placing technical OSS adoption decisions into organizational, business strategy as well as the broader OSS community context. The platform will be validated against a collection of use cases coming from different types of organizations: big companies, SMEs, public administration, consolidated OSS communities and emergent small OSS products.

Software requirement patterns have been proposed as a type of artifact for fostering requirements reuse. In this paper, we present PABRE-Proj a tool aimed at supporting requirements elicitation and specification.

i* is a goal-oriented and agent-oriented modeling framework that focuses on the analysis of intentional and strategic relationships among actors. In this mini-tutorial, we highlight a number of recent applications in practical industrial and business settings.

SUMMARY The process of designing a software architecture using different kinds of components is often challenging. Different designs support some quality attributes while damaging others; therefore, trade‐off analysis is needed to make informed decisions. Moreover, analysis made in theory needs to be complemented with observations in practice, especially when using a particular set of technologies to implement the system. In this paper, we present a particular instance of this problem. We study how the Three‐Layer architectural pattern may be developed using plug‐ins. We compare two extreme alternatives according to several representative scenarios and their impact in some quality attributes. Then, we apply this theoretical knowledge to a case study, the implementation of a plug‐in‐based tool for managing architectural knowledge using Eclipse. We report some unexpected difficulties found that forced us to adapt the theoretical solution into an operative architecture. Copyright © 2012 John Wiley & Sons, Ltd.

Background: Supporting sustainability in software engineering is becoming an active area of research. We want to contribute the first Systematic Literature Review(SLR) in this field to aid researchers who are motivated to contribute to that topic by providing a body of knowledge as starting point, because we know from own experience, this search can be tedious and time consuming. Aim: We aim to provide an overview of different aspects of sustainability in software engineering research with regard to research activity, investigated topics, identified limitations, proposed approaches, used methods, available studies, and considered domains. Method: The applied method is a SLR in five reliable and commonly-used databases according to the (quasi-standard) protocol by Kitchenham et al. [1]. We assessed the 100 first results of each database ordered by relevance with respect to the search query. Results: Of 500 classified publications, we regard 96 as relevant for our research questions. We sketch a taxonomy of their topics and domains, and provide lists of used methods and proposed approaches. Most of the excluded publications were ruled out because of an unfitting usage of terms within the search query. Conclusions: Currently, there is little research coverage on the different aspects of sustainability in software engineering while other disciplines are already more active. Future work includes extending the study by reviewing a higher number of publications, including dedicated journal and workshop searches, and snowballing.

Cloud Computing offers simple and cost effective outsourcing in dynamic service environments, and allows the construction of service-based applications using virtualization. By aggregating the capabilities of various IaaS cloud providers, federated clouds can be built. Managing such a distributed, heterogeneous environment requires sophisticated interoperation of adaptive coordinating components. In this paper we introduce an integrated federated management and monitoring approach that enables autonomous service provisioning in federated clouds. In this architecture, cloud brokers manage the number and the location of the utilized virtual machines for the received service requests. In order to provide seamless service executions, a state of the art monitoring solution is proposed that supports cloud selection performed by the management layer of the architecture. Our solution is able to cope with highly dynamic service executions by federating heterogeneous cloud infrastructures in a transparent and autonomous manner.

Web Services are becoming the cornerstone of modern service-based software applications. Their adequacy to a particular context of use is expressed by means of a service level agreement, written in terms of quality of service's attributes (like response time, availability, etc.). To perform a continuous and accurate assessment of the real quality of service and thus the fulfillment of the service level agreement, monitoring tools for web services have come into play. Each of these tools exhibits its own characteristics that can make it more adequate than others in a concrete situation. The purpose of this paper is to describe these characteristics using the concept of quality model. To do so, we first consider web service monitoring tools in context, by identifying other types of tool they interact with; we then systematically build the quality model by applying a method that has this specific purpose; and last we briefly apply the model to evaluate some exemplar monitoring tools. The paper focuses on commercial web service monitoring tools.

There are many approaches that propose the use of measures for assessing the quality of conceptual schemas. Many of these measures focus purely on the syntactic aspects of the conceptual schema diagrams, e.g. their size, their shape, etc. Similarities among different measures may be found both at the intra-model level (i.e., several measures over the same type of diagram are defined following the same layout) and at the intermodel level (i.e., measures over different types of diagrams are similar considering an appropriate metaschema correspondence). In this paper we analyse these similarities for a particular family of diagrams used in conceptual modelling, those that can be ultimately seen as a combination of nodes and edges of different types. We propose a unifying measuring framework for this family to facilitate the measure definition process and illustrate its application on a particular type, namely business process diagrams.

Actors, dependencies, goal satisfaction, ..., in a word, strategic modeling and reasoning, are recurrent matters in many software engineering and information system activities and disciplines. Standing out among other initiatives, i* soon became the preferred strategic analysis framework to many research groups that have adopted it and shaped it to their particular interests. A quick look to the state of the art on information science uncovers a significant number of approaches that use i* one way or another. It is then the perfect time to reflect about the current state of the framework and to issue new opportunities that may pose renovated objectives for researchers and furthermore foster the adoption of i* beyond the scientific community. This keynote identifies some challenges to overcome in this process, with special focus on syntactical and ontological aspects of the framework, and proposes a research agenda related to the most significant of these challenges.

The i* community has raised several main dialects and dozens of variations in the definition of the i* language. Differences may be found related not just to the representation of new concepts but to the very core of the i* language. If on the one hand this is caused by large adoption of the framework in the academic setting, on the other hand, it also poses some threats. For example, novices have trouble learning how to use the language, and besides these inconsistencies prevent i* from being largely adopted in business settings. Based on positive results from previous work related to conceptual modeling languages, we believe that foundational ontologies may present a promising solution for this problem. Foundational ontologies may help clarifying the semantics of core i* constructs and provide practical guidelines for their use. Last, they may serve as the basis to propose a normative definition of the framework. In this paper, we develop this idea, first by justifying the use of foundational ontologies and, in particular, the UFO ontology. Then, we raise some problems found in the i* literature. And then, we show the outcomes of adopting an ontological approach for the specific case of the i* framework. We focus here on one of the most characteristic i* construct, namely the means-end link.

Dealing with non-functional requirements (NFRs) has posed a challenge onto software engineers for many years. Over the years, many methods and techniques have been proposed to improve their elicitation, documentation, and validation. Knowing more about the state of the practice on these topics may benefit both practitioners' and researchers' daily work. A few empirical studies have been conducted in the past, but none under the perspective of software architects, in spite of the great influence that NFRs have on daily architects' practices. This paper presents some of the findings of an empirical study based on 13 interviews with software architects. It addresses questions such as: who decides the NFRs, what types of NFRs matter to architects, how are NFRs documented, and how are NFRs validated. The results are contextualized with existing previous work.

Researchers from requirements engineering and software architecture had emphasized the importance of NonFunctional Requirements and their influence in the architectural design process. To improve this process we have designed a tool, ArchiTech, which aims to support architects during the design process by suggesting alternative architectural decisions that can improve some types of non-functional requirements in a particular project, and facilitate the reuse of architectural knowledge shared between projects of the same architectural domain (e.g., web-based applications).

Increasingly, service-based applications (SBAs) are composed of third-party services available over the Internet. Even if third-party services have shown to work during design-time, they might fail during the operation of the SBA due to changes in their implementation, provisioning, or the communication infrastructure. As a consequence, SBAs need to dynamically adapt to such failures during run-time to ensure that they maintain their expected functionality and quality. Ideally the need for an adaptation is proactively identified, i.e., failures are predicted before they can lead to consequences such as costly compensation and roll-back activities. Currently, approaches to predict failures are based on monitoring. Due to its passive nature, however, monitoring might not cover all relevant service executions, which can diminish the ability to correctly predict failures. In this paper we demonstrate how online testing, as an active approach, can improve failure prediction by considering a broader range of service executions. Specifically, we introduce a framework and prototypical implementation that exploits synergies between monitoring, online testing and quality prediction. For online test selection and assessment we adapt usage-based testing strategies. We experimentally evaluate the strengths of our approach in predicting the need for an adaptation of an SBA.

Whilst it is considered a good practice to focus Requirements Engineering on current stakeholder needs, the high costs implied by requirements changes and the emergence of the Autonomic Computing paradigm raised the need for dealing with issues that are not currently requirements but that may come to be in the future. This work shows how foresight techniques can be used for requirements elicitation, and discusses the impacts of studying the future to that requirements engineering activity. In particular, it addresses the use of the Futures Wheel method to enrich goal models.

Current software systems are hybrid in nature. They are built by integrating third party Off-The-Shelf (OTS) components with preexisting legacy and bespoke custom-made software. In such systems, components are usually arranged into layers (e.g., hardware platform, operating systems and database layers, among others) to allow for their operation and interaction. Although several methods have been proposed to support OTS component selection, the truth is that in many cases the process is driven by political and other non-technical aspects, considering components as independent and isolated. Because of this, relevant stakeholder's requirements and concerns, as well as the implications that the selection of a particular component may bring to the system architecture, are simply ignored. In the worst case this may lead to the selection of unsuited or inappropriate components and eventually to miscarried projects, but more often to situations in which projects froze due to lack of stakeholders' agreement in relation to the newly created architectural scenario and some of its emerging requirements. In this paper we address these issues and present a proposal that uses software quality models as framework to support the negotiation of both initial and emerging requirements and the reconciliation of stakeholders' concerns. The approach considers components at different layers of system's architecture. The advantages of the proposal are illustrated with a real case conducted in a banking organization.

The use of the i*(iStar) framework by the requirements engineering community is many-fold. Among the several possible engineering cases, we are particularly interested here in the joint use of i*with other modelling frameworks to obtain what we call i*-based frameworks. In this context, several challenges need to be overcome: theoretical, technical, methodological and community-related. In this paper, we review current i*-based frameworks under several possible scenarios and observe that not all of these challenges are always addressed, and even more, there is lack of guidelines or well-accepted methodological design steps on how to overcome these issues. Then, we detail the several engineering artifacts and techniques whose consideration in i*-based frameworks may help to overcome them. To illustrate the vision, we present the case of combining i*with data warehouse models, from the initial definition of the ontology to the final implementation using profiles. Finally, we provide a research agenda to apply the proposed vision including a final reflection on defining a maturity model as a convenient way to support forthcoming research in the topic.

Software requirement patterns have been proposed as an artifact for fostering requirements reuse. In this paper we present PABRE-Man, a software subsystem aimed at managing a catalogue of patterns ready to be applied in requirements engineering projects.

The impact of non-functional requirements (NFRs) over software systems has been widely documented. Consequently, cost-effective software production method shall provide means to integrate this type of requirements into the development process. In this vision paper we analyze this assumption over a particular type of software production paradigm: model-driven development (MDD). We report first the current state of MDD approaches with respect to NFRs and remark that, in general, NFRs are not addressed in MDD methods and processes, and we discuss the effects of this situation. Next, we outline a general framework that integrates NFRs into the core of the MDD process and provide a detailed comparison among all the MDD approaches considered. Last, we identify some research issues related to this framework.

The mobile office devices market is currently growing, mainly due to the descending cost of wireless technology as well as the high diversity of functions and features covered. Diversity and proliferation become a hard problem when a person or organization aims at selecting the appropriate device for their particular needs. We propose here a framework for producing device recommendations based on personal or business needs. The framework is articulated through an architecture that includes subsystems for data extraction, recommendation and personalization. All these subsystems operate upon a goal-oriented knowledge base whose presentation is the subject of this paper. Our approach is built upon three independent models: a market model, which contains descriptions of the current devices offered in the marketplace; a domain model, which states the needs of the person or organization; a mediator model, which describes the types of devices available. We show the actors and processes around these models. Last, we present a prototype that acts as proof-of-concept of the recommender system.

This paper presents our PABRE method for facilitating requirements elicitation on the basis of requirement patterns with the goal of saving time and reducing errors during this activity. The process presented applies for elicitation in off-the-shelf selection projects driven by call for tenders processes and uses a requirement patterns catalogue. The process selects patterns from the catalogue that apply to the particular selection project, and convert them into the real requirements that finally configure the project requirements book. We show some benefits of the pattern approach for requirements engineers and IT consultants, as well as for customers. Finally we discuss the strengths and weaknesses of the proposal and identify some future work.

Because of their complexity, software systems are currently built by integrating components of different nature into hybrid architectures, usually including some third party Off-The-Shelf (OTS) components. Several methods have been proposed to support OTS component selection. Most of them work by comparing requirements, incrementally gained though several iterations, and component descriptions. However, the iterative nature of these methods makes them practically unsuitable in some cases, particularly when conducting call-for-tender-based processes for selecting coarse-grained OTS components. In these cases, company goals need to be thoughtfully and systematically engineered to obtain a well structured set of requirements, which is to remain static during the entire procurement process and cannot be validated through hands-on experimentation. In this paper we report a case study in the selection of an ERP system for a telecommunications company using a call-for-tender process. We present the activities that were undertaken to elicit, analyze and structure the requirements to be included in call-for-tender documents, and the evaluation of components based on the identification of mismatches between requirements and component capabilities.

Goal- and agent-oriented models have become a consolidated type of artifact in various software and knowledge engineering activities. Several languages exist for representing such type of models but there is a lack of associated methodologies for guiding their construction up to the necessary level of detail. In this paper we present RiSD, a method for building Strategic Dependency (SD) models in the i* notation. RiSD is defined in a prescriptive way to reduce uncertainness when constructing the model. RiSD tackles three fundamental issues: (1) it tends to reduce the average size of the resulting models; (2) it defines some traceability relationships among model elements; (3) it provides some lexical and syntactical conventions. As a result, we may say that RiSD supports the construction process of goal- and agent-oriented models whilst increasing their understanding.

The selection of COTS components is made not only by an analysis of their technical quality but also (and sometimes mostly) by considering how they fulfill those non-technical requirements considered relevant, which refer to licensing, reputation, and similar issues. In this paper we present an approach for managing nontechnical requirements during COTS selection. The proposal is based on extending the ISO/IEC 9126-1 catalogue of quality factors by adding factors related to non-technical issues, obtaining a cohesive and comprehensive framework for managing requirements during selection.

The i* approach is a consolidated modelling technique that has proven to be useful in the requirements engineering phases of software development. Using i* requires the adoption of a methodology for defining the models and tool support for manipulating them. For addressing those aspects, we propose J-PRiM, a tool that allows to define i* models by applying PRiM, our process reengineering i* methodology

DesCOTS is a system that has an aim to help clients in the selection of COTS components. This system is based in the use of quality models associated to a software domain for evaluating the products in that domain, and for defining in a formal way the requirements of the clients for finding a suitable product in that domain. The evaluation and the formal definition of requirements are facilitated by metrics of each quality entity in the quality models. Our ISO/IEC 9126-1 based quality models are a set of quality entities structured in hierarchies of characteristics, subcharacteristics and attributes; with possible intermediate hierarchies of sub-characteristics and attributes

The need to rapidly develop high quality, complex software has driven research in separate communities, including product line and agile methods. This workshop brings together participants from both communities to investigate what the approaches have in common, how they differ, and the potential costs and benefits in developing an agile product line engineering approach.

This workshop summary presents an overview of the one-day International Workshop on Models and Processes for the Evaluation of off-the-shelf Components (MPEC'05), held in conjunction with the 27th International Conference on Software Engineering (ICSE'05). Details about MPEC'05 may be found at http://www.lsi.upc.edu/events/mpec/.

The i* approach includes the construction of strategic dependency models (SD) developed to model information systems that can be applied in a wide variety of contexts. We are interested in the formulation of metrics over i* models to be used in components system architectures analysis. These metrics are defined to analyse the models with respect to some properties that are interesting for the system being modelled, such as security, efficiency or accuracy. Metrics are defined in terms of the actors and dependencies of the models and the results of their evaluation are used to inform multiple component selection. As the definition and evaluation of the property metrics are cumbersome activities, the need of tool support arises. Thus, we have enlarged REDEPEND, a Microsoft Visio plug-in that graphically models system goals using the i* formalism, to support the definition and evaluation of architectural properties obtaining the REDEPEND-REACT prototype.

In the last years, some methods have been proposed for dealing with COTS component selection. In all of them, a key point is the comparison of the user requirements, which drive the selection process, with the capabilities of the evaluated COTS. Quality models are a means to obtain structured descriptions of COTS domains. Once built, COTS in a domain may be evaluated with respect to the quality entities included therein, quality requirements may be stated with respect to the quality model, and the classical factor-requirement negotiation process may be used for the selection of the most appropriate COTS. Our goal is to have completely implemented in a near future a new version of the system DesCOTS (Grau et al., 2004) that supports all the above processes. This system is constituted by 4 subsystems: QM, that helps in the construction and management of quality models (Carvallo et al., 2004); EV, that helps in the evaluation of COTS components and that is the one presented in this paper; SL, that helps in the definition of requirements in a project and in the selection of COTS components that hold these requirements; and AD, that allows the administration of the whole system.

Selection of software packages from user requirements is a central task in software engineering. Selection of inappropriate packages may compromise business processes and may interfere negatively in the functioning of the involved organization. Success of package selection is endangered because of many factors, one of the most important being the absence of structured descriptions of both package features and user quality requirements. In this paper, we propose a methodology for describing quality factors of software packages using the ISO/IEC quality standard as a framework. Following this standard, relevant attributes for a specific software domain are identified and structured as a hierarchy, and metrics for them are chosen. Software packages in this domain can then be described in a uniform and comprehensive way. Therefore, selection of packages can be ameliorated by transforming user quality requirements into requirements expressed in terms of quality model attributes. We illustrate the approach by presenting, in some depth, a quality model for the mail server domain.

We present the proactive behavioural features of PROMENADE, a process modelling language for formalizing the construction of software process models. PROMENADE aims at improving expressiveness, standardization, flexibility and reuse in software process modelling. In this article we focus on expressiveness, which is achieved by means of a declarative (instead of imperative) proactive control-flow based on precedence relationships. Different families of such precedences have been defined within the language (namely, basic, derived and dynamic). Also, PROMENADE provides (1) a comprehensive parameter task-binding mechanism to keep track of document-flow between tasks; (2) a high level notation to define new precedence relationships, which may be used to tailor the language to the specific needs of each user and process; (3) the definition of flexible models by leaving some parts undefined until enactment time. The specific concepts necessary to model a software process are defined and integrated into the UML metamodel. The extended metamodel is converted into a UML profile using stereotypes, constraints and tag definitions.