ABSTRACT The COVID-19 pandemic has permanently altered workplace structures, making remote work a widespread practice. While many employees advocate for flexibility, many employers reconsider their attitude toward remote work and opt for structured return-to-office mandates. Media headlines repeatedly emphasize that the corporate world returns to full-time office work. This study examines how companies in software-intensive industry regulate work location, whether corporate policies have evolved in the last five years, and, if so, how, and why. We collected data on remote work regulation from corporate HR and management representatives from 68 companies that vary in size, location, and preferred work modality. Our findings reveal that although many companies prioritize office-oriented work (50%), most companies in our sample permit hybrid work (84%) and only four companies are returning to full-time office work. Remote work regulation does not reveal any particular new “best practice” as policies differ greatly; however, the single most popular arrangement was the three in-office days per week. More than half of the companies (53%) encourage or mandate office attendance centrally, with additional 18% having decentralized mandates. Over a quarter (28%) have changed regulations gradually increasing the mandatory office presence or implementing differentiated conditions. Our key recommendation for office-oriented companies is to consider trust-based recommendations as an alternative to centralized office presence mandates, while for companies oriented toward remote working, we warn about the points of no (or hard) return. Finally, the current state of policies is clearly not final, as companies continue to experiment and adjust their work regulation

This preliminary research explores the critical issue of preventing children's exposure to inappropriate advertisements within childtargeted content on streaming platforms.Inspired by real-world examples from Spotify and Disney+, where advertisements were misaligned with the content's target audience, this preliminary work proposes a framework (Junior Guardian) designed to safeguard children's experiences as streaming platform users.Specifically, this framework should incorporate a context-aware recommender system to automate and enhance the objectivity of advertisement suitability assessments.

In recent years, Software Engineering (SE) scholars and practitioners have emphasized the importance of integrating soft skills into SE education. However, teaching and learning soft skills are complex, as they cannot be acquired passively through raw knowledge acquisition. On the other hand, hackathons have attracted increasing attention due to their experiential, collaborative, and intensive nature, which certain tasks could be similar to real-world software development. This paper aims to discuss the idea of hackathons as an educational strategy for shaping SE students' soft skills in practice. Initially, we overview the existing literature on soft skills and hackathons in SE education. Then, we report preliminary empirical evidence from a seven-day hybrid hackathon involving 40 students. We assess how the hackathon experience promoted innovative and creative thinking, collaboration and teamwork, and knowledge application among participants through a structured questionnaire designed to evaluate students' self-awareness. Lastly, our findings and new directions are analyzed through the lens of SelfDetermination Theory (SDT), which offers a psychological lens to understand human behavior. This paper contributes to academia by advocating the potential of hackathons in SE education and proposing concrete plans for future research within SDT. For industry, our discussion has implications around developing soft skills in future SE professionals, thereby enhancing their employability and readiness in the software market.

[Context] The lack of practical relevance in many Software Engineering (SE) research contributions is often rooted in oversimplified views of industrial practice, weak industry connections, and poorly defined research problems. Clear criteria for evaluating SE research problems can help align their value, feasibility, and applicability with industrial needs. [Goal] In this paper, we introduce the Lean Research Inception (LRI) framework, designed to support the formulation and assessment of practically relevant research problems in SE. We describe its initial evaluation strategy conducted in a workshop with a network of SE researchers experienced in industry-academia collaboration and report the evaluation of its three assessment criteria (valuable, feasible, and applicable) regarding their importance and completeness in assessing practical relevance. [Method] We applied LRI retroactively to a published research paper, engaging workshop participants in discussing and assessing the research problem by applying the proposed criteria using a semantic differential scale. Participants provided feedback on the criteria’s importance and completeness, drawn from their own experiences in industry-academia collaboration. [Results] The findings reveal an overall agreement on the importance of the three criteria – valuable (83.3%), feasible (76.2%), and applicable (73.8%) – for aligning research problems with industrial needs. Qualitative feedback suggested adjustments in terminology with a clearer distinction between feasible and applicable, and refinements for valuable by more clearly considering business value, ROI, and originality. [Conclusion] While LRI still constitutes ongoing research and requires further evaluation, our emerging results strengthen our confidence that the three criteria applied using the semantic differential scale can already help the community assess the practical relevance of SE research problems.

Abstract Mixed methods research is often used in software engineering, but researchers outside of the social or human sciences often lack experience when using these designs. This paper provides guiding principles and advice on how to design mixed method research, and to encourage the intentional, rigorous, and innovative application of mixed methods in software engineering. It also presents key properties of core mixed method research designs. Through a number of fictitious but recognizable software engineering research scenarios, we showcase how to choose suitable mixed method designs and consider the inevitable trade-offs any design choice leads to. We describe several antipatterns that illustrate what to avoid in mixed method research, and when mixed method research should be considered over other approaches.

Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. This paper aims to deliver a comprehensive overview of the current status quo of engineering ML-enabled systems and lay the foundation to steer practically relevant and problem-driven academic research. We conducted an international survey to collect insights from practitioners on the current practices and problems in engineering ML-enabled systems. We received 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems using open and axial coding procedures. Our survey results reinforce and extend existing empirical evidence on engineering ML-enabled systems, providing additional insights into typical ML-enabled systems project contexts, the perceived relevance and complexity of ML life cycle phases, and current practices related to problem understanding, model deployment, and model monitoring. Furthermore, the qualitative analysis provides a detailed map of the problems practitioners face within each ML life cycle phase and the problems causing overall project failure. The results contribute to a better understanding of the status quo and problems in practical environments. We advocate for the further adaptation and dissemination of software engineering practices to enhance the engineering of ML-enabled systems. • International survey gathering insights from 188 practitioners across 25 countries. • Overview of current practices and challenges in engineering ML-enabled systems. • Inferential quantitative analysis reporting the status quo with confidence intervals. • Qualitative analysis mapping ML life cycle challenges and causes of project failure.

In the ever-expanding landscape of Artificial Intelligence (AI), where innovation thrives and new products and services are continuously being delivered, ensuring that AI systems are designed and developed responsibly throughout their entire lifecycle is crucial. To this end, several AI ethics principles and guidelines have been issued to which AI systems should conform. Nevertheless, relying solely on high-level AI ethics principles is far from sufficient to ensure the responsible engineering of AI systems. In this field, AI professionals often navigate by sight. Indeed, while recommendations promoting Trustworthy AI (TAI) exist, they are often high-level statements difficult to translate into concrete implementation strategies. Currently, there is a significant gap between high-level AI ethics principles and low-level concrete practices for AI professionals. To address this challenge, our work presents an experience report where we develop a novel holistic framework for Trustworthy AI --- designed to bridge the gap between theory and practice --- and report insights from its application in an industrial case study. The framework builds up from the results of a systematic review of the state of the practice as well as a survey and think-aloud interviews with 34 AI practitioners. The framework, unlike most of the ones in literature, is designed to provide actionable guidelines and tools to support different types of stakeholders throughout the entire Software Development Life Cycle (SDLC). Our goal is to empower AI professionals to confidently navigate the ethical dimensions of TAI through practical insights, ensuring that the vast potential of AI is exploited responsibly for the benefit of society as a whole.

[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve iterative experimentation with data, models, and algorithms. However, ML components are often developed by data scientists with diverse educational backgrounds, potentially resulting in code that doesn't adhere to software design best practices. [Goal] In order to better understand this phenomenon, we investigated the impact of the SOLID design principles on ML code understanding. [Method] We conducted a controlled experiment with three independent trials involving 100 data scientists. We restructured real industrial ML code that did not use SOLID principles. Within each trial, one group was presented with the original ML code, while the other was presented with ML code incorporating SOLID principles. Participants of both groups were asked to analyze the code and fill out a questionnaire that included both open-ended and closed-ended questions on their understanding. [Results] The study results provide statistically significant evidence that the adoption of the SOLID design principles can improve code understanding within the realm of ML projects. [Conclusion] We put forward that software engineering design principles should be spread within the data science community and considered for enhancing the maintainability of ML code.

Recently, there has been growing attention on behalf of both academic and practice communities towards the ability of Artificial Intelligence (AI) systems to operate responsibly and ethically. As a result, a plethora of frameworks and guidelines have appeared to support practitioners in implementing Trustworthy AI applications (TAI). However, little research has been done to investigate whether such frameworks are being used and how. In this work, we study the vision AI practitioners have on TAI principles, how they address them, and what they would like to have – in terms of tools, knowledge, or guidelines – when they attempt to incorporate such principles into the systems they develop. Through a survey and semi-structured interviews, we systematically investigated practitioners’ challenges and needs in developing TAI systems. Based on these practical findings, we highlight recommendations to help AI practitioners develop Trustworthy AI applications.

Context: The increasing integration of artificial intelligence and machine learning into software systems has highlighted the critical importance of ensuring fairness in these technologies. Bias in software can lead to inequitable outcomes, making fairness testing essential. However, the current landscape of fairness testing tools remains underexplored, particularly regarding their practical applicability and usability for software development practitioners. Goal: This study aimed to evaluate the practical applicability of existing fairness testing tools for software development practitioners, assessing their usability, documentation, and overall effectiveness in real-world industry settings. Method: We identified 41 fairness testing tools from the literature and conducted a heuristic evaluation and documentary analysis of their installation processes, user interfaces, supporting documentation, and update frequencies. Technical analysis included assessing configurability for diverse datasets. The analysis focused on identifying strengths and deficiencies to determine their suitability for industry use. Findings: Our findings revealed that most fairness testing tools show significant deficiencies, particularly in user-friendliness, detailed documentation, and configurability. These limitations restrict their practical use in industry settings. The tools also lack regular updates and possess a narrow focus on specific datasets, which constrains their versatility and scalability. Despite some strengths, such as cost-effectiveness and compatibility with several environments, the overall landscape of fairness testing tools requires substantial improvements to meet industry needs. Conclusion: There is a pressing need to develop fairness testing tools that align more closely with industry requirements, offering enhanced usability, comprehensive documentation, and greater configurability to effectively support software development practitioners.

Context: This study explores how software professionals identify and address biases in AI systems within the software industry, focusing on practical knowledge and real-world applications. Goal: We focused on understanding the strategies employed by practitioners to manage bias and their implications for fairness debt. Method: We employed a qualitative research method, gathering insights from industry professionals through interviews and using thematic analysis to explore the collected data. Findings: Professionals identify biases through discrepancies in model outputs, demographic inconsistencies, and training data issues. They address these biases using strategies such as enhanced data management, model adjustments, crisis management, improving team diversity, and ethical analysis. Conclusion: Our paper presents initial evidence on addressing fairness debt and lays the groundwork for developing structured guidelines to manage fairness-related issues in AI systems.

Software quality is crucial in software development: if not addressed in early phases of the software development life cycle, it may even lead to technical bankruptcy, i.e., a situation in which modifications cost more than redeveloping the application from scratch. In addition, code security must also be addressed to reduce software vulnerabilities and to comply with legal requirements. In this work, we aim to investigate the relationship between refactoring code quality and software security, with the purpose of understanding whether and to what extent improving software quality could have a positive impact on software security as well. Specifically, we investigate to what extent rule violations of a software quality tool such as SonarQube overlap with rule violations of a software vulnerability tool like Fortify Static Code Analyzer. We first compared the rules encoded in the quality models of both tools, to discover possible overlapping cases. Later, we compared the issues raised by both tools on a set of open source Java projects; we also investigated the cases in which a quality refactoring process impacts over software security (thus removing one or more vulnerabilities). We furthermore validated our results statistically. Our results show that resolving software quality issues might also resolve security issues but only in part: many security issues still persist in the source code; also, some quality aspects are more likely to be improved in respect to others. In addition, this empirical study uncovers rule co-occurrences between the two tools. This study confirms the need for using a security-oriented static analysis tool to enforce software security instead of relying only on a quality-oriented one. Results have highlighted important insights for practitioners.

Technical debt management is a critical activity that is gaining the attention of both practitioners and researchers. Several tools providing automatic support for technical debt management have been introduced over the last years. SonarQube is one of the most widely applied tools to automatically measure technical debt in software systems. SonarQube has been adopted to quantify the diffuseness of technical debt in projects of the Apache Software Foundation ecosystem. Lenarduzzi et al. [1] found that the vast majority of technical debt issues in the code are code smells and that, surprisingly, developers tend to take more time to remove severe issues than the less-severe ones. While this study provides very interesting insights both for researchers and practitioners interested in technical debt management, we identified some major limitations that could have led to results that do not perfectly reflect reality. This study aims to address such limitations by presenting a differentiated replication study. Our findings have pointed out significant differences with the reference work. The results show that technical debt issues appear much more rarely than what the reference work reported.In this study, we implemented a new methodology to calculate the diffuseness of SonarQube issues at project and commit level, based on the reconstruction of the SonarQube quality profile in order to understand how the quality profile has evolved and to compare the number of active rules per category and severity level with the respective number of issues found. The results show that over 50% of rules active in the quality profile, are Code Smell rules and that over 90% of the issues belong to Code Smell category. Furthermore, analyzing the life span of the issues, we found that developers take into account the level of severity of the issues only for the Bug category, thus fixing the issues starting from the most severe, which is not the case for the other categories.

Registered reports are scientific publications which begin the publication process by first having the detailed research protocol, including key research questions, reviewed and approved by peers. Subsequent analysis and results are published with minimal additional review, even if there was no clear support for the underlying hypothesis, as long as the approved protocol is followed. Registered reports can prevent several questionable research practices and give early feedback on research designs. In software engineering research, registered reports were first introduced in the International Conference on Mining Software Repositories (MSR) in 2020. They are now established in three conferences and two pre-eminent journals, including this one (EMSE). We explain the motivation for registered reports, outline the way they have been implemented in software engineering, and outline some ongoing challenges for addressing high quality software engineering research.

Privacy by Design and Security by Design are two fundamental aspects in the current technological and regulatory context. Therefore, software development must integrate these aspects and consider software security on one hand, and user-centricity from the design phase on the other. It is necessary to support the team in all stages of the software lifecycle in integrating privacy and security requirements. Taking these aspects into account, the paper presents VIS-PRISE prototype, a visual tool for supporting the design team in the secure agile development.

Background. Test-Driven Development (TDD) is an agile software development practice, which encourages developers to write “quick-and-dirty” production code to make tests pass, and then apply refactoring to “clean” written code. However, previous studies have found that refactoring is not applied as often as the TDD process requires, potentially affecting software quality.

Regression testing wants to prevent that errors, which have already been corrected once, creep back into a system that has been updated. A naïve approach consists of re-running the entire test suite (TS) against the changed version of the software under test (SUT). However, this might result in a time-and resource-consuming process; e.g., when dealing with large and/or complex SUTs and TSs. To avoid this problem, Test Case Selection (TCS) approaches can be used. This kind of approaches build a temporary TS comprising only those test cases (TCs) that are relevant to the changes made to the SUT, so avoiding executing unnecessary TCs. In this paper, we introduce CATTO (Commit Adaptive Tool for Test suite Optimization), a tool implementing a TCS strategy for SUTs written in Java as well as a wrapper to allow developers to use CATTO within IntelliJ IDEA and to execute CATTO just-in-time before committing changes to the repository. We conducted a preliminary evaluation of CATTO on seven open-source Java projects to evaluate the reduction of the test-suite size, the loss of fault-revealing TCs, and the loss of fault-detection capability. The results suggest that CATTO can be of help to developers when performing TCS. The video demo and the documentation of the tool is available at: https://catto-tool.github.io/

Sustainability is not a trend; more and more areas of knowledge defend it and apply it among their main characteristics. The area of Information Technology (IT) has not been left behind and, through the so-called Green IT, advocates for a more sustainable environment. Organizations around the world are realizing that Green IT is an important asset that produces great benefits at ecological, social, and economic level. For this reason, we have developed a revised version of the “Governance and Management Framework for Green IT”. With this new version we intend to offer a more complete and solid guide that helps organizations to gradually implement, assess, and improve those characteristics of governance and management that are the basis of the processes and practices of Green IT. This proposal has been empirically validated through a case study and the results obtained demonstrate the validity, usefulness, and applicability of the framework for organizations.

GitHub currently hosts more than 100 million public repositories. This has made it very popular to conduct Mining Software Repositories (MSR) studies. Researchers have been exploiting the information stored in GitHub (e.g., commits, pull requests, or issues) to investigate both developer- and project-related aspects. GitHub provides the REST API to make queries without cloning repositories. In this tool-demo paper, we highlight some issues we noticed when conducting an MSR study on GitHub by using the REST API and present G-Repo: a tool developed to support researchers when tackling these issues able to ease the creation of datasets for MSR studies. Also, we provide a manually-annotated dataset with information about the kind and the (spoken) languages of 1,500 repositories hosted on GitHub. A video showing the functioning of G-Repo is available at: https://youtu.be/mb9CIALBFZk.

Abstract In recent years, we have been witnessing a rapid increase of research on exergames—i.e., computer games that require users to move during gameplay as a form of physical activity and rehabilitation. Properly balancing the need to develop an effective exercise activity with the requirements for a smooth interaction with the software system and an engaging game experience is a challenge. Model-driven software engineering enables the fast prototyping of multiple system variants, which can be very useful for exergame development. In this paper, we propose a framework, PhyDSLK, which eases the development process of personalized and engaging Kinect-based exergames for rehabilitation purposes, providing high-level tools that abstract the technical details of using the Kinect sensor and allows developers to focus on the game design and user experience. The system relies on model-driven software engineering technologies and is made of two main components: (i) an authoring environment relying on a domain-specific language to define the exergame model encapsulating the gameplay that the exergame designer has envisioned and (ii) a code generator that transforms the exergame model into executable code. To validate our approach, we performed a preliminary empirical evaluation addressing development effort and usability of the PhyDSLK framework. The results are promising and provide evidence that people with no experience in game development are able to create exergames with different complexity levels in one hour, after a less-than-two-hour training on PhyDSLK. Also, they consider PhyDSLK usable regardless of the exergame complexity.

Nowadays, the dimension and complexity of software development projects increase the possibility of cyber-attacks, information exfiltration and data breaches. In this context, developers play a primary role in addressing privacy requirements and, consequently security, in software applications. Currently, only general guidelines exist that are difficult to put in operation due to the lack of the required security skills and knowledge, and to the use of legacy software development processes that do not deal with privacy and security aspects. This paper presents a knowledge base, the Privacy Knowledge Base (PKB), and the VIS-PRISE prototype (Visually Inspection to Support Privacy and Security) a visual tool that support developers' decisions to integrate privacy and security requirements in all software development phases. An initial experimental study with junior developers is also presented.

Researcher Bias (RB) occurs when researchers influence the results of an empirical study based on their expectations. RB might be due to the use of Questionable Research Practices (QRPs). In research fields like medicine, blinding techniques have been applied to counteract RB. We conducted an explorative qualitative survey to investigate RB in Software Engineering (SE) experiments, with respect to: (i) QRPs potentially leading to RB, (ii) causes behind RB, and (iii) possible actions to counteract RB including blinding techniques. Data collection was based on semi-structured interviews. We interviewed nine active experts in the empirical SE community. We then analyzed the transcripts of these interviews through thematic analysis. We found that some QRPs are acceptable in certain cases. Also, it appears that the presence of RB is perceived in SE and, to counteract RB, a number of solutions have been highlighted: some are intended for SE researchers and others for the boards of SE research outlets.

Noise, defined as an unwanted sound, is one of the most common factors people have to deal with when performing their daily working tasks. Researchers have marginally investigated the effect of noise on software engineers' performance. In this paper, we present the results of two replicated experiments whose main goal was to increase the body of knowledge, by confirming or not the results of the baseline experiments, on the effect of noise while comprehending functional requirements specifications and fixing faults in source code. The results of the replicated experiments suggest that: (i) noise does not significantly affect the comprehension of functional requirements specifications and (ii) noise significantly and negatively affects fixing faults if this task lasts 30 minutes, while it does not have a significant impact if the task lasts 60 minutes. The results of the replications confirm to a large extent those of the baseline experiments and allow us to postulate, as done for the baseline experiments, that fixing faults is more vulnerable to noise than comprehending the specifications of functional requirements.

Test-Driven Development (TDD) is an incremental approach to software development. Despite it is claimed to improve both quality of software and developers’ productivity, the research on the claimed effects of TDD has so far shown inconclusive results. Some researchers have ascribed these inconclusive results to the negative affective states that TDD would provoke. A previous (baseline) experiment has, therefore, studied the affective reactions of (novice) developers—i.e., 29 third-year undergraduates in Computer Science (CS)—when practicing TDD to implement software. To validate the results of the baseline experiment, we conducted a replicated experiment that studies the affective reactions of novice developers when applying TDD to develop software. Developers in the treatment group carried out a development task using TDD, while those in the control group used a non-TDD approach. To measure the affective reactions of developers, we used the Self-Assessment Manikin instrument complemented with a liking dimension. The most important differences between the baseline and replicated experiments are: (i) the kind of novice developers involved in the experiments—third-year vs. second-year undergraduates in CS from two different universities; and (ii) their number—29 vs. 59. The results of the replicated experiment do not show any difference in the affective reactions of novice developers. Instead, the results of the baseline experiment suggest that developers seem to like TDD less as compared to a non-TDD approach and that developers following TDD seem to like implementing code less than the other developers, while testing code seems to make them less happy.

Abstract Background Assessing and communicating software engineering research can be challenging. Design science is recognized as an appropriate research paradigm for applied research, but is rarely explicitly used as a way to present planned or achieved research contributions in software engineering. Applying the design science lens to software engineering research may improve the assessment and communication of research contributions. Aim The aim of this study is 1) to understand whether the design science lens helps summarize and assess software engineering research contributions, and 2) to characterize different types of design science contributions in the software engineering literature. Method In previous research, we developed a visual abstract template, summarizing the core constructs of the design science paradigm. In this study, we use this template in a review of a set of 38 award winning software engineering publications to extract, analyze and characterize their design science contributions. Results We identified five clusters of papers, classifying them according to their different types of design science contributions. Conclusions The design science lens helps emphasize the theoretical contribution of research output—in terms of technological rules—and reflect on the practical relevance, novelty and rigor of the rules proposed by the research.

Background: One of the most important current challenges of Software Engineering (SE) research is to provide relevant evidence to practice. In health related fields, Rapid Reviews (RRs) have shown to be an effective method to achieve that goal. However, little is known about how the SE research community perceives the potential applicability of RRs. Aims: The goal of this study is to understand the SE research community viewpoints towards the use of RRs as a means to provide evidence to practitioners. Method: To understand their viewpoints, we invited 37 researchers to analyze 50 opinion statements about RRs, and rate them according to what extent they agree with each statement. Q-Methodology was employed to identify the most salient viewpoints, represented by the so called factors. Results: Four factors were identified: Factor A groups undecided researchers that need more evidence before using RRs; Researchers grouped in Factor B are generally positive about RRs, but highlight the need to define minimum standards; Factor C researchers are more skeptical and reinforce the importance of high quality evidence; Researchers aligned to Factor D have a pragmatic point of view, considering RRs can be applied based on the context and constraints faced by practitioners. Conclusions: In conclusion, although there are opposing viewpoints, there are also some common grounds. For example, all viewpoints agree that both RRs and Systematic Reviews can be poorly or well conducted.

In this study, we collected evidence from empirical studies published over two decades-from 1995 to 2018-to answer the following general research question: "What is the current existing empirical evidence concerning the use of software modeling to support source code maintenance?" We carried out a systematic literature review on research published in relevant journals, as well as conference and workshop proceedings, to investigate various aspects of our general research question. Although there is a common belief that software models are beneficial for source code maintainability, this aspect has been scarcely investigated in the literature and only a few empirical studies have been conducted in an industrial context. Moreover, most of the used models are UML. As so, our study shows that there is a small body of research related to this field.

[Context] The popularity of tools for software quality analysis has increased over the years, with special attention to tools that calculate technical debt based on violations of a set of rules. SonarQube is one of the most used tools and provides an estimation of the time needed to remediate technical debt. However, practitioners are still skeptical about the accuracy of its remediation time estimation. [Objective] In this paper, we analyze the accuracy of SonarQube remediation time on a set of 15 open source Java projects. [Method] We designed and conducted a case study where we asked 65 novice developers to remove rule violations and reduce the technical debt of 15 projects. [Results] The results point out that SonarQube remediation time, compared to the actual time for reducing technical debt, is generally overestimated, and that the most accurate estimation relates to code smells, while the least accurate concerns bugs. [Conclusions] Practitioners and researchers could benefit from the results of this work to understand up to which extent technical debt is overestimated and have a more accurate estimation of the remediation time.?

Sustainability is a problem that is increasingly worrying organisations around the world, which has led them to adopt sustainable practices in their processes. From the point of view of one of the areas that have had the greatest impact in recent times in organisations, the area of information technology (IT), different sustainable practices have arisen in isolation, known as Green IT practices. So, a framework that organises and establishes these practices (until now isolated) is necessary in order to implement, assess, and improve the Green IT in organisations in an efficient, gradual, and integrated way. In this study, the authors propose a maturity model based on capability maturity model integration (CMMI), through which it is intended to help organisations gradually implement and improve through different levels of maturity, the governance and management of Green IT. The validations of this proposal carried out through experts and practical cases demonstrate the usefulness of this proposal when implementing, assessing, and improving the Green IT in organisations.

Background: Test-Driven Development (TDD) is an agile software development practice, which is claimed to boost both external quality of software products and developers' productivity.

Context: UML software development relies on different types of UML diagrams, which must be consistent with one another. UML Synthesis techniques suggest to generate diagram(s) from other diagram(s), thereby implicitly suggesting that input and output diagrams of the synthesis process be consistent with one another.

Context: Through the years researchers have proposed several approaches to foster the growth and quality of experiments in Software Engineering. Among these approaches, there are some initiatives that rely on tool support for specifying controlled experiments. Goal: This paper reports results from a study, which aims to organize, analyze and outline the specifications of each initiative through a comparative analysis. Method: Specifications of each initiative have been compared through a comparative analysis, carried out according to eight criteria: (i) standard empirical concepts, (ii) goals and targets, (iii) involved variables, (iv) subject description, (v) design of experiment, (vi) tasks and activities, (vii) instruments and measurements, and (viii) the threats to research validity. Results: The results show that, among the tools currently existing and used in literature, the eSEE (Experimental Software Engineering Environment) is a complete model. However, it is also the most complex. In the other hand, the most flexible one is Experiment DSL. Conclusion: Based on our results, the currently existing solutions have strengths and weaknesses that should address efforts to make improvements in this area. In principal, our general suggestion is to place emphasis on methodological quality âĂŞ more than on method quantity.

Context: Researchers perform experiments to check their proposals under controlled conditions. Thus, experiments are an important category of empirical studies and are the classical approach for identifying cause-effect relationships. Goal: Quantitatively characterize and analyze the controlled experiments in software engineering published in journal and conference proceedings in the decade from 2003 to 2013. Method: We performed a systematic mapping study that includes all full papers published at EASE, ESEM and ESEJ. A total of 731 were selected. Results: We obtained 110 papers that report controlled experiments. In these experiments we obtained quantitative data about authors and institutions, subjects, tasks, environment, replication and threats to validity. Conclusions: The main contribution of this work is the amount of experiments published in the three main venues of Empirical Software Engineering between the years 2003 to 2013. And also how these experiments are being reported and executed.

Context: The software engineering community is becoming more aware of the need for experimental replications. In spite of the importance of this topic, there is still much inconsistency in the terminology used to describe replications.

Context: technology transfer and innovation dissemination are key success factors for an enterprise. The shift to a new software technology determines inevitable changes to ingrained and familiar processes and at the same time leads to changes in practices, requires training and commitment on behalf of technical staff and management. As so, it cannot leave out neither organizational nor technical factors. Objective: our conjecture is that the process of innovation dissemination is facilitated if the new technology is supported by empirical evidence. In this sense, Empirical Software Engineering (ESE) serves as support for transferring an innovation, either it being a process or product, within production processes. Method: this paper investigates the relation between empirical studies and technical/organizational factors in order to identify the most suitable empirical study for disseminating an innovation in an enterprise. The analysis has been carried out with respect to empirical studies carried out during a ten year time span within the Software Engineering Research LABoratory (SERLAB), at the University of Bari. Results: the results point out that a critical factor in designing empirical studies is the quality model, i.e. measurement program defined by researchers during the study and used to collect relevant information on the effectiveness and efficacy of the innovation being transferred, in order to gain commitment on behalf of stakeholders who will finally adopt the technology. Conclusion: the study outcomes provide an empirically founded guideline that can be used when choosing the most appropriate approach for addressing an innovation.

Abstract ISO has recently published Part 7 of the ISO/IEC 15504 standard, with the aim of determining the extent to which an organization consistently implements processes that contribute to achievement of its business goals. This new Part 7 of ISO/IEC 15504 has addressed issues related to the assessment of organizational maturity, as the CMMI‐DEV model also does. Recently, growing interest has been shown towards the need to harmonize different improvement models or standards, thereby presenting an integrated vision about them. All this being so and in an effort to offer information on how the maturity levels described in these two models are related, we have carried out the harmonization of these two models. It is based, firstly, on a mapping between processes of ISO 12207:08 and process areas of CMMI‐DEV, and in second place, on a matching between processes of ISO 12207:08 and ISO 15504‐5. For this work, we have taken into account the latest versions of the models, and defined a suitable process to carry out the mapping in a systematic way. We established differences and similarities between the maturity levels (and their processes) described in these models, our goal being to support organizations which are interested in tackling organizational maturity. Copyright © 2009 John Wiley & Sons, Ltd.

Objective: Evaluate the value of a product line in terms of maintainability, extensibility and configurability with refer to the interested stakeholders: customers, maintainers, producers. Rationale: There are values that customers constantly require in a modern software application. Some of these values are supported by product lines. Nevertheless, in the industrial and scientific communities the conjecture that customer values clash with those of producers/maintainers is diffused. Design of Study: we have designed and carried out a case study in an industrial context on an ongoing project to verify the validity of a product line in creating value for stakeholders. So data was collected as the project was being executed along a nine month period. Then, descriptive statistics and hypothesis testing were carried out. Results: experience acquired during the execution of an industrial project has allowed the authors to point out the differences between program families and software product lines. Also, the case study has shown how product lines contribute to stakeholder value proposition elicitation and reconciliation. Conclusions: This study has represented a first step towards analyzing the value that product lines represent for various stakeholders.

Effort estimation is a long faced problem, but, in spite of the amount of research spent in this field, it still remains an open issue in the software engineering community. In two previous works the authors proposed an approach named dynamic calibration for effort estimation of software projects. In this paper they present a tool named SPEED that implements the dynamic calibration approach

Dynamic calibration (DC), presented by the authors in previous works has proved to be a flexible approach for massive maintenance software project estimation, able to recalibrate an estimation model in use according to relevant process performance changes pointed out by the project manager. Nevertheless, it results quite subjective in its application and tightly based on manager experience. In this work the authors present an improvement of the approach based on the use of statistical process control (SPC) technique. SPC is a statistically based method able to quickly highlight shift in process performances. It is well known in manufacturing contexts and it has recently emerged in the software engineering community. In this work, authors have integrated SPC in DC as decision support tool for identifying when recalibration of the estimation model must be carried out. This extension makes DC less "person-based", more deterministic and transferable in its use than the previous version. The extended approach has been experimented on industrial data related to a renewal project and the results compared with both, a concurrent approach such as analogy based estimation and its previous version. The results are encouraging and stimulate further investigation.

Software reuse can become a key factor for improving and guaranteeing software quality, when adopted systematically all along the software process. The main characteristic of reuse-oriented processes is that they require a common repository for storing, searching and retrieving software modules. Moreover, reuse occurs systematically and is an integrated part of the process. Previous works of the same authors have empirically shown that the full reuse maintenance model (FRM) slows down quality degradation following to maintenance interventions on a software system. This work is a further step in the investigation towards demonstrating how reuse oriented development (ROD) impacts on software quality; how it favors FRM model; and finally, whether reuse-oriented development influences productivity, and as so, is more efficient. This has been done through a case study carried out on two ongoing industrial projects. Results are positive and support our research hypotheses.

Effort estimation is a long faced problem, but, in spite of the amount of research spent in this field, it still remains an open issue in the software engineering community. This is true especially in the case of renewal of legacy systems, where the current and well established approaches also fail. This paper presents an application of the method named dynamic calibration for effort estimation of renewal projects together with its experimental validation. The approach satisfies all the requirements of the estimation models. The results obtained by applying dynamic calibration are compared with those obtained with a competitor method: estimation by analogy. The results are shown to be promising although further experimentation on field is needed.

Subcontracting is one of the challenges modern software engineering must face. The need for decision models to guide software engineers in identifying when it is advisable and how to divide a contract, which risks it involves and which actions should be adopted to control them, is continuously increasing. Many researchers have proposed and experimented with these decision models from various viewpoints. We feel the need for a method able to merge and formalize these experiences in order for them to be reused by the entire community. This work presents an approach for formalizing such experience. It also proposes a decision model for dividing a contract according to its characteristics and to the cooperating organizations. This decision model, together with others present in the literature, have been integrated and formalized in an experience package.