The domain of rural areas, including rural communities, agriculture, and forestry, is going through a process of deep digital transformation. Digitalisation can have positive impacts on sustainability in terms of greater environmental control, and community prosperity. At the same time, it can also have disruptive effects, with the marginalisation of actors that cannot cope with the change. When developing a novel system for rural areas, requirements engineers should carefully consider the specific socio-economic characteristics of the domain, so that potential positive effects can be maximised, while mitigating negative impacts. The goal of this paper is to support requirements engineers with a reference catalogue of drivers, barriers and potential impacts associated to the introduction of novel ICT solutions in rural areas. To this end, we interview 30 cross-disciplinary experts in digitalisation of rural areas, and we analyse the transcripts to identify common themes. According to the experts, main drivers are economic, with the possibility of reducing costs, and regulatory, as institutions push for more precise tracing and monitoring of production; barriers are the limited connectivity, but also distrust towards technology and other socio-cultural aspects; positive impacts are socio-economic (e.g., reduction of manual labour, greater productivity), while negative ones include potential dependency from technology, with loss of hands-on expertise, and marginalisation of certain actors (e.g., small farms, subjects with limited education). This paper contributes to the literature with a domain-specific catalogue that characterises digitalisation in rural areas. The catalogue can be used as a reference baseline for requirements elicitation endeavours in rural areas, to support domain analysis prior to the development of novel solutions, as well as fit-gap analysis for the adaptation of existing technologies.

Background: Distributed data-intensive systems are increasingly designed to be only eventually consistent. Persistent data is no longer processed with serialized and transactional access, exposing applications to a range of potential concurrency anomalies that need to be handled by the application itself. Controlling concurrent data access in monolithic systems is already challenging, but the problem is exacerbated in distributed systems. To make it worse, only little systematic engineering guidance is provided by the software architecture community regarding this issue. Aims: In this paper, we report on our study of the effectiveness and applicability of the novel design guidelines we are proposing in this regard. Method: We used action research and conducted it in the context of the software architecture design process of a multi-site platform development project. Results: Our hypotheses regarding effectiveness and applicability have been accepted in the context of the study. The initial design guidelines were refined throughout the study. Thus, we also contribute concrete guidelines for architecting distributed data-intensive systems with eventually consistent data. The guidelines are an advancement of Domain-Driven Design and provide additional patterns for the tactical design part. Conclusions: Based on our results, we recommend using the guidelines to architect safe eventually consistent systems. Because of the relevance of distributed data-intensive systems, we will drive this research forward and evaluate it in further domains.

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.

Mashup user interfaces provides their functionality through the combination of different services. The integration of such services can be solved by using reusable and third-party components. Furthermore, these interfaces must be adapted to user preferences, context changes, user interactions and c omponent availability. Model transformation is a useful mechanism to address this adaptation but normally these operations only focus on the functional requirements. In this sense, quality attributes should be included in the adaptation process to obtain the best adapted mashup user interface. This paper proposes a generic quality-aware transformation process to support the adaptation of software architectures. The transformation process has been applied in ENIA, a geographic information system, by constructing a specific quality model for the adaptation of mashup user interfaces. This model is taken into account for evaluating the different transformation alternatives and choosing the one that maximizes the quality assessments. The approach has been validated by a set of adaptation scenarios that are intended to maximize different quality factors and therefore apply distinct combinations of metrics.

Software productivity and effort estimationEvery software business has to be able to budget and plan its software development projects realistically.This is the prerequisite for professional project management, for competitive offers, and for a more objective assessment of the offers made by potential contractors.In order to achieve these objectives, a software business must be capable of estimating their own productivity in developing software at an appropriate level of quality and produce accurate estimates for the effort required to develop software.Since 1999, the International Conference on Product-Focused Software Process Improvement (PROFES) has established itself as one of the most recognized international conferences on professional software process improvement (SPI) motivated by product, process, and service quality needs.PROFES addresses both quality engineering and management topics, including processes, methods, techniques, tools, organizations, and enabling SPI.Both solutions found in practice and relevant research results from academia are presented.The 14 th instance of the conference -PROFES 2013was held in Paphos, Cyprus, from 12 June to 14 June 2013.The technical program was selected by a committee of leading experts in SPI, software process modeling, and empirical software engineering research.Overall, 41 papers were submitted, with each paper getting reviewed by at least three reviewers.After thorough evaluation, the Program Committee finally selected 22 technical full papers.The topics addressed in these papers indicate that SPI is a vibrant research discipline and is also of high interest for industry.Many papers report on case studies or SPI-related experience gained in industry.The technical program consisted of the following tracks: decision support in software engineering, empirical software engineering, managing software processes, safety-critical software engineering, software measurement, SPI, and software maintenance.We would like to thank all authors, presenters, and session chairs for their time and effort in making PROFES 2013 a success.Our special thanks go to our keynote speakers for highlighting the most recent findings and novel results in the area of process improvement.We were proud to have three keynote speakers: Stefan Wagner (University of Stuttgart, Germany) on 'Making Software Quality Visible', Alexis Ocampo (ECOPETROL S.A., Colombia) on 'ECO-MAPS: Information Quality Driven Enterprise Modeling', and Christos Xenis (LogiSoft, Cyprus) on the 'Implementation of an Online Multi-Level and Device-Independent Time & Attendance System'.Furthermore, we also would like to thank the organizers of the co-located tutorial sessions: Hermann Kaindl (Vienna University of Technology, Austria) on 'Model-based Transition from Requirements to High-level Software Design' and Jens Heidrich (Fraunhofer IESE, Germany) on 'Software Effort Estimation and Risk Management'.In addition, we sincerely thank all Program Committee members and reviewers who provided excellent support in reviewing the papers, Andreas Jedlitschka (Fraunhofer IESE, Germany) for his work as General Chair, and George Angelos Papadopoulos (

The research about the formalization and conduction of controlled experiments in software engineering has reported important insights and guidelines for their organization. However, the computational support to formalize and execute controlled experiments still requires deeper investigation. In this context, this paper presents an empirical study that evaluates a domain-specific language (DSL) proposed to formalize controlled experiments in software engineering. The language is part of a model-driven approach that allows the generation of executable workflows for the experiment participants, according to the statistical design of the experiment. Our study involves the modeling of 16 software engineering experiments to analyze the completeness and expressiveness of the investigated DSL when specifying different controlled experiments. The results highlight several limitations of the DSL that affect the formalization and execution of experiments. These outcomes were used to extend and improve the evaluated DSL. Finally, the improved version of the language was used to model the same experiments in order to illustrate the benefits of the proposed improvements.

(Background) Empirical Software Engineering (SE) strives to provide empirical evidence about the pros and cons of SE approaches. This kind of knowledge becomes relevant when the issue is whether to change from a currently employed approach to a new one or not. An informed decision is required and is particularly important in the development of safety-critical systems. For example, for the safety analysis of safety-critical embedded systems, methods such as Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA) are used. With the advent of model-based systems and software development, the question arises whether safety engineering methods should also be adopted. New technologies such as Component Integrated Fault Trees (CFT) come into play. Industry demands to know the benefits of these new methods over established ones such as Fault Trees (FT). (Methods) For the purpose of comparing CFT and FT with regard to the capabilities of the safety analysis methods (such as quality of the results) and to the participants' rating of the consistency, clarity, and maintainability of the methods, we designed a comparative study as a controlled experiment using a within-subject design. The experiment was run with seven academic staff members working towards their PhD. The study was replicated with eleven domain experts from industry. (Results) Although the analysis of the tasks' solutions showed that the use of CFT did not yield a significantly different number of correct or incorrect solutions, the participants rated the modeling capacities of CFT higher in terms of model consistency, clarity, and maintainability. (Conclusion) From this first evidence, we conclude that CFT have the potential of being beneficial for companies looking for a safety analysis approachfor projects using model-based development.

Background: The identification of alternative technologies - which is one step of the formal decision-making process -- results in a judgment on whether a technology is considered as a candidate for the further decision-making process. A model has been proposed that aims at improving the delivery of relevant information from ESE research to software managers.

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A systematic approach to decision making in software engineering is required, for instance, if an organization aims at achieving CMMI level three. Rational decision making regarding the selection and introduction of SE technologies requires adequate information about their suitability for the intended organizational context. Research is often unable to provide such information, and this could be one reason why promising techniques are sometimes not adopted in practice. From a research point of view, successful technology transfer requires knowing which information decision makers in industry need, and where they actually look for it. With this knowledge, empirical research can strive to produce the needed information in order to increase the likelihood of successful technology adoption. To address these questions, we conducted an online survey among German software industry decision makers. To focus the survey, we used inspections as an exemplary technology. We invited 9653 companies to participate, from which we received 92 fully completed questionnaires. Our main findings are that information regarding the impact of technologies on product quality, cost, and development time, as well as on technology cost-benefit ratio is considered most important among decision makers. The preferred sources of information are colleagues, textbooks, and industry workshops.

Knowledge management is a relatively young discipline. Nevertheless, it has accumulated a valuable body of knowledge in the structuring of knowledge and in the design of socio-technical knowledge management systems. However, concepts to describe common, recurring patterns of how to describe, structure, interrelate, group, or manage knowledge elements are still missing. In this article, we introduce the concepts “knowledge pattern” and “knowledge antipattern” to describe best and worst practices in knowledge management, “knowledge refactoring” to improve or change knowledge antipatterns, and “quality of knowledge” to describe desirable characteristics of knowledge in knowledge management systems. The concepts are transferred from software engineering to the field of knowledge management based on our experience from several knowledge management projects.