Context: The state-of-the-art and practice on software quality is growing constantly and presents several challenges. The ever-growing body of knowledge on the topic, obfuscates the situation further, the lack of explicit structure makes it difficult to identify which properties exist today and how they can be evaluated, two critical aspects to increase software quality. Objective: A step to disambiguate software properties descriptions is made via a Property Model Ontology (PMO) and steps towards the evaluation of the structure are carried out together with experts. The objectives of this paper are: 1) present in detail the PMO, 2) describe the research process used to develop and evaluate the PMO, and, 3) exemplify the usage of the PMO through real instantiations obtained from practitioners and researchers. Method: Expert interviews and qualitative research methods are used to evaluate the PMO and develop a proof of concept. Results: The PMO consists of concepts describing extra-functional properties (EFPs) and their evaluation methods, i.e., how to measure the properties. The PMO is instantiated in a modelling environment through a metamodel and an online web-content management system. Conclusions: Consensus on the definition and structure of EFPs is achieved and a common understanding on how they can be reused in practice.

Context: The state-of-the-art and practice on software quality is growing constantly and presents several challenges. The ever-growing body of knowledge on the topic, obfuscates the situation further, the lack of explicit structure makes it difficult to identify which properties exist today and how they can be evaluated, two critical aspects to increase software quality. Objective: A step to disambiguate software properties descriptions is made via a Property Model Ontology (PMO) and steps towards the evaluation of the structure are carried out together with experts. The objectives of this paper are: 1) present in detail the PMO, 2) describe the research process used to develop and evaluate the PMO, and, 3) exemplify the usage of the PMO through real instantiations obtained from practitioners and researchers. Method: Expert interviews and qualitative research methods are used to evaluate the PMO and develop a proof of concept. Results: The PMO consists of concepts describing extra-functional properties (EFPs) and their evaluation methods, i.e., how to measure the properties. The PMO is instantiated in a modelling environment through a metamodel and an online web-content management system. Conclusions: Consensus on the definition and structure of EFPs is achieved and a common understanding on how they can be reused in practice.

Background: Modern Code Reviews (MCR) are frequently adopted when assuring code and design quality in continuous integration and deployment projects. Although tiresome, they serve a secondary purpose of learning about the software product. Aim: Our objective is to design and evaluate a support tool to help software developers focus on the most important code fragments to review and provide them with suggestions on what should be reviewed in this code. Method: We used design science research to develop and evaluate a tool for automating code reviews by providing recommendations for code reviewers. The tool is based on Transformer-based machine learning models for natural language processing, applied to both programming language code (patch content) and the review comments. We evaluate both the ability of the language model to match similar lines and the ability to correctly indicate the nature of the potential problems encoded in a set of categories. We evaluated the tool on two open-source projects and one industry project. Results: The proposed tool was able to correctly annotate (only true positives) 35%–41% and partially correctly annotate 76%–84% of code fragments to be reviewed with labels corresponding to different aspects of code the reviewer should focus on. Conclusion: By comparing our study to similar solutions, we conclude that indicating lines to be reviewed and suggesting the nature of the potential problems in the code allows us to achieve higher accuracy than suggesting entire changes in the code considered in other studies. Also, we have found that the differences depend more on the consistency of commenting rather than on the ability of the model to find similar lines.

Context: Action research is popular in software engineering due to its industrial nature and promises of effective technology transfers. Yet, the methodology is still gaining popularity, and guidelines for conducting quality action research studies are needed. Objective: This paper aims to collect, summarize, and discuss guidelines for conducting action research in academia-industry collaborations. The guidelines are designed for researchers and practitioners alike. Method: I use existing guidelines for empirical studies and my own experiences to define guidelines for researchers and host organizations for conducting action research. Results: I identified 22 guidelines for conducting action research studies. They provide actionable recommendations on identifying the relevant context, planning and executing interventions (actions), reporting them, and reasoning around the ethics of action research. Conclusions: The paper concludes that the best way of engaging with action research is when we can be embedded in the host organization and when the collaboration leads to tangible change in the host organization and the generation of new scientific results.

Background: In software engineering, each software product has a life cycle that at some point results in a decision being made with regard to extending its maintenance or upgrading the system to a new platform and architecture via a re-engineering or migration process. However, sometimes this decision is a non-starter; the technical dept accumulates, and platforms cease to exist, meaning that there will always be a time when extending the life support of a legacy system is no longer simply an option, and the service must be modernized. Aim: In this paper, we focus on the migration processes, where a legacy system is updated to a microservice architecture, to understand the current state-of-the-art, applied industry practices and potential pitfalls or research gaps in the topic domain. The study aims to explore previous research to find related trends and expose gaps in the literature. Method: We conducted a systematic mapping study on the research trends within the topic of redesign and re-engineering projects related to microservice architectures to understand what we know about microservices, what the current research trends in the area are, and if possible, what the common nominators for successful migration processes are. Results: Our observations reveal that most microservice migration research is confined to journal articles and conference proceedings. However, a severe fragmentation in publication venues exists within the field. Furthermore, the focus of the research field is primarily on the transformation phase of the re-engineering process, with the majority of the contributions being managerial in nature, particularly of the process type. Additionally, over 50 % of the research conducted is empirical in nature. Conclusion: Based on the results, microservice migration research is maturing well; most of the research is empirical. The research field is scattered. There are notable technical, managerial, and organizational challenges and differing motivations. To better understand the motivations and challenges of the practitioners, we are going to conduct survey and interview studies within this field.

Background: Continuous software engineering practices are currently considered state of the art in Software Engineering (SE). Recently, this interest in continuous SE has extended to ML system development as well, primarily through MLOps. However, little is known about continuous SE in ML development outside the specific continuous practices present in MLOps. Aim: In this paper, we explored continuous SE in ML development more generally, outside the specific scope of MLOps. We sought to understand what challenges organizations face in adopting all the 13 continuous SE practices identified in existing literature. Method: We conducted a multiple case study of organizations developing ML systems. Data from the cases was collected through thematic interviews. The interview instrument focused on different aspects of continuous SE, as well as the use of relevant tools and methods. Results: We interviewed 8 ML experts from different organizations. Based on the data, we identified various challenges associated with the adoption of continuous SE practices in ML development. Our results are summarized through 7 key findings. Conclusion: The largest challenges we identified seem to stem from communication issues. ML experts seem to continue to work in silos, detached from both the rest of the project and the customers.

Software development encompasses various factors beyond technical considerations. Neglecting non-technical elements like individuals, processes, culture, and social and organizational aspects can lead to debt-like characteristics that demand attention. Therefore, we introduce the non-technical debt (NTD) concept to encompass and explore these aspects. This indicates the applicability of the debt analogy to non-technical facets of software development. Technical debt (TD) and NTD share similarities and often arise from risky decision-making processes, impacting both software development professionals and software quality. Overlooking either type of debt can lead to significant implications for software development success. The current study conducts a comprehensive multivocal literature review (MLR) to explore the most recent research on NTD, its causes, and potential mitigation strategies. For analysis, we carefully selected 40 primary studies among 110 records published until October 1, 2022. The study investigates the factors contributing to the accumulation of NTD in software development and proposes strategies to alleviate the adverse effects associated with it. This MLR offers a contemporary overview and identifies prospects for further investigation, making a valuable contribution to the field. The findings of this research highlight that NTD’s impacts extend beyond monetary aspects, setting it apart from TD. Furthermore, the findings reveal that rectifying NTD is more challenging than addressing TD, and its consequences contribute to the accumulation of TD. To avert software project failures, a comprehensive approach that addresses NTD and TD concurrently is crucial. Effective communication and coordination play a vital role in mitigating NTD, and the study proposes utilizing the 3C model as a recommended framework to tackle NTD concerns.

Background: As low-code development platforms (LCDPs) are becoming a trend, understanding how end-user developers think and feel as they work with such platforms is important. Particularly, assessing experiences during episodes of use can contribute to overall experience throughout long-term use. Aim: This paper aims to understand end-user developers’ episodic experience when they are building an application on a low-code platform and to provide guidance on how such experiences can be measured. Method: We designed the Episodic Developer Experience Questionnaire for LCDPs based on prior literature and refined it through expert Delphi sessions. The instrument contains 10 individual experience items, capturing various aspects of episodic experience. We further validated it through remote online tests on an LCDP. Results: The results showed significant differences in the relationships between items describing aspects of overall experience and items describing perceptions of tool quality and task difficulty. Programming expertise also affected end-user developers’ episodic experience. Conclusion: The study illustrates the design of questionnaire-based experience assessment in the context of development and identifies the importance of separating personal experience from assessment of tasks and tools since tool quality and task difficulty do not necessarily influence experience straightforwardly.

Background: Systematic literature reviews (SLRs) have become a standard practice as part of software engineering (SE) research, although their quality varies. To build on the reviews, both for future research and industry practice, they need to be of high quality. Aim: To assess the quality of SLRs in SE, we put forward an appraisal instrument for SLRs. Method: A well-established appraisal instrument from research in healthcare was used as a starting point to develop the instrument. It is adapted to SE using guidelines, checklists, and experiences from SE. The first version was reviewed by four external experts on SLRs in SE and updated based on their feedback. To demonstrate its use, the updated version was also used by the authors to assess a sample of six selected systematic literature studies. Results: The outcome of the research is an appraisal instrument for quality assessment of SLRs in SE. The instrument includes 15 items with different options to capture the quality. The instrument also supports consolidating the items into groups, which are then used to assess the overall quality of an SLR. Conclusion: The presented instrument may be helpful support for an appraiser in assessing the quality of SLRs in SE.

Background: Python is a popular and easy-to-use programming language.It is constantly expanding, with new features and libraries being introduced daily for a broad range of applications.This dynamic expansion needs a robust support structure for developers to effectively utilise the language.Aim: In this study we conduct an in-depth analysis focusing on several research topics to understand the theme of Python questions and identify the challenges that developers encounter, using the questions posted on Stack Overflow.Method:We perform a quantitative and qualitative analysis of Python questions in Stack Overflow.Topic Modelling is also used to determine the most popular and difficult topics among developers. Results:The findings of this study revealed a recent surge in questions about scientific computing libraries pandas and TensorFlow.Also, we observed that the discussion of Data Structures and Formats is more popular in the Python community, whereas areas such as Installation, Deployment, and IDE are still challenging. Conclusion:This study can direct the research and development community to put more emphasis on tackling the actual issues that Python programmers are facing.