Organizations are increasingly adopting Participatory Reuse, an InnerSource practice where consumers of the reusable assets also contribute to their development and maintenance. While existing literature has reported various challenges, solutions, and lessons learned from organizations engaging in participatory reuse, a consolidated synthesis of these findings remains lacking. This study aims to collect and analyze the challenges, solutions, and lessons learned in participatory reuse from empirical works conducted within industrial settings. We conducted a case survey of $\mathbf{3 4}$ primary studies in $\mathbf{1 5}$ case organizations that have implemented participatory reuse practices. Based on the core characteristics of software reuse, we categorized the identified challenges, solutions, and lessons learned into three themes: developing reusable assets, using reusable assets, and managing participatory reuse. We organized the identified evidence as a catalog of participatory reuse challenges, solutions, and lessons learned. This catalog is intended to support both practitioners and researchers in identifying relevant strategies to address specific participatory reuse challenges within their organizations. We also pilot tested the catalog with four industry experts, who found it valuable in identifying solutions and lessons learned from the challenges.

InnerSource is a collaborative and cost-effective approach to software development that encourages software reuse and co-creation. Many organizations have embraced InnerSource (IS) to reap these benefits by opening their projects for reuse within the organization and enabling contributions from various teams. However, it is crucial to establish a certain level of maturity in IS projects before inviting contributions. This paper introduces IS self-assessment guidelines designed to help IS teams at IKEA (Ingka Group) evaluate the maturity level of their IS projects. We developed the IS self-assessment guidelines following the design science methodology. The focus of the guidelines is to assess how ready IS teams are to receive external contributions.

Abstract Contemporary practices such as InnerSource (adopting open source practices within an organization), continuous integration and delivery (CI/CD), and the use of microservices promote software reuse. Although the benefits of individual contemporary practices on reuse may be known, the implications of collective contemporary practices, mainly challenges and improvements to mitigate the challenges, are to a large extent unknown. In this study, we investigate the additional effort (cost factors), benefits, challenges, and potential improvements in contemporary reuse at Ericsson. We performed the study in two phases: a) the initial data collection based on a combination of data collection methods (e.g., interviews, discussions, company portals), and b) a follow-up group discussion after a year to understand the status of the challenges and improvements identified in the first phase. Our results indicate that developing reusable assets resulted in upfront cost factors, such as additional effort in ensuring compliance. Furthermore, development with reuse also resulted in cost factors, for example, additional effort in integrating and understanding reusable assets. Ericsson perceived the cost factors as an investment resulting in long-term benefits such as improved quality, productivity, customer experience, and way of working. The main challenge faced by Ericsson was the pressure on the producers of reusable assets. Our study shows how InnerSource can lead to an increase in contributions to reusable assets. Furthermore, Ericsson implemented measures like automating compliance checks that improved the maturity of reusable assets, resulting in an increase in their reuse. In summary, effective use of contemporary practices such as InnerSource and CI/CD, along with the use of microservices, can facilitate large scale reuse.

ABSTRACT Context Rapid Reviews are secondary studies aiming to deliver evidence to experts in a more timely manner and with lower costs than traditional literature reviews. Previous studies have shown that experts and researchers are positive toward Rapid Reviews. However, little is known about how Rapid Reviews differ from traditional Systematic Reviews. Objective The goal of this paper is to compare a Rapid Review with a Systematic Review in terms of their methods (e.g., search strategy, study selection, quality assessment, and data extraction) and findings to understand how optimizing the traditional Systematic Review method impacts what we obtain with Rapid Review. Method To achieve this goal, we conducted a Systematic Review with the same research questions answered by a pre‐existing Rapid Review and compared those two studies. Also, we surveyed experts from industry and academia to evaluate the relevance of the findings obtained from both the secondary studies. Results The Rapid Review lasted 6 days, while the Systematic Review took 1 year and 2 months. The main bottlenecks we identified in the Systematic Review are (i) executing the search strategy and (ii) selecting the procedure. Together, they took 10 months. The researchers had to analyze the information from 11,383 papers for the Systematic Review compared with 1973 for the Rapid Review. Still, most ( 78%) of the papers included in the Systematic Review were returned by the Rapid Review search, and some papers that could be included were unduly excluded during the Rapid Review's selection procedure. Both secondary studies identified the same number of pieces of evidence (30), but the pieces of evidence are not the same. Conclusion The Rapid Review and Systematic Review results are inherently different and complementary. The time and cost to conduct a Systematic Review can be prohibitive in experts' contexts. Thus, at least in such situations, a Rapid Review may be an adequate choice. Moreover, a Rapid Review may be executed in the experts' context as a previous low‐cost step before deciding to invest in a high‐cost Systematic Review.

Software reuse costs and benefits have been investigated in several primary studies, which have been aggregated in multiple secondary studies as well. However, existing secondary studies on software reuse have not critically appraised the evidence in primary studies. Moreover, there has been relatively less focus on how software reuse costs and benefits were measured in the primary studies, and the aggregated evidence focuses more on software reuse benefits than reuse costs. This study aims to cover the gaps mentioned in the context above by synthesizing and critically appraising the evidence reported on software reuse costs and benefits from industrial cases. We used a systematic literature review (SLR) to conduct this study. The results of this SLR are based on a final set of 30 primary studies. We identified nine software reuse benefits and six software reuse costs, in which better quality and improved productivity were investigated the most. The primary studies mostly used defect-based and development time-based metrics to measure reuse benefits and costs. Regarding the reuse practices, the results show that software product lines, verbatim reuse, and systematic reuse were the top investigated ones, contributing to more reuse benefits. The quality assessment of the primary studies showed that most of them are either of low (20%) or moderate (67%) quality. Based on the number and quality of the studies, we conclude that the strength of evidence for better quality and improved productivity as reuse benefits is high. There is a need to conduct more high quality studies to investigate, not only other reuse costs and benefits, but also how relatively new reuse-related practices, such as InnerSource and microservices architecture, impact software reuse.

Abstract During software evolution, decisions related to components' origin or source significantly impact the quality properties of the product and development metrics such as cost, time to market, ease of maintenance, and further evolution. Thus, such decisions should ideally be supported by evidence, i.e., using previous experiences and information from different sources, even own previous experiences. A hindering factor to such reuse of previous experiences is that these decisions are highly context‐dependent and it is difficult to identify when previous experiences come from sufficiently similar contexts to be useful in a current setting. Conversely, when documenting a decision (as a decision experience), it is difficult to know which context factors will be most beneficial when reusing the experience in the future. An interview study is performed to identify a list of context factors that are perceived to be most important by practitioners when using experiences to support decision‐making for component sourcing, using a specific scenario with alternative sources of experiences. We observed that the further away (from a company or an interviewee) the experience evidence is, as is the case for online experiences, the more context factors are perceived as important by practitioners to make use of the experience. Furthermore, we discuss and identify further research to make this type of decision‐making more evidence‐based.

Background: InnerSource consists of the use of open source development techniques within the corporation. It helps improve software reuse through increased transparency and inter-team collaboration. Companies need to understand their context and specific needs before deciding to adopt any specific InnerSource practices since they cannot apply all InnerSource practices at once. Aim: This study aims to support the case company in assessing its readiness for adopting InnerSource practices to improve its internal reuse, identify and prioritize the improvement areas, and identify suitable solutions. Method: We performed a case study using a questionnaire and a workshop to check the current and desired status of adopting InnerSource practices and collect potential solutions. Results: The study participants identified that the company needs to prioritize the improvements related to the discoverability, communication channels, and ownership of the reusable assets. In addition, they identified certain InnerSource practices as solutions for the prioritized improvement areas, such as better structured repositories for storing and searching the reusable assets and standardized documentation of the reusable assets. Conclusion: The questionnaire instrument aids the case company in identifying the improvement areas related to InnerSource and reuse practices. InnerSource practices could improve the development and maintenance of reusable assets. Keywords: InnerSource, software reuse, readiness

Kubernetes is a free, open-source container orchestration system for deploying and managing Docker containers that host microservices. Kubernetes cluster logs help in determining the reason for the failure. However, as systems become more complex, identifying failure reasons manually becomes more difficult and time-consuming. This study aims to identify effective and efficient classification algorithms to automatically determine the failure reason. We compare five classification algorithms, Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting Classifier, and Multilayer Perceptron. Our results indicate that Random Forest produces good accuracy while requiring fewer computational resources than other algorithms.

Background: Modern Code Review (MCR) is a lightweight alternative to traditional code inspections. While secondary studies on MCR exist, it is u a nknown whether the research community has targeted themes that practitioners consider important. Objectives: The objectives are to provide an overview of MCR research, analyze the practitioners’ opinions on the importance of MCR research, investigate the alignment between research and practice, and propose future MCR research avenues. Method: We conducted a systematic mapping study to survey state of the art until and including 2021, employed the Q-Methodology to analyze the practitioners’ perception of the relevance of MCR research, and analyzed the primary studies’ research impact. Results: We analyzed 244 primary studies, resulting in five themes. As a result of the 1,300 survey data points, we found that the respondents are positive about research investigating the impact of MCR on product quality and MCR process properties. In contrast, they are negative about human factor– and support systems–related research. Conclusion: These results indicate a misalignment between the state of the art and the themes deemed important by most survey respondents. Researchers should focus on solutions that can improve the state of MCR practice. We provide an MCR research agenda that can potentially increase the impact of MCR research.

Abstract Software engineering is decision intensive. Evidence‐based software engineering is suggested for decision‐making concerning the use of methods and technologies when developing software. Software development often includes the reuse of software assets, for example, open‐source components. Which components to use have implications on the quality of the software (e.g., maintainability). Thus, research is needed to support decision‐making for composite software. This paper presents a roadmap for research required to support evidence‐based decision‐making for choosing and integrating assets in composite software systems. The roadmap is developed as an output from a 5‐year project in the area, including researchers from three different organizations. The roadmap is developed in an iterative process and is based on (1) systematic literature reviews of the area; (2) investigations of the state of practice, including a case survey and a survey; and (3) development and evaluation of solutions for asset identification and selection. The research activities resulted in identifying 11 areas in need of research. The areas are grouped into two categories: areas enabling evidence‐based decision‐making and those related to supporting the decision‐making. The roadmap outlines research needs in these 11 areas. The research challenges and research directions presented in this roadmap are key areas for further research to support evidence‐based decision‐making for composite software.

Reviewing source code is a common practice in a modern and collaborative coding environment. In the past few years, the research on modern code reviews has gained interest among practitioners and researchers. The objective of our investigation is to observe the evolution of research related to modern code reviews, identify research gaps and serve as a basis for future research. We use a systematic mapping approach to identify and classify 177 research papers. As preliminary result of our investigation, we present in this paper a classification scheme of the main contributions of modern code review research between 2005 and 2018.

Usage of software engineering research in industrial practice is a well-known challenge. Synthesis of knowledge from multiple research studies is needed to provide evidence-based decision-support for industry. The objective of this paper is to present a vision of how a knowledge translation framework may look like in software engineering research, in particular how to translate research evidence into practice by combining contextualized expert opinions with research evidence. We adopted the framework of knowledge translation from health care research, adapted and combined it with a Bayesian synthesis method. The framework provided in this paper includes a description of each step of knowledge translation in software engineering. Knowledge translation using Bayesian synthesis intends to provide a systematic approach towards contextualized, collaborative and consensus-driven application of research results. In conclusion, this paper contributes towards the application of knowledge translation in software engineering through the presented framework.

Knowledge translation is defined, in health sciences, as “the exchange, synthesis and ethically sound application of research results in practice”. The objective of this paper is to implement and conduct a feasibility evaluation of a knowledge translation framework in software engineering. We evaluated the outcome of the knowledge translation framework in an industrial setting, along with the effectiveness of the interventions undertaken as part of knowledge translation in a multi-case study. The results of the evaluation suggest that the practitioners perceive the knowledge translation framework to be valuable and useful. In conclusion, this paper contributes towards the reporting of a systematic implementation of knowledge translation and evaluating its use in software engineering.

The choice of which software component to use influences the success of a software system. Only a few empirical studies investigate how the choice of components is conducted in industrial practice. This is important to understand to tailor research solutions to the needs of the industry. Existing studies focus on the choice for off-the-shelf (OTS) components. It is, however, also important to understand the implications of the choice of alternative component sourcing options (CSOs), such as outsourcing versus the use of OTS. Previous research has shown that the choice has major implications on the development process as well as on the ability to evolve the system. The objective of this study is to explore how decision making took place in industry to choose among CSOs. Overall, 22 industrial cases have been studied through a case survey. The results show that the solutions specifically for CSO decisions are deterministic and based on optimization approaches. The non-deterministic solutions proposed for architectural group decision making appear to suit the CSO decision making in industry better. Interestingly, the final decision was perceived negatively in nine cases and positively in seven cases, while in the remaining cases it was perceived as neither positive nor negative.

Ethical guidelines of software engineering journals require authors to provide statements related to the conflict of interest and the process of obtaining consent (if human subjects are involved). The objective of this study is to review the reporting of the ethical considerations in Empirical Software Engineering - An International Journal. The results indicate that two out of seven studies reported some ethical information however, not explicitly. The ethical discussions were focussed on anonymity and confidentiality. Ethical aspects such as competence, comprehensibility and vulnerability of the subjects were not discussed in any of the papers reviewed in this study. It is important to not only state that consent was obtained however, the procedure of obtaining consent should be reported to improve the accountability and trust.

Systematic literature reviews in software engineering are necessary to synthesize evidence from multiple studies to provide knowledge and decision support. However, synthesis methods are underutilized in software engineering research. Moreover, translation of synthesized data (outcomes of a systematic review) to provide recommendations for practitioners is seldom practiced. The objective of this paper is to introduce the use of Bayesian synthesis in software engineering research, in particular to translate research evidence into practice by providing the possibility to combine contextualized expert opinions with research evidence. We adopted the Bayesian synthesis method from health research and customized it to be used in software engineering research. The proposed method is described and illustrated using an example from the literature. Bayesian synthesis provides a systematic approach to incorporate subjective opinions in the synthesis process thereby making the synthesis results more suitable to the context in which they will be applied. Thereby, facilitating the interpretation and translation of knowledge to action/application. None of the synthesis methods used in software engineering allows for the integration of subjective opinions, hence using Bayesian synthesis can add a new dimension to the synthesis process in software engineering research.

Background: Systematic literature studies are commonly used in software engineering. There are two main ways of conducting the searches for these type of studies; they are snowballing and database searches. In snowballing, the reference list (backward snowballing - BSB) and citations (forward snowballing - FSB) of relevant papers are reviewed to identify new papers whereas in a database search, different databases are searched using predefined search strings to identify new papers. Objective: Snowballing has not been in use as extensively as database search. Hence it is important to evaluate its efficiency and reliability when being used as a search strategy in literature studies. Moreover, it is important to compare it to database searches. Method: In this paper, we applied snowballing in a literature study, and reflected on the outcome. We also compared database search with backward and forward snowballing. Database search and snowballing were conducted independently by different researchers. The searches of our literature study were compared with respect to the efficiency and reliability of the findings. Results: Out of the total number of papers found, snowballing identified 83% of the papers in comparison to 46% of the papers for the database search. Snowballing failed to identify a few relevant papers, which potentially could have been addressed by identifying a more comprehensive start set. Conclusion: The efficiency of snowballing is comparable to database search. It can potentially be more reliable than a database search however, the reliability is highly dependent on the creation of a suitable start set.