GenAI is playing an increasingly important role in computing courses at all levels, offering new opportunities to support teaching and learning. However, using GenAI effectively raises important concerns regarding trust, academic integrity, and broader social and ethical dimensions. This Working Group was formed to report on the current state of the art in using GenAI in upper-level computing courses to aid educators. The working group will undertake a methodological review of published work and solicit input from the computing educational community as part of the report.

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. © The Author(s) 2024.

Team project courses in software engineering allow students to apply their acquired disciplinary knowledge while developing essential skills needed to work in the software industry. This paper examines the challenges and lessons learned by students in two team project courses involving industrial customers. The first course involves small teams and less complex project, whereas the second course, has larger teams and more complex projects. Using thematic analysis, we analyzed 158 reports submitted by two cohorts of students across two successive team project courses. As per our findings most challenges and lessons learned pertain to soft skills, such as teamwork, working in remote and hybrid setting, and collaboration with industrial customers. The results show that challenges and lessons learned evolve as students progress to the second team project course, for example, managing changes and addressing individual skill gaps were more pronounced in the first project course, while students reported greater coordination, communication, and contribution issues in the second team project course. The alignment between the challenges faced and the lessons learned suggests that addressing challenges in teamwork, collaborating with industrial customers, and working in hybrid or remote settings helped students develop effective strategies to mitigate these challenges. This process offers a valuable learning experience for the students, enriching their professional growth.

Context: Several secondary studies have investigated the relationship between internal quality attributes, source code metrics and external quality attributes. Sometimes they have contradictory results. Objective: We synthesize evidence of the link between internal quality attributes, source code metrics and external quality attributes along with the efficacy of the prediction models used. Method: We conducted a tertiary review to identify, evaluate and synthesize secondary studies. We used several characteristics of secondary studies as indicators for the strength of evidence and considered them when synthesizing the results. Results: From 711 secondary studies, we identified 15 secondary studies that have investigated the link between source code and external quality. Our results show : (1) primarily, the focus has been on object-oriented systems, (2) maintainability and reliability are most often linked to internal quality attributes and source code metrics, with only one secondary study reporting evidence for security, (3) only a small set of complexity, coupling, and size-related source code metrics report a consistent positive link with maintainability and reliability, and (4) group method of data handling (GMDH) based prediction models have performed better than other prediction models for maintainability prediction. Conclusions: Based on our results, lines of code, coupling, complexity and the cohesion metrics from Chidamber & Kemerer (CK) metrics are good indicators of maintainability with consistent evidence from high and moderate-quality secondary studies. Similarly, four CK metrics related to coupling, complexity and cohesion are good indicators of reliability, while inheritance and certain cohesion metrics show no consistent evidence of links to maintainability and reliability. Further empirical studies are needed to explore the link between internal quality attributes, source code metrics and other external quality attributes, including functionality, portability, and usability. The results will help researchers and practitioners understand the body of knowledge on the subject and identify future research directions. © 2023 The Author(s)

Context: 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. Objective: 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. Method: 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. Results: 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. Conclusion: 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. © 2024 The Author(s)

Context: A large number of source code metrics are reported in the literature. It is necessary to systematically collect, describe and classify source code metrics to support research and practice.Objective: We aim to utilize existing secondary studies to develop a cat- alog of source code metrics together with their descriptions. The catalog will also provide information about which units of code (e.g., operators, operands, lines of code, variables, parameters, code blocks, or functions) are used to measure the internal quality attributes and the scope on which they are collected. 

Method: We conducted a tertiary study to identify secondary studies re- porting source code metrics. We have classified the source code metrics according to the measured internal quality attributes, the units of code used in the measures, and the scope at which the source code metrics are collected. 

Results: From 711 secondary studies, we identified 52 relevant secondary studies. We reported 423 source code metrics together with their de- scriptions and the internal quality attributes they measure. Source code metrics predominantly incorporate function as a unit of code to measure internal quality attributes. In contrast, several source code metrics use more than one unit of code when measuring internal quality attributes. Nearly 51% of the source code metrics are collected at the class scope, while almost 12% and 15% of source code metrics are collected at module and application levels, respectively. 

Conclusions: Researchers and practitioners can use the extensive catalog to assess which source code metrics meet their individual needs based on the description and classification scheme presented. 

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.

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. © 2023 Owner/Author.

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 © 2023 Owner/Author.

This article presents an ecosystem that Ericsson developed to systematically practice large-scale reuse of microservices in a cloud-native context. We discuss how various ecosystem aspects, such as its continuous delivery mechanism, marketplace, and automated checking of design rules, facilitated the development and reuse of microservices across Ericsson. We also share lessons learned while developing the ecosystem including the initiatives related to the adoption of InnerSource practices for sustaining the ecosystem.