Knowledge sharing factors for modern code review to minimize software engineering waste

Software engineering activities, for instance, Modern Code Review (MCR) produce quality software by identifying the defects from the code. It involves social coding and provides ample opportunities to share knowledge among MCR team members. However, the MCR team is confronted with the issue of waiting waste due to poor knowledge sharing among MCR team members. As a result, it delays the project delays and increases mental distress. To minimize the waiting waste, this study aims to identify knowledge sharing factors that impact knowledge sharing in MCR. The methodology employed for this study is a systematic literature review to identify knowledge sharing factors, data coding with continual comparison and memoing techniques of grounded theory to produce a unique and categorized list of factors influencing knowledge sharing. The identified factors were then assessed through expert panel for its naming, expressions, and categorization. The study finding reported 22 factors grouped into 5 broad categories i.e. Individual, Team, Social, Facility conditions, and Artifact. The study is useful for researchers to extend the research and for the MCR team to consider these factors to enhance knowledge sharing and to minimize waiting waste

Recomendação de revisores de código no desenvolvimento distribuído de software

Code review is one of the main software defect mitigation techniques. Intrinsically collaborative, the process involves the analysis of source code modifications by technical peer in adherence to the guidelines of each repository. However, the efficiency of this approach is directly associated with choosing the appropriate technical peers to conduct the scrutiny. Especially in global software development, where members of the development team are geographically scattered with time and availability constraints and there are cultural and technical differences, choosing the reviewer becomes a complex task without the aid of computational tools. The aim of this paper is to present Gitrev, a tool that gathers reviewer recommendation methods for distributed software development that enhance collaboration in the review process. Taking into account the particularities of decentralised production processes, the proposed methods are based on historical interactions between developers, through the design of a collaborative network based on data from GitHub hosted open source repositories. The evaluation of the presented solution is a historic research based on the related literature and objective metrics of efficiency of the reviewers’ participation and instantiated on four major projects available on the platform. The results shows the recommended reviewers enhance interaction and collaboration among team members and adhere significantly to the objectives of the review process.A revisão de código é uma das principais técnicas de diminuição de defeitos de software. Intrinsecamente colaborativo, o processo envolve a análise das modificações no código fonte por um revisor, de acordo com as diretrizes de cada repositório. Contudo, a eficiência da técnica está diretamente associada à escolha dos pares técnicos adequados para realizar o escrutínio. Especialmente no Desenvolvimento Distribuído de Software (DDS), onde os membros da equipe de desenvolvimento estão espalhados geograficamente com restrições de horário e disponibilidade, além de diferenças culturais e técnicas, a escolha do revisor vira tarefa complexa sem auxílio de ferramentas computacionais. O objetivo do presente trabalho é apresentar o GitRev, uma plataforma que reúne diferentes métodos de recomendação de revisores para o desenvolvimento distribuído de software, potencializando a colaboração no processo de revisão. Levando em consideração as particularidades dos processos descentralizados de produção, são propostos métodos baseados nas interações históricas entre os desenvolvedores. Esta análise é realizada através da modelagem de uma rede social colaborativa tendo como base os dados oriundos dos repositórios open source hospedados no GitHub. Uma pesquisa histórica é conduzida para avaliar a solução apresentada, calcada na literatura relacionada e em métricas objetivas de eficiência da participação dos revisores e instanciado em quatro grandes projetos disponíveis na plataforma. Com os resultados obtidos, é possível observar que os revisores recomendados aumentam a interação e colaboração entre os envolvidos e aderem significativamente aos objetivos do processo de revisão

Modeling User-Affected Software Properties for Open Source Software Supply Chains

Background: Open Source Software development community relies heavily on users of the software and contributors outside of the core developers to produce top-quality software and provide long-term support. However, the relationship between a software and its contributors in terms of exactly how they are related through dependencies and how the users of a software affect many of its properties are not very well understood. Aim: My research covers a number of aspects related to answering the overarching question of modeling the software properties affected by users and the supply chain structure of software ecosystems, viz. 1) Understanding how software usage affect its perceived quality; 2) Estimating the effects of indirect usage (e.g. dependent packages) on software popularity; 3) Investigating the patch submission and issue creation patterns of external contributors; 4) Examining how the patch acceptance probability is related to the contributors\u27 characteristics. 5) A related topic, the identification of bots that commit code, aimed at improving the accuracy of these and other similar studies was also investigated. Methodology: Most of the Research Questions are addressed by studying the NPM ecosystem, with data from various sources like the World of Code, GHTorrent, and the GiHub API. Different supervised and unsupervised machine learning models, including Regression, Random Forest, Bayesian Networks, and clustering, were used to answer appropriate questions. Results: 1) Software usage affects its perceived quality even after accounting for code complexity measures. 2) The number of dependents and dependencies of a software were observed to be able to predict the change in its popularity with good accuracy. 3) Users interact (contribute issues or patches) primarily with their direct dependencies, and rarely with transitive dependencies. 4) A user\u27s earlier interaction with the repository to which they are contributing a patch, and their familiarity with related topics were important predictors impacting the chance of a pull request getting accepted. 5) Developed BIMAN, a systematic methodology for identifying bots. Conclusion: Different aspects of how users and their characteristics affect different software properties were analyzed, which should lead to a better understanding of the complex interaction between software developers and users/ contributors