Code Reviewer Recommendations as a Multi-Objective Problem: Balancing Expertise, Availability and Collaborations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156106/1/ASE_J_Multi_Objective_Code_Reviewer_assignment_FV__Copy_ (1).pdfSEL

Improving Code Reviewer Recommendation: Accuracy, Latency, Workload, and Bystanders

Code review ensures that a peer engineer manually examines the code before it is integrated and released into production. At Meta, we develop a wide range of software at scale, from social networking to software development infrastructure, such as calendar and meeting tools to continuous integration. We are constantly improving our code review system, and in this work we describe a series of experiments that were conducted across 10's of thousands of engineers and 100's of thousands of reviews. We build upon the recommender that has been in production since 2018, RevRecV1. We found that reviewers were being assigned based on prior authorship of files. We reviewed the literature for successful features and experimented with them with RevRecV2 in production. The most important feature in our new model was the familiarity of the author and reviewer, we saw an overall improvement in accuracy of 14 percentage points. Prior research has shown that reviewer workload is skewed. To balance workload, we divide the reviewer score from RevRecV2 by each candidate reviewers workload. We experimented with multiple types of workload to develop RevRecWL. We find that reranking candidate reviewers by workload often leads to a reviewers with lower workload being selected by authors. The bystander effect can occur when a team of reviewers is assigned the review. We mitigate the bystander effect by randomly assigning one of the recommended reviewers. Having an individual who is responsible for the review, reduces the time take for reviews by -11%

Mitigating Turnover with Code Review Recommendation: Balancing Expertise, Workload, and Knowledge Distribution

Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turnover tend to disrupt and increase workloads for developers. In this work, we suggest that through code review recommendation we can distribute knowledge and mitigate turnover with minimal impact on the development process. We evaluate review recommenders in the context of ensuring expertise during review, Expertise, reducing the review workload of the core team, CoreWorkload, and reducing the Files at Risk to turnover, FaR. We find that prior work that assigns reviewers based on file ownership concentrates knowledge on a small group of core developers increasing risk of knowledge loss from turnover by up to 65%. We propose learning and retention aware review recommenders that when combined are effective at reducing the risk of turnover by -29% but they unacceptably reduce the overall expertise during reviews by -26%. We develop the Sophia recommender that suggest experts when none of the files under review are hoarded by developers but distributes knowledge when files are at risk. In this way, we are able to simultaneously increase expertise during review with a ΔExpertise of 6%, with a negligible impact on workload of ΔCoreWorkload of 0.09%, and reduce the files at risk by ΔFaR -28%. Sophia is integrated into GitHub pull requests allowing developers to select an appropriate expert or “learner” based on the context of the review. We release the Sophia bot as well as the code and data for replication purposes

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

Software restructuring: understanding longitudinal architectural changes and refactoring

The complexity of software systems increases as the systems evolve. As the degradation of the system's structure accumulates, maintenance effort and defect-proneness tend to increase. In addition, developers often opt to employ sub-optimal solutions in order to achieve short-time goals, in a phenomenon that has been recently called technical debt. In this context, software restructuring serves as a way to alleviate and/or prevent structural degradation. Restructuring of software is usually performed in either higher or lower levels of granularity, where the first indicates broader changes in the system's structural architecture and the latter indicates refactorings performed to fewer and localised code elements. Although tools to assist architectural changes and refactoring are available, there is still no evidence these approaches are widely adopted by practitioners. Hence, an understanding of how developers perform architectural changes and refactoring in their daily basis and in the context of the software development processes they adopt is necessary. Current software development is iterative and incremental with short cycles of development and release. Thus, tools and processes that enable this development model, such as continuous integration and code review, are widespread among software engineering practitioners. Hence, this thesis investigates how developers perform longitudinal and incremental architectural changes and refactoring during code review through a wide range of empirical studies that consider different moments of the development lifecycle, different approaches, different automated tools and different analysis mechanisms. Finally, the observations and conclusions drawn from these empirical investigations extend the existing knowledge on how developers restructure software systems, in a way that future studies can leverage this knowledge to propose new tools and approaches that better fit developers' working routines and development processes

Contribution Barriers to Open Source Projects

Contribution barriers are properties of Free/Libre and Open Source Software (FLOSS) projects that may prevent newcomers from contributing. Contribution barriers can be seen as forces that oppose the motivations of newcomers. While there is extensive research on the motivation of FLOSS developers, little is known about contribution barriers. However, a steady influx of new developers is connected to the success of a FLOSS project. The first part of this thesis adds two surveys to the existing research that target contribution barriers and motivations of newcomers. The first exploratory survey provides the indications to formulate research hypotheses for the second main survey with 117 responses from newcomers in the two FLOSS projects Mozilla and GNOME. The results lead to an assessment of the importance of the identified contribution barriers and to a new model of the joining process that allows the identification of subgroups of newcomers affected by specific contribution barriers. The second part of the thesis uses the pattern concept to externalize knowledge about techniques lowering contribution barriers. This includes a complete categorization of the existing work on FLOSS patterns and the first empirical evaluation of these FLOSS patterns and their relationships. The thesis contains six FLOSS patterns that lower specific important contribution barriers identified in the surveys. Wikis are web-based systems that allow its users to modify the wiki's contents. They found on wiki principles with which they minimize contribution barriers. The last part of the thesis explores whether a wiki, whose content is usually natural text, can also be used for software development. Such a Wiki Development Environment (WikiDE) must fulfill the requirements of both an Integrated Development Environment (IDE) and a wiki. The simultaneous compliance of both sets of requirements imposes special challenges. The thesis describes an adapted contribution process supported by an architecture concept that solves these challenges. Two components of a WikiDE are discussed in detail. Each of them helps to lower a contribution barrier. A Proof of Concept (PoC) realization demonstrates the feasibility of the concept