ReviewRanker: A Semi-Supervised Learning Based Approach for Code Review Quality Estimation

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning based system ReviewRanker which is aimed at assigning each code review a confidence score which is expected to resonate with the quality of the review. Our proposed method is trained based on simple and and well defined labels provided by developers. The labeling task requires little to no effort from the developers and has an indirect relation to the end goal (assignment of review confidence score). ReviewRanker is expected to improve industry-wide code review quality inspection through reducing human bias and effort required for such task. The system has the potential of minimizing the back-and-forth cycle existing in the development and review process. Usable code and dataset for this research can be found at: https://github.com/saifarnab/code_revie

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

IMPLEMENTING CONTINUOUS CODE QUALITY FOR CODE QUALITY DEVELOPMENT IN THE SCRUM TEAM

There are challenges and drawbacks that will be faced in implementing a Scrum method. One of the problems is in Code Quality because the team only has a short time in each sprint. This causes the team to not be able to conduct a thorough code review, resulting in the team having to increase their workload and pressure. Code review is necessary to identify defects in the code before it goes into the core of the project. But the cumbersome, time-consuming and labor-intensive nature of code review causes a barrier in adopting code review practices properly. To simplify and speed up code review, the researcher will try to implement Continuous Code Quality (CCQ) with the Scrum process by conducting empirical experiments. By examining the effect on the speed of the process and knowing the point of view of the development team. Based on the experiments conducted, the CCQ automation system helps the development team in speeding up the code review process and maintaining good code quality. The biggest influence on the speed of the process remains in the complexity of the system built in a running sprint. Even with an automated code review system, a manual code review process is still needed to ensure there are no errors that cannot be detected by the automated system

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

What’s bothering developers in code review?

The practice of code review is widely adopted in industry and hasbeen studied to an increasing degree in the research community.However, the developer experience of code review has receivedlimited attention. Here, we report on initial results from a mixed-method exploratory study of the developer experience

Understanding, Analysis, and Handling of Software Architecture Erosion

Architecture erosion occurs when a software system's implemented architecture diverges from the intended architecture over time. Studies show erosion impacts development, maintenance, and evolution since it accumulates imperceptibly. Identifying early symptoms like architectural smells enables managing erosion through refactoring. However, research lacks comprehensive understanding of erosion, unclear which symptoms are most common, and lacks detection methods. This thesis establishes an erosion landscape, investigates symptoms, and proposes identification approaches. A mapping study covers erosion definitions, symptoms, causes, and consequences. Key findings: 1) "Architecture erosion" is the most used term, with four perspectives on definitions and respective symptom types. 2) Technical and non-technical reasons contribute to erosion, negatively impacting quality attributes. Practitioners can advocate addressing erosion to prevent failures. 3) Detection and correction approaches are categorized, with consistency and evolution-based approaches commonly mentioned.An empirical study explores practitioner perspectives through communities, surveys, and interviews. Findings reveal associated practices like code review and tools identify symptoms, while collected measures address erosion during implementation. Studying code review comments analyzes erosion in practice. One study reveals architectural violations, duplicate functionality, and cyclic dependencies are most frequent. Symptoms decreased over time, indicating increased stability. Most were addressed after review. A second study explores violation symptoms in four projects, identifying 10 categories. Refactoring and removing code address most violations, while some are disregarded.Machine learning classifiers using pre-trained word embeddings identify violation symptoms from code reviews. Key findings: 1) SVM with word2vec achieved highest performance. 2) fastText embeddings worked well. 3) 200-dimensional embeddings outperformed 100/300-dimensional. 4) Ensemble classifier improved performance. 5) Practitioners found results valuable, confirming potential.An automated recommendation system identifies qualified reviewers for violations using similarity detection on file paths and comments. Experiments show common methods perform well, outperforming a baseline approach. Sampling techniques impact recommendation performance