Code Review Tools Research Articles

Automated Code Review Tool

As software development undergoes continual evolution, the assurance of code quality remains paramount in the development lifecycle. This research paper introduces an innovative Automated Code Review Tool engineered to augment and streamline the code review process. The tool capitalizes on advanced static code analysis, machine learning algorithms, and industry best practices to automate the detection of code quality issues, security vulnerabilities, and compliance with coding standards. The Automated Code Review Tool addresses the inherent challenges of manual code reviews, including human error, time constraints, and inconsistencies. Through automation, developers can expedite the identification and resolution of potential issues, thereby enhancing overall code quality and software reliability. Key features of the tool encompass intelligent pattern recognition, real-time feedback, and customizable rule sets, enabling development teams to tailor the tool to their project-specific requirements. Leveraging machine learning algorithms facilitates the tool's continuous learning from past reviews, thereby adapting to evolving coding practices and emerging security threats. This paper presents the architectural framework and design principles underpinning the Automated Code Review Tool, emphasizing its capacity to foster collaboration among development teams, reduce time-to-market, and ultimately contribute to the development of more robust and secure software. A comprehensive evaluation of the tool's efficacy across diverse development environments is conducted, offering insights into the practical implications of embracing automated solutions in real-world contexts. Keywords: Automated Code Review Tool, Code quality, Static code analysis, Machine learning algorithms, Coding standards, Manual code reviews, Human error, Time constraints, etc.

Concerns identified in code review: A fine-grained, faceted classification

Code review is a valuable software process that helps software practitioners to identify a variety of defects in code. Even though many code review tools and static analysis tools used to improve the efficiency of the process exist, code review is still costly.

Automatic patch linkage detection in code review using textual content and file location features

Contemporary code review tools are a popular choice for software quality assurance. Using these tools, reviewers are able to post a linkage between two patches during a review discussion. Large development teams that use a review-then-commit model risk being unaware of these linkages. Our objective is to first explore how patch linkage impacts the review process. We then propose and evaluate models that detect patch linkage based on realistic time intervals. First, we carry out an exploratory study on three open source projects to conduct linkage impact analysis using 942 manually classified linkages. Second, we propose two techniques using textual and file location similarity to build detection models and evaluate their performance. The study provides evidence of latency in the linkage notification. We show that a patch with the Alternative Solution linkage (i.e., patches that implement similar functionality) undergoes a quicker review and avoids additional revisions after the team has been notified, compared to other linkage types. Our detection model experiments show promising recall rates for the Alternative Solution linkage (from 32% to 95%), but precision has room for improvement. Patch linkage detection is promising, with likely improvements if the practice of posting linkages becomes more prevalent. From our implications, this paper lays the groundwork for future research on how to increase patch linkage awareness to facilitate efficient reviews.

Exploitation of DevOps concepts for the ASDEX Upgrade DCS

The ASDEX Upgrade Discharge Control System (DCS) is designed to be distributed and highly modular. By nature it is a complex system that requires expert knowledge to develop, deploy and operate. This paper discusses the application of DevOps concepts to DCS and its impact on the operation of ASDEX Upgrade. The aim is to provide stable and undisturbed operation of ASDEX Upgrade, whilst having a low hurdle to include new capabilities into DCS to extend the operational scope. To ensure both goals are met DevOps concepts have been introduced to DCS. For the code development a review process was added using the code review tool Gerrit. Each change is only accepted into the code repository once the review and automated testing have been completed. The automated testing and packaging of DCS are conducted using the automation server Jenkins. DCS is not directly installed on the host computers, but is operated using the containerization framework Docker. The containers allow easy deployment of DCS onto new systems as well as the quick change of DCS versions on the systems. A new or updated component can easily be tested by deploying the appropriate container. In case of failure the previous state is restored by redeploying the container in the previous version. DCS components can be classified into two types, the real time and the non real time parts. Non real time parts are the services that are required to manage the configuration of the system and the setup of the real-time interaction between participants. The new deployment method with Docker is used for both non real time as well as real time services and is operational on ASDEX Upgrade since the 2019/2020 campaign. No impact of the determinism of the real time components have been observed when operated within a Docker container.

Does Reviewer Recommendation Help Developers?

Selecting reviewers for code changes is a critical step for an efficient code review process. Recent studies propose automated reviewer recommendation algorithms to support developers in this task. However, the evaluation of recommendation algorithms, when done apart from their target systems and users (i.e., code review tools and change authors), leaves out important aspects: perception of recommendations, influence of recommendations on human choices, and their effect on user experience. This study is the first to evaluate a reviewer recommender in vivo . We compare historical reviewers and recommendations for over 21,000 code reviews performed with a deployed recommender in a company environment and set out to measure the influence of recommendations on users’ choices, along with other performance metrics. Having found no evidence of influence, we turn to the users of the recommender. Through interviews and a survey we find that, though perceived as relevant, reviewer recommendations rarely provide additional value for the respondents. We confirm this finding with a larger study at another company. The confirmation of this finding brings up a case for more user-centric approaches to designing and evaluating the recommenders. Finally, we investigate information needs of developers during reviewer selection and discuss promising directions for the next generation of reviewer recommendation tools. Preprint: https://doi.org/10.5281/zenodo.1404814 .

Tools, processes and factors influencing of code review

Code review is the most effective quality assurance strategy in software development where reviewers aim to identify defects and improve the quality of source code of both commercial and open-source software. Ultimately, the main purpose of code review activities is to produce better software products. Review comments are the building blocks of code review. There are many approaches to conduct reviews and analysis source code such as pair programming, informal inspections, and formal inspections. Reviewers are responsible for providing comments and suggestions to improve the quality of the proposed source code modifications. This work aims to succinctly describe code review process, giving a framework of the tools and factors influencing code review to aid reviewers and authors in the code review stages and choose the suitable code review tool.

Information Needs in Contemporary Code Review

Contemporary code review is a widespread practice used by software engineers to maintain high software quality and share project knowledge. However, conducting proper code review takes time and developers often have limited time for review. In this paper, we aim at investigating the information that reviewers need to conduct a proper code review, to better understand this process and how research and tool support can make developers become more effective and efficient reviewers. Previous work has provided evidence that a successful code review process is one in which reviewers and authors actively participate and collaborate. In these cases, the threads of discussions that are saved by code review tools are a precious source of information that can be later exploited for research and practice. In this paper, we focus on this source of information as a way to gather reliable data on the aforementioned reviewers' needs. We manually analyze 900 code review comments from three large open-source projects and organize them in categories by means of a card sort. Our results highlight the presence of seven high-level information needs, such as knowing the uses of methods and variables declared/modified in the code under review. Based on these results we suggest ways in which future code review tools can better support collaboration and the reviewing task.

Gitana: A software project inspector

The development of software projects entails significant implementation and collaboration activities, typically supported by tools such as issue trackers, code review tools and Version Control Systems. However, these tools only provide a partial view of the project and often lack of advanced querying mechanisms, thus hampering the analysis of the status of the project and endangering the decision making process on the best way to drive the development process. We present Gitana , a software project inspector able to import the activity of the different support tools into a single relational database, thus providing a central point to perform all kinds of cross-cutting analysis on the software project data. Tool website: https://github.com/SOM-Research/gitana . • Conceptual schema modeling the activities around a software project. • Data integration of various types of project data (e.g., VCSs, forums). • Import and incremental update processes to fetch project data. • Full implementation of the approach available as an open source tool on GitHub.

Analysis of a Code Review Tool Evolution

In this study, the authors intend to assess the improvements of Gerrit. The central concern is “Does Rietveld evolve into Gerrit as the developers intended?” To answer this question, the authors first compare qualitative features of two code review tools. The authors then conducted an interview with a developer of Gerrit and obtained the developer's original intention of improvements in Gerrit. By analyzing mined data from code review logs, the authors try to explain the effects of improvements quantitatively. The result of analysis showed the authors that the improvements of Gerrit that the developer is expected are not observed explicitly.

Focus on software and data acquisition

Focus on software and data acquisition

Automated Code Review Tools for Security

Automated Code Review Tools for Security

Automated Code Review Tools for Security

Computer security has experienced important fundamental changes over the past decade. The most promising developments in security involve arming software developers and architects with the knowledge and tools they need to build more secure software. Among the many security tools available to software practitioners, static analysis tools for automated code review are the most effective. The paper presents how they work and why all developers should use them.