Employing lightweight, tool-based code review of code changes (aka modern code review) has become the norm for a wide variety of open-source and industrial systems. In this paper, we make an exploratory investigation of modern code review at Google. Google introduced code review early on and evolved it over the years; our study sheds light on why Google introduced this practice and analyzes its current status, after the process has been refined through decades of code changes and millions of code reviews. By means of 12 interviews, a survey with 44 respondents, and the analysis of review logs for 9 million reviewed changes, we investigate motivations behind code review at Google, current practices, and developers' satisfaction and challenges.

https://www.zora.uzh.ch/id/eprint/152982/1/icse2018seip.pdf

Modern code review: a case study at google

Many open source and commercial developers practice contemporary code review, a lightweight, informal, tool-based code review process. To better understand this process and its benefits, we gathered information about code review practices via surveys of open source software developers and developers from Microsoft. The results of our analysis suggest that developers spend approximately 10-15 percent of their time in code reviews, with the amount of effort increasing with experience. Developers consider code review important, stating that in addition to finding defects, code reviews offer other benefits, including knowledge sharing, community building, and maintaining code quality. The quality of the code submitted for review helps reviewers form impressions about their teammates, which can influence future collaborations. We found a large amount of similarity between the Microsoft and OSS respondents. One interesting difference is that while OSS respondents view code review as an important method of impression formation, Microsoft respondents found knowledge dissemination to be more important. Finally, we found little difference between distributed and co-located Microsoft teams. Our findings identify the following key areas that warrant focused research: 1) exploring the non-technical benefits of code reviews, 2) helping developers in articulating review comments, and 3) assisting reviewers’ program comprehension during code reviews.

Process Aspects and Social Dynamics of Contemporary Code Review: Insights from Open Source Development and Industrial Practice at Microsoft

Code reviews are popular in both industrial and open source projects. The benefits of code reviews are widely recognized and include better code quality and lower likelihood of introducing bugs. However, since code review is a manual activity it comes at the cost of spending developers' time on reviewing their teammates' code. Our goal is to make the first step towards partially automating the code review process, thus, possibly reducing the manual costs associated with it. We focus on both the contributor and the reviewer sides of the process, by training two different Deep Learning architectures. The first one learns code changes performed by developers during real code review activities, thus providing the contributor with a revised version of her code implementing code transformations usually recommended during code review before the code is even submitted for review. The second one automatically provides the reviewer commenting on a submitted code with the revised code implementing her comments expressed in natural language. The empirical evaluation of the two models shows that, on the contributor side, the trained model succeeds in replicating the code transformations applied during code reviews in up to 16% of cases. On the reviewer side, the model can correctly implement a comment provided in natural language in up to 31% of cases. While these results are encouraging, more research is needed to make these models usable by developers.

pdf/towards-automating-code-review-activities-4zhi72fg6i.pdf

Towards Automating Code Review Activities

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.

/pdf/information-needs-in-contemporary-code-review-g9aumec3b4.pdf

Information Needs in Contemporary Code Review

Although peer code review is widely adopted in both commercial and open source development, existing studies suggest that such code reviews often contain a significant amount of non-useful review comments. Unfortunately, to date, no tools or techniques exist that can provide automatic support in improving those non-useful comments. In this paper, we first report a comparative study between useful and non-useful review comments where we contrast between them using their textual characteristics, and reviewers' experience. Then, based on the findings from the study, we develop RevHelper, a prediction model that can help the developers improve their code review comments through automatic prediction of their usefulness during review submission. Comparative study using 1,116 review comments suggested that useful comments share more vocabulary with the changed code, contain salient items like relevant code elements, and their reviewers are generally more experienced. Experiments using 1,482 review comments report that our model can predict comment usefulness with 66% prediction accuracy which is promising. Comparison with three variants of a baseline model using a case study validates our empirical findings and demonstrates the potential of our model.

Predicting usefulness of code review comments using textual features and developer experience

A snapshots data structure provides compilers and other tools with snapshots of a buffer at different points in time. Snapshot heads identify trees within a directed acyclic graph (DAG) of nodes. Leaf nodes of a given tree collectively hold data elements representing information that was resident in the buffer at a particular point in time regardless of subsequent editing of the buffer. During buffer editing operations a new tree is created in the DAG with leaf nodes holding data elements that match one-to-one a subsequence of data elements held by leaf nodes of an existing tree.

Buffer snapshots from unmodifiable data piece tables

Because of its many uses and benefits, code reviews are a standard part of the modern software engineering workflow. Since they require involvement of people, code reviewing is often the longest part of the code integration activities. Using experience gained at Microsoft and with support of data, we posit (1) that code reviews often do not find functionality issues that should block a code submission; (2) that effective code reviews should be performed by people with specific set of skills; and (3) that the social aspect of code reviews cannot be ignored. We find that we need to be more sophisticated with our guidelines for the code review workflow. We show how our findings from code reviewing practice influence our code review tools at Microsoft. Finally, we assert that, due to its costs, code reviewing practice is a topic deserving to be better understood, systematized and applied to software engineering workflow with more precision than the best practice currently prescribes.

/pdf/code-reviews-do-not-find-bugs-how-the-current-code-review-25e6o0zpu7.pdf

Code reviews do not find bugs: how the current code review best practice slows us down

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jack Tilford

Author Tools

Chat about Author

Papers

Buffer snapshots from unmodifiable data piece tables

Code reviews do not find bugs: how the current code review best practice slows us down