Peer Code Review Statistics


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Peer Code Review Statistics 2023: Facts about Peer Code Review outlines the context of what’s happening in the tech world.

LLCBuddy editorial team did hours of research, collected all important statistics on Peer Code Review, and shared those on this page. Our editorial team proofread these to make the data as accurate as possible. We believe you don’t need to check any other resources on the web for the same. You should get everything here only 🙂

Are you planning to form an LLC? Maybe for educational purposes, business research, or personal curiosity, whatever the reason is – it’s always a good idea to gather more information about tech topics like this.

How much of an impact will Peer Code Review Statistics have on your day-to-day? or the day-to-day of your LLC Business? How much does it matter directly or indirectly? You should get answers to all your questions here.

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Top Peer Code Review Statistics 2023

☰ Use “CTRL+F” to quickly find statistics. There are total 21 Peer Code Review Statistics on this page 🙂

Peer Code Review “Latest” Statistics

  • Capers Jones’ ongoing analysis of over 12,000 software development projects showed that the latent defect discovery rate of formal inspection is in the 60-65% range.[1]
  • According to a 2012 research by VDC Research, automated methods for peer code review are now used by 17.6% of the surveyed embedded software developers, and 23.7% plan to utilize them in the next two years.[1]
  • Empirical studies provided evidence that up to 75% of code review defects affect software evolvability/maintainability rather than functionality, making code reviews an excellent tool for software companies with long product or system life cycles.[1]
  • In a specific company, it was noted in 2015 that 75% of code review feedbacks are provided by team authors, despite being marginally less helpful than feedback from other teams.[2]
  • According to Bosu (2015), some projects the proportion of relevant comments decreased by 10%, when they compared changes in 40 files with changes in a single file.[2]
  • 96% of participants think patches with more LOC have a negative impact on duration.[2]
  • 29% of the participants think that patches with more LOC get fewer comments, while 49% disagree and say that patches with more LOC receive more comments.[2]
  • 97.8% of the participants reported medium to very high experience with projects with multiple teams, whereas 93.3% reported medium to very high experience with projects with multiple locations, suggesting that they have experience in DSD.[2]
  • The most intriguing findings, according to McConnell, are that no technique’s modal rate exceeds 75% and that the approaches’ average efficiency is about 40%.[3]
  • According to several estimates severe bugs are 100 times more expensive to fix after shipping than they are to fix before shipping.[3]
  • According to Basili and Selby, code reading found 80% more defects per hour than testing, even when evaluating programmers on code with no comments. Inspections are often a less expensive way to uncover flaws than testing.[3]
  • If aforementioned four categories was simply used, the best bug discovery rate will be 68%.[3]
  • The SmartBear study of Cisco Systems found that “spot checking” 20% to 33% of the code resulted in lower defect density with minimal time expenditure.[4]
  • The SmartBear study of Cisco Systems found that lightweight code review takes less than 20% the time of formal reviews and finds just as many bugs.[4]
  • Despite the fact that the majority of my students were upper level computer science majors, 63% of them admitted at the beginning of the semester that they had never used git.[5]
  • According to a 2017 study of 240 development teams, 90% of the teams employ a review procedure based on changes, and 60% use frequent changes.[6]
  • 96% of the participants believe that duration is negatively affected by patches with a higher number of LOC.[6]
  • A study of an organization at AT&T with more than 200 people reported a 14% increase in productivity and a 90% decrease in defects after the organization introduced reviews.[7]
  • In a software-maintenance organization, 55% of one-line maintenance changes were in error before code reviews were introduced.[7]
  • The Aetna Insurance Company found 82% of the errors in a program by using inspections and was able to decrease its development resources by 20%.[7]
  • 95% of the modifications following the introduction of reviews were accurate the first time, when all changes were taken into account.[7]

Also Read

How Useful is Peer Code Review

One of the key advantages of peer code review is that it can help improve the quality of the code being produced. By having multiple people review the code, errors and bugs that may have been missed by the initial programmer can be identified and corrected early on in the development process. This can save a significant amount of time and effort that would have otherwise been spent trying to find and fix these issues later on.

Additionally, peer code review can also help improve the overall skill level of the team. By having developers evaluate each other’s code, they can learn from one another and pick up new techniques and best practices. This can help create a more cohesive team that is working towards a common goal, rather than individuals working in isolation.

Another benefit of peer code review is that it can help improve communication within the team. By discussing and debating the code with each other, developers can gain a better understanding of the codebase and how it works. This can lead to more efficient collaboration and better problem-solving skills when working on future projects.

Furthermore, peer code review can also help catch potential security vulnerabilities in the code. Having multiple sets of eyes looking at the code can help identify areas where security measures may need to be strengthened. This can help prevent costly data breaches and protect the integrity of the software being developed.

Overall, the benefits of peer code review are numerous and can greatly improve the quality of the code being produced. It can help identify and fix errors early on, improve the skill level of the team, enhance communication, and strengthen security measures. By incorporating peer code review into the development process, teams can work more efficiently and produce higher quality software in the long run.

Reference

  1. wikipedia – https://en.wikipedia.org/wiki/Code_review
  2. springeropen – https://jserd.springeropen.com/articles/10.1186/s40411-018-0058-0
  3. burke – https://kevin.burke.dev/kevin/the-best-ways-to-find-bugs-in-your-code/
  4. smartbear – https://smartbear.com/learn/code-review/best-practices-for-peer-code-review/
  5. teachdatascience – https://teachdatascience.com/countingcommits/
  6. webinarcare – https://webinarcare.com/best-peer-code-review-software/peer-code-review-statistics/
  7. agilesparks – https://www.agilesparks.com/peer-code-review-benefits-and-statistics/

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