Peer Code Review Statistics 2022 - Everything You Need to Know


Are you looking to add Peer Code Review to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Peer Code Review statistics of 2022.

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Best Peer Code Review Statistics

☰ Use “CTRL+F” to quickly find statistics. There are total 50 Peer Code Review Statistics on this page πŸ™‚

Peer Code Review Software Statistics

  • β€œβ€¦ software testing alone has limited effectiveness – the average defect detection rate is only 25 percent for unit testing, 35 percent for function testing, and 45 percent for integration testing. [0]
  • Case studies of review results have been impressive In a softwaremaintenance organization, 55 percent of one line maintenance changes were in error before code reviews were introduced. [0]
  • a changebased code review process.[10]Efficiency and effectiveness of reviews[edit]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. [1]
  • Empirical studies provided evidence that up to 75% of code review defects affect software evolvability/maintainability rather than functionality,[14][15][4] making code reviews an excellent tool for software companies with long product or system life cycles. [1]
  • A 2012 study by VDC Research reports that 17.6% of the embedded software engineers surveyed currently use automated tools to support peer code review and 23.7% expect to use them within 2 years. [1]
  • [3] 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. [1]
  • Research for the State of Software Development in 2018 report from Coding Sans found 67.66% of software developers use peer review to ensure code quality. [2]
  • Moreover, that number is even higher among top performing software developers; 73.53% of the most successful developers use this method. [2]
  • The flipside of the Coding Sans finding that the majority of software developers use peer code review, is the statistic that 12.54% of software developers are using β€œno specific way.”. [2]

Peer Code Review Latest Statistics

  • In contrast, the average effectiveness of design and code inspections are 55 and 60 percent. [0]
  • After reviews were introduced, only 2 percent of the changes were in error. [0]
  • When all changes were considered, 95 percent were correct the first time after reviews were introduced. [0]
  • Before reviews were introduced, under 20 percent were correct the first time. [0]
  • Reviews cut the errors by over 80 percent. [0]
  • The Aetna Insurance Company found 82 percent of the errors in a program by using inspections and was able to decrease its development resources by 20 percent. [0]
  • It was delivered early and had only about 1 percent of the errors that would normally be expected. [0]
  • A study of an organization at AT&T with more than 200 people reported a 14 percent increase in productivity and a 90 percent decrease in defects after the organization introduced reviews. [0]
  • A survey among 240 development teams from 2017 found that 90% of the teams use a review process that is based on changes , and 60% use regular, change. [1]
  • A survey among 240 development teams from 2017 found that 90% of the teams use a review process that is based on changes , and 60% use regular, change. [1]
  • For informal inspection, the figure is less than 50%. [1]
  • The latent defect discovery rate for most forms of testing is about 30%.[11]A code review case study published in the book [12]Best. [1]
  • [16]This also means that less than 15% of the issues discussed in code reviews are related to bugs. [1]
  • This also means that less than 15% of the issues discussed in code reviews are related to bugs.[17][18]Guidelines[edit]The effectiveness of code review was found to depend on the speed of reviewing. [1]
  • The latent defect discovery rate for most forms of testing is about 30%.[11]A code review case study published in the book [12] The types of defects detected in code reviews have also been studied. [1]
  • His study2 showed that when developers read the code to identify defects before the meeting, actually having the meeting only increased the total defects found by 4%. [3]
  • Conduct reviews for 100% of the code for all projects. [3]
  • Reviews now take about 20 minutes – a savings of about 75% of code review time. [3]
  • This means not pursuing endeavors such as β€œStarting today, 100% of all code written must be peer reviewed.”. [3]
  • Inspection rates of less than 300 LOC/hour result in the best defect detection, and rates under 500 are still good, but expect to miss a significant percentage of defects if LOC are reviewed faster than that. [4]
  • Limiting reviews to 60 minutes, reviewers found between 70 percent and 90 percent of errors. [2]
  • At the beginning of the semester, 63% of my students said they had never used git before , and by the end of the semester everyone could commit, push, pull, manage merge conflicts, and more. [5]
  • observed, in a particular organization, that 75% of the code review feedbacks come from members of the author’s team, but are slightly less useful than those from other teams. [6]
  • Their conclusions are files that received a slow initial feedback in the past will also likely receive slow feedback in the future;. [6]
  • From them, 50 voluntarily provided a response within a week (response rate of 62.5%). [6]
  • We can see that 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. [6]
  • Participation consists of the fraction of invited reviewers that are active, ranging from 0% to 100%. [6]
  • Nevertheless, we do not expect that participation is 100%, given that there are developers that are invited automatically and may not be relevant reviewers anymore. [6]
  • We split our data into 13 groups according to this factor, listed in the first column of Table 4. [6]
  • 2Influence of the Patch Size over Duration , Participation (%), Comment Density (comments per 100 ) and Comment Density by Reviewer (comments per 100 per active reviewer). [6]
  • However, the relationship between patch size and duration is nonlinear; a linear leastsquares regression model produced an r squared value of 2.9%, which means that a linear model has low explanatory power for the data. [6]
  • showed that for some projects the proportion of relevant comments decreased by 10%, when they compared changes in 40 files with changes in a single file, while Baysal et al. [6]
  • Fig. 3Table 5 Outcomes by number of teamsAccording to our results, the duration of code review is considerably higher if more teams are involved, with higher mean and also standard deviation values. [6]
  • Results show that the average revew duraton n the same locaton s 32% greater than n the same team;. [6]
  • the average duration with two locations is 38% greater than with two teams; and. [6]
  • the average density of review comments with two locations is 24% higher than with two teams. [6]
  • 6 show that 96% of the participants believe that duration is negatively affected by patches with a higher number of LOC. [6]
  • Similar results are associated with participation, as 80% of participants stated that it becomes worse. [6]
  • However, only 29% of the participants believe that the number of comments decreases for patches with more LOC, while 49% have a different opinion, indicating that more comments are provided. [6]
  • 2 Influence of the Patch Size over Duration , Participation (%), Comment Density (comments per 100 ) and Comment Density by Reviewer (comments per 100 per active reviewer). [6]
  • Get 50% OFF for switching to Review Assistant from competing tools!. [7]

I know you want to use Peer Code Review Software, thus we made this list of best Peer Code Review Software. We also wrote about how to learn Peer Code Review Software and how to install Peer Code Review Software. Recently we wrote how to uninstall Peer Code Review Software for newbie users. Don’t forgot to check latest Peer Code Review statistics of 2022.

Reference


  1. agilesparks – https://www.agilesparks.com/blog/peer-code-review-benefits-and-statistics/.
  2. wikipedia – https://en.wikipedia.org/wiki/Code_review.
  3. devprojournal – https://www.devprojournal.com/software-development-trends/how-to-conduct-better-peer-code-review/.
  4. smartbear – https://smartbear.com/learn/code-review/agile-code-review-process/.
  5. smartbear – https://smartbear.com/learn/code-review/what-is-code-review/.
  6. teachdatascience – https://teachdatascience.com/countingcommits/.
  7. springeropen – https://jserd.springeropen.com/articles/10.1186/s40411-018-0058-0.
  8. devart – https://www.devart.com/review-assistant/.

In Conclusion

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