Code review explained

Code review (sometimes referred to as peer review) is a software quality assurance activity in which one or more people examine the source code of a computer program, either after implementation or during the development process. The persons performing the checking, excluding the author, are called "reviewers". At least one reviewer must not be the code's author.[1] [2]

Code review differs from related software quality assurance techniques like static code analysis,self-checks, testing, and pair programming. Static analysis relies primarily on automated tools, self-checks involve only the author, testing requires code execution, and pair programming is performed continuously during development rather than as a separate step.[1]

Goal

Although direct discovery of quality problems is often the main goal,[3] code reviews are usually performed to reach a combination of goals:[4]

Review types

Several variations of code review processes exist, with additional types specified in IEEE 1028.[5]

Inspection (formal)

Historically, the first code review process that was studied and described in detail was called "Inspection" by its inventor, Michael Fagan.[6] Fagan inspection is a formal process that involves a careful and detailed execution with multiple participants and phases. In formal code reviews, software developers attend a series of meetings to examine code line by line, often using printed copies. Research has shown formal inspections to be extremely thorough and highly effective at identifying defects.[6]

Regular change-based code review (Walk-throughs)

In recent years, many industry teams have adopted a lighter-weight review process in which the scope of each review is on the changes to the codebase performed in a ticket, user story, commit, or some other unit of work.[7] Furthermore, there are rules or conventions that integrate the review task into the development workflow through conventions like mandatory review of all tickets, commonly as part of a pull request, instead of explicitly planning each review. Such a process is called "regular, change-based code review".[1] There are many variations of this basic process. A 2017 survey of 240 development teams found that 90% of teams using code review followed a change-based process, with 60% specifically using regular change-based review.[3] Major software corporations including such as Microsoft,[8] Google,[9] and Facebook follow a change-based code review process.

Efficiency and effectiveness

Ongoing research by Capers Jones analyzing over 12,000 software development projects found formal inspections had a latent defect discovery rate of 60-65%, while informal inspections detected fewer than 50% of defects. The latent defect discovery rate for most forms of testing is about 30%.[10] [11] A code review case study published in the book Best Kept Secrets of Peer Code Review contradicted the Capers Jones study, finding that lightweight reviews can uncover as many bugs as formal reviews while being more efficient in terms of cost and money[12]

Studies indicate that up to 75% of code review comments affect software evolvability and maintainability rather than functionality,[13] [14] [15] [16] suggesting that code reviews are an excellent tool for software companies with long product or system life cycles.[17] Therefore, less than 15% of issues discussed in code reviews relate directly to bugs.[18]

Guidelines

Research indicates review effectiveness correlates with review speed. Optimal code review rates range from 200 to 400 lines of code per hour.[19] [20] [21] [22] Inspecting and reviewing more than a few hundred lines of code per hour for critical software (such as safety critical embedded software) may be too fast to find errors.[23]

Supporting tools

Static code analysis software assist reviewers by automatically checking source code for known vulnerabilities and defect patterns, particularly for large chunks of code.[24] 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% planning to use them within two years.[25]

See also

External links

Notes and References

  1. Book: Baum . Tobias . Liskin . Olga . Niklas . Kai . Schneider . Kurt . 2016 IEEE International Conference on Software Quality, Reliability and Security (QRS). 74–85 . 2016 . 10.1109/QRS.2016.19. 978-1-5090-4127-5 . A Faceted Classification Scheme for Change-Based Industrial Code Review Processes . 9569007 .
  2. Book: Kolawa, Adam . Huizinga, Dorota . Automated Defect Prevention: Best Practices in Software Management . 2007 . Wiley-IEEE Computer Society Press . 260 . 978-0-470-04212-0 .
  3. Book: Baum . Tobias . Product-Focused Software Process Improvement . Leßmann . Hendrik . Schneider . Kurt . 2017 . 978-3-319-69925-7 . Lecture Notes in Computer Science . 10611 . 111–127 . The Choice of Code Review Process: A Survey on the State of the Practice . 10.1007/978-3-319-69926-4_9.
  4. Book: Baum . Tobias . Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2016 . Liskin . Olga . Niklas . Kai . Schneider . Kurt . 2016 . 9781450342186 . 85–96 . Factors Influencing Code Review Processes in Industry . 10.1145/2950290.2950323 . 15467294.
  5. Book: IEEE Standard for Software Reviews and Audits. IEEE STD 1028-2008 . August 2008 . 1–53 . 10.1109/ieeestd.2008.4601584. 978-0-7381-5768-9 .
  6. Fagan . Michael . Design and code inspections to reduce errors in program development . IBM Systems Journal . 1976 . 15 . 3 . 182–211. 10.1147/sj.153.0182 .
  7. Book: Rigby . Peter . Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering . Bird . Christian . 2013 . 9781450322379 . 202–212 . Convergent contemporary software peer review practices . 10.1.1.641.1046 . 10.1145/2491411.2491444 . 11163811.
  8. MacLeod . Laura . Greiler . Michaela . Storey . Margaret-Anne. Bird . Christian. Czerwonka . Jacek. Code Reviewing in the Trenches: Challenges and Best Practices . IEEE Software . 35 . 4 . 34 . 2017 . 10.1109/MS.2017.265100500 . 49651487 . 2020-11-28 .
  9. Book: Sadowski. Caitlin . Söderberg. Emma. Church. Luke. Sipko. Michal. Baachelli. Alberto. Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice . Modern code review: A case study at google . 181–190 . 2018 . 10.1145/3183519.3183525. 9781450356596 . 49217999 . free.
  10. Web site: Measuring Defect Potentials and Defect Removal Efficiency . Capers . Jones . Crosstalk, The Journal of Defense Software Engineering . June 2008 . 2010-10-05 . https://web.archive.org/web/20120806092322/http://www.crosstalkonline.org/storage/issue-archives/2008/200806/200806-0-Issue.pdf . 2012-08-06 . dead .
  11. Embedded Software: Facts, Figures, and Future . Computer . 42 . 4 . 42–52 . Capers . Jones . Christof . Ebert . April 2009 . 10.1109/MC.2009.118 . 14008049 .
  12. Book: Jason Cohen . Best Kept Secrets of Peer Code Review (Modern Approach. Practical Advice.) . Smart Bear Inc. . 2006 . 978-1-59916-067-2 . registration .
  13. Book: 10.1109/ICSE.2015.131 . 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering . 2020-11-28 . 2 . 27–28 . 2015 . Czerwonka . Jacek . Greiler . Michaela . Tilford . Jack . Code Reviews do Not Find Bugs. How the Current Code Review Best Practice Slows Us Down . 978-1-4799-1934-5 . 29074469 .
  14. 10.1109/TSE.2008.71 . 10.1.1.188.5757 . What Types of Defects Are Really Discovered in Code Reviews? . 2012-03-21. IEEE Transactions on Software Engineering . 35 . 3 . 430–448 . 2009 . Mantyla . M.V. . Lassenius . C. . 17570489 .
  15. Web site: Expectations, outcomes, and challenges of modern code review . A. Bacchelli. C. Bird. Proceedings of the 35th IEEE/ACM International Conference On Software Engineering (ICSE 2013). May 2013. 2015-09-02.
  16. Web site: Modern code reviews in open-source projects: which problems do they fix? . M. Beller. A. Bacchelli. A. Zaidman. E. Juergens. Proceedings of the 11th Working Conference on Mining Software Repositories (MSR 2014). May 2014. 2015-09-02.
  17. Web site: Does the Modern Code Inspection Have Value? . Harvey . Siy . Lawrence . Votta . 2004-12-01 . 2015-02-17 . unomaha.edu . dead . https://web.archive.org/web/20150428192217/http://csalpha.ist.unomaha.edu/~hsiy/research/sm.pdf . 2015-04-28 .
  18. Web site: Characteristics of Useful Code Reviews: An Empirical Study at Microsoft . Amiangshu. Bosu . Michaela . Greiler . Chris . Bird . 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories . May 2015. 2020-11-28.
  19. Kemerer. C.F.. Paulk. M.C.. The Impact of Design and Code Reviews on Software Quality: An Empirical Study Based on PSP Data. IEEE Transactions on Software Engineering. 2009-04-17. 35. 4. 534–550. 10.1109/TSE.2009.27. 11059/14085 . 14432409. free.
  20. Web site: Code Review Metrics. Open Web Application Security Project. 9 October 2015. https://web.archive.org/web/20151009202719/https://www.owasp.org/index.php/Code_Review_Metrics. 2015-10-09.
  21. Web site: Best Practices for Peer Code Review. Smart Bear. Smart Bear Software. 9 October 2015. https://web.archive.org/web/20151009202810/http://smartbear.com/all-resources/articles/best-practices-for-peer-code-review/. 2015-10-09.
  22. Bisant. David B.. A Two-Person Inspection Method to Improve Programming Productivity. IEEE Transactions on Software Engineering. October 1989. 15. 10. 1294–1304. 10.1109/TSE.1989.559782. 14921429. 9 October 2015.
  23. Web site: A Guide to Code Inspections . Jack . Ganssle . The Ganssle Group . February 2010 . 2010-10-05.
  24. Book: 10.1109/ICSE.2013.6606642 . 978-1-4673-3076-3 . Reducing human effort and improving quality in peer code reviews using automatic static analysis and reviewer recommendation . 2013 35th International Conference on Software Engineering (ICSE) . 931–940 . 2013 . Balachandran . Vipin . 15823436 .
  25. Web site: Automated Defect Prevention for Embedded Software Quality . VDC Research. VDC Research. 2012-02-01 . 2012-04-10.

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