提升效率：11项同行代码审查最佳实践

Peer

CodeReview

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本文档探讨了"最佳实践：同行代码审查"，这是一个在软件开发过程中不可或缺的环节，它通过同事之间的代码审核来发现错误、增强协作和保持代码可维护性。尽管代码审查的理念被广泛接受，但实践中可能存在效率低下和效果不佳的问题。例如，强制执行的审查会议可能会占用宝贵的时间，导致团队士气下降，而过于宽松的流程又可能使代码质量难以得到保障。文档强调了以下11个经过科学验证和实践证明有效的轻量级同行代码审查最佳实践，旨在提高代码质量并优化开发人员的工作效率： 1. **限制每次审查代码行数**：建议每次审查不超过200-400行代码。这样做可以保持聚焦，避免审查者在大量代码中迷失，同时鼓励他们更深入地检查每个部分。 2. **采用分段式审查**：将大项目分解成小部分，逐个模块或功能进行审查，确保每一部分都能得到细致的关注。 3. **明确审查目标**：在开始审查前，明确期望达成的目标，如识别潜在问题、优化设计或增强代码一致性。 4. **使用自动化工具**：结合静态代码分析工具，辅助人工审查，减少重复劳动，提升效率。 5. **及时反馈**：提供即时反馈，避免问题积压，促进快速修复。 6. **鼓励团队建设**：创建一个支持性和非批判性的环境，鼓励开放讨论和学习。 7. **定期培训和教育**：确保所有参与者具备足够的技能和知识，了解最佳实践。 8. **灵活的审查周期**：根据项目的复杂性和团队规模调整审查频率，既不过于频繁干扰开发节奏，也不忽视关键部分。 9. **记录和跟踪**：保存审查历史记录，以便跟踪改进，并作为未来的参考。 10. **激励机制**：实施奖励制度，认可高质量的代码审查，激发团队的积极性。 11. **持续改进**：根据实际效果调整和优化审查流程，使其适应不断变化的需求。遵循这些最佳实践，可以使团队在保持代码质量的同时，避免浪费开发资源，提升整体开发效率和团队士气。SmartBear Software的丰富经验和科学研究支持了这些方法的有效性，是实现高效同行代码审查的重要参考。

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Best Practices for Peer Code Review

Smart Bear Software |

The Latest Code Review Tips & More:

blog.smartbear.co

| 978.236.7860 | www.SmartBear.com

Best Practices for Peer Code Review

Introduction

It’s common sense that peer code review – in which software developers

review each other’s code before releasing software to QA – identifies bugs,

encourages collaboration, and keeps code more maintainable.

But it’s also clear that some code review techniques are inefficient and

ineffective. The meetings often mandated by the review process take time

and kill excitement. Strict process can stifle productivity, but lax process

means no one knows whether reviews are effective or even happening. And

the social ramifications of personal critique can ruin morale.

This whitepaper describes 11 best practices for efficient, lightweight peer

code review that have been proven to be effective by scientific study and by

Smart Bear's extensive field experience. Use these techniques to ensure your

code reviews improve your code – without wasting your developers' time.

1. Review fewer than 200-400 lines of code at a time.

The Cisco code review study (see sidebar for details) showed that for optimal

effectiveness, developers should review fewer than 200-400 lines of code

(LOC) at a time. Beyond that, the ability to find defects diminishes. At this rate,

with the review spread over no more than 60-90 minutes, you should get a

70-90% yield; in other words, if 10 defects existed, you’d find 7-9 of them.

The graph on the following page, which plots defect density against the

number of lines of code under review, supports this rule. Defect density is the

number of defects per 1000 lines of code. As the number of lines of code

under review grows beyond 300, defect density drops off considerably.

In this case, defect density is a measure of “review effectiveness.” If two

reviewers review the same code and one finds more bugs, we would consider

her more effective. Figure 1 shows how, as we put more code in front of a

reviewer, her effectiveness at finding defects drops. This result makes sense –

the reviewer probably doesn’t have a lot of time to spend on the review, so

inevitably she won't do as good a job on each file.

The World’s Largest Code Review Study

at Cisco Systems®

Our team at Smart Bear Software has spent

years researching existing code review studies

and collecting “lessons learned” from more

than 6000 programmers at 100+ companies.

Clearly people find bugs when they review

code – but the reviews often take too long to

be practical! We used the information gleaned

through years of experience to create the

concept of lightweight code review. Using

lightweight code review techniques,

developers can review code in 1/5

the time

needed for full “formal” code reviews. We also

developed a theory for best practices to

employ for optimal review efficiency and value,

which are outlined in this white paper.

To test our conclusions about code review in

general and lightweight review in particular,

we conducted the world’s largest-ever

published study on code review,

encompassing 2500 code reviews, 50

programmers, and 3.2 million lines of code at

Cisco Systems®. For ten months, the study

tracked the MeetingPlace® product team,

distributed across Bangalore, Budapest, and

San José.

At the start of the study, we set up some rules

for the group:

 All code had to be reviewed before it was

checked into the team’s Perforce version

control software.

 Smart Bear’s Code Collaborator code

review software tool would be used to

expedite, organize, and facilitate all code

review.

 In-person meetings for code review were

not allowed.

 The review process would be enforced by

tools.

 Metrics would be automatically collected

by Code Collaborator, which provides

review-level and summary-level reporting.

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