Code Review in 2024: Best Practices & Latest Advances

Code review automation

Code review is a systematic examination of source code by developers other than the author to identify bugs, security flaws, and deviations from standards early in the development process. As software continues permeating every facet of life, effective code review is more crucial than ever for releasing high-quality, secure applications.

This article will explore the latest advances in code review and provide research-backed best practices to optimize review processes in 2024 and beyond.

The Growing Role of Code Review

Code review serves several vital purposes:

  • Detecting defects early. Studies show code review catches between 60-90% of defects, higher than most testing methods [1]. Reviews find issues when they are cheapest to fix.

  • Enforcing standards and best practices. Reviews ensure code adheres to the team‘s conventions and style guide. This maintains consistency and readability across the codebase.

  • Knowledge sharing. Reviewers gain familiarity with parts of the codebase they don‘t normally work on. The author also learns from reviewer feedback.

  • Security. Manual review augments automated scanning to uncover vulnerabilities like XSS, injections, etc.

  • Technical debt management. By continuously improving code quality, reviews reduce the accumulation of technical debt. This makes code easier to maintain and extend over time.

The high ROI of review has led to widespread adoption. Stack Overflow‘s 2022 survey of over 70,000 developers found:

  • 84% work on teams practicing code review [2]
  • 70% consider reviews very/extremely valuable for quality [2]

As software eats the world, every industry now relies on secure, reliable code. Code review provides a safety net for catching issues before they impact customers. That‘s why modern development teams dedicate significant resources to perfecting their review process.

Current State of Code Review

Approaches to code review vary widely across teams. Stack Overflow found:

  • 37% perform informal peer discussion
  • 35% use pull/merge requests on Git servers like GitHub
  • 15% use pair programming
  • 13% use automated code review tools [2]

Additionally, DevOps and continuous delivery have influenced current code review practices:

  • Reviews occur on small, incremental changes rather than big batches.
  • Automated analysis integrated into CI/CD pipelines provides rapid feedback.
  • Reviewer productivity and velocity have become bigger concerns.

Despite growth, many teams still struggle with issues like inconsistent reviewing, sluggish performance, and lack of metrics [3]. Adoption of more disciplined practices can help optimize review effectiveness and efficiency.

Latest Advances in Code Review

Code review is evolving and benefiting from innovations in tools and processes:

Automated Code Analysis

Powerful automated analysis tools like SonarQube, CodeScene, and Codacy integrate with Git and CI/CD workflows. They scan commits, pull requests, and other code changes for:

  • Bugs and anti-patterns
  • Security vulnerabilities
  • Performance issues
  • Dead code and duplicates
  • Non-compliant coding practices

These tools apply static analysis, pattern matching, and other techniques to catch issues traditionally requiring human judgment [4].

Automated analysis provides consistent, 24/7 objective feedback. It reduces repetitive manual work so reviewers can focus on higher-level concerns.

According to a study by Fannie Mae, integrating automated analysis into review workflows increased defect detection 17% [5]. Tools improve reviewer productivity and lead to cleaner, more secure code.

Code review automation

Automated code analysis complements human review [Source: Perforce]

Focused, Incremental Reviews

On fast-paced Agile teams, reviewing every line of code is impractical. Instead, reviews center on the changes between commits or pull requests.

This incremental approach allows for more frequent, bite-sized reviews. Benefits include:

  • Issues spotted immediately after introduction instead of waiting for full review
  • Faster feedback loops to improve work in progress
  • Smaller review batches that are more digestible

In a survey of over 1,300 developers, 89% agreed that reviewing incremental diffs leads to higher quality code [6].

Tools like GitHub, GitLab, and Bitbucket facilitate this workflow by highlighting incremental diffs and allowing threaded review conversations on pull requests.

Advanced Code Review Metrics

Quantitative review analytics provide insight into the efficiency and efficacy of processes. Key metrics include:

  • Defect escape rate
  • Defect resolution rate
  • Review coverage
  • Review participation
  • Average time per review
  • Accept/reject rates

Free tools like Review Assistant generate basic GitHub review metrics. More advanced analysis requires code review management platforms like Upsource or CodeStream.

Regular analysis of metrics exposes inefficiencies and training gaps. It also helps balance review thoroughness with development velocity, adjusting policies to optimize for both.

Artificial Intelligence Assisted Reviews

AI can support human reviewers in several ways:

  • Review prioritization – ML models rank pull requests by risk, focusing reviewers on the most critical code first.

  • Reviewer recommendation – An AI assistant suggests the most appropriate reviewers for a given code change based on past activity, experience, and current workload.

  • Code summarization – Key information is extracted from lengthy review conversations using NLP techniques.

  • Automatic remediation – Bots instantly fix trivial style violations and lint issues rather than wait for a developer to patch them manually.

AI doesn‘t replace human judgment but augments capacity for more efficient, consistent, and comprehensive review coverage.

Code Review Best Practices

Based on research and industry experience, here are 10 best practices for optimizing code review processes:

1. Establish a Documented Process

Have a documented process addressing:

  • What parts of the codebase will be reviewed
  • When peer reviews are required
  • Who is responsible for reviewing each change
  • What aspects the reviewer should evaluate
  • How review findings get addressed
  • Metrics to measure effectiveness

Documenting expectations avoids confusion and ensures consistency.

2. Promote Constructive Feedback Culture

Create an environment where developers view feedback as helpful, not critical. Guide reviewers to provide thoughtful suggestions focused on improving the code rather than attacking the author.

Research shows negative interactions lead to developers disengaging and ignoring review comments [7]. Maintaining a constructive culture results in better code.

3. Keep Review Sets Small and Focused

Studies demonstrate reviewers‘ attention and effectiveness decrease with larger review batches [8].

Scope reviews around specific commits or pull requests that represent single logical changes. Reject sprawling multi-feature branches.

4. Use Checklists to Guide Reviewers

Well-designed checklists raise awareness of what to examine for. Include checks validating correctness, security, performance, compliance with standards, etc.

Checklists result in more defects found and a 35% increase in review speed according to an experiment by ABB Inc. [9].

5. Rotate Reviewers Across Projects

Don‘t over utilize individual reviewers. Rotate participation to spread knowledge across the team. Studies show developer learning improves code quality [10].

Diverse perspectives from different reviewers enhance detection of issues.

6. Integrate Reviews into Existing Workflows

Minimize disruption by embedding reviews into common workflows. Take advantage of review tools provided by Git servers, IDEs, and CI/CD pipelines.

Frictionless integration ensures reviews happen at natural points during development.

7. Invest in Reviewer Training

Ensure reviewers have expertise in technical best practices, design principles, security, UX, and other quality attributes. Have them research and share frequent code issues.

An experiment by Cisco showed training helps reviewers catch 24% more defects [11]. Building reviewer skills boosts review effectiveness.

8. Regularly Assess Metrics

Analyze review metrics frequently to expose inefficiencies. Look for improvements like reducing repetitive comments or increasing first-pass acceptance rates.

Tune policies to balance velocity and quality based on data. Consistent metric-driven refinement increases ROI.

9. Automate Where Possible

Automation improves consistency and reduces human burden. Automate checklist items, linting, static analysis, duplicate detection, and reformatting.

Empower reviewers to focus on higher reasoning like logic, performance, and design quality.

10. Continuously Evolve

Solicit regular feedback via surveys and interviews. Learn about pain points and experiment with new techniques.

Efficient, high-value review requires continuous incremental innovation.

What‘s Next for Code Review?

As technology and methodologies advance, code review will continue adapting:

  • Smarter AI assistance – More advanced AI will triage reviews and enhance human capabilities.
  • Immersive analytics – VR/AR could enable more intuitive review data visualization and debugging.
  • IDE integration – Reviews will integrate tighter with developer workflows through IDE plugins.
  • Tamper-proofing – Blockchain could provide immutable evidence of reviewed/approved code changes.
  • Expanded scope – Reviews will encompass code‘s full lifecycle including PRs, test code, and production monitoring.
  • Holistic quality analysis – Review scope will expand beyond functional correctness to attributes like ethics, accessibility, performance, etc.

The increased reliance on software means that code review must continuously evolve as part of delivering secure, resilient applications.

Conclusion

Code review is now a critical practice for engineering teams across industries. Evolving tools and integrating reviews into modern development workflows helps tame the complexity of modern software.

Adopting streamlined, metrics-driven review processes – augmented by automation – leads to enormous quality and productivity gains. Teams should optimize reviews based on proven practices and continuously refine them.

With software now powering human experiences and even life-critical systems, comprehensive code review provides a safety net for catching issues before impacting users. Investments made in maturing review capabilities will pay dividends in the form of faster, higher quality software delivery.

How do you perform code reviews today, and how will your processes need to evolve moving forward? Please share your experiences and questions in the comments below!