Aravind Putrevu
May 28, 2024
|6 min read
Code reviews are critical checkpoints when developing software. Traditionally, they’ve been done by human developers, who pore over lines of code, hunting for defects and bugs. The process ensures adherence to coding standards, promotes best practices, and levels up domain knowledge across teams. However, it’s not quick or flawless. According to SmartBear's study of a Cisco Systems programming team, a review of 200-400 lines of code should take about 60 to 90 minutes to achieve a 70-90% defect discovery rate. Still, it’s an incredibly valuable part of the process. Software developers almost universally consider code reviews to be important, according to a 2022 Global DevSecOps Survey.
Code reviews have been a staple of software development since the 1970s. Although the tools and languages have changed, the goal hasn’t. When a software developer makes a code change, they want other pairs of eyes on it to make sure the change has the effect that they want without any unintentional side effects. In this manner, code reviews help mitigate risk of breaking changes. It is also a cultural practice that trains new engineers and builds team cohesion.
They’re also one of the only practices left that are entirely manual. Engineers must actually take the time and energy to look at the changes being made and analyze them from different perspectives. They can’t afford to take risks on security, standards compliance, or reliability issues, so it’s resource intensive and requires skills developed over time with practice.
Even after years of practice, code reviews are difficult, slow, and inefficient. Roughly 45% of developers cite 'Lack of Time' as the primary obstacle to reviewing code, while 34% attribute it to the 'Pressure to Ship'. Everyone involved in software development, from the CEO to the project manager, has to put faith in the process in hopes that code changes don’t introduce any new problems. This is why they're time-consuming, prone to lack of oversight, and can sometimes turn into a subjective critique session rather than a constructive feedback loop.
Here are a few pain points:
Time Constraints: Developers are often pressed for time, juggling multiple tasks and deadlines. Comprehensive code reviews either compete with this valuable time and cause delayed timelines, or code review quality may suffer as shortcuts are taken in order to complete the work.
Cognitive Bias and Variability: No two developers think alike. This subjectivity can lead to inconsistent reviews, where the focus and thoroughness vary wildly based on the reviewer's preferences, expertise, and mental state.
Error Prone: Subtle bugs and dependency issues can be missed, especially in complex or large codebases. This can lead to vulnerabilities, and technical debt released into the wild.
Knowledge Silos: Technical knowledge tends to get siloed, especially in large teams. This silo effect can prevent a thorough understanding of the codebase, reducing the effectiveness of code reviews.
Where there are humans doing work, there are imperfections and risks of blind spots. To make matters worse, developers that spend more than a day a week reviewing code don’t have any correlation to improvements in perceived code quality. There is also no correlation in terms of more time shipping new features (as opposed to fixing bugs or paying back tech debt).
Several AI-powered tools and platforms are making waves in the software development world, such as GitHub Copilot, CodeGuru by Amazon, and DeepCode by Snyk. These tools leverage machine learning and advanced algorithms to automate processes, suggest optimizations, and even generate code snippets to address identified issues.
Their adoption underscores the potential and demand for AI in enhancing code review processes. Having these tools at the fingertips of developers helps produce more resilient and sophisticated code at the point of authorship.
With AI, developers can theoretically eliminate most (if not all) of the pain points they experience. AI is fast, readily available, and AI doesn’t have to deal with organizational politics.
The most effective use of AI in software development marries its strengths with the irreplaceable intuition, creativity, and experience of human developers. This synergistic approach leverages AI for what it does best—speed, consistency, and automation—while relying on humans for strategic decision-making and nuanced understanding that AI (currently) cannot replicate.
AI can now be used to address the challenges of traditionally human-centric process of code reviews. For example, AI can scan entire code repositories and workflow systems to understand the context in which the codebase runs. This is a major advantage for today’s modern AI code review systems, and one that pre-genAI tools lacked.
Here are a few other ways AI can help:
Automating Tedious Tasks: Code reviews often involve repetitive tasks, such as checking coding standards, documentation, and boilerplate code compliance. AI can automate these aspects of code reviews, freeing up human reviewers to focus on more complex and subjective aspects of the code that require human judgment and experience. This not only speeds up the review process but also reduces the cognitive load on human reviewers.
Identify Defects Faster: AI can tirelessly scan through thousands of lines of code in minutes, identifying logical flaws and even complex security vulnerabilities with precision that rivals or surpasses the human eye. This allows human reviewers to focus on higher-level architectural and design considerations instead of getting bogged down in finding needle-in-the-haystack type errors.
Consistent and Objective: AI doesn't have a bad day or get mad at a management decision (yet). It doesn't have biases towards certain coding styles or practices unless they're part of its training data. By applying uniform standards across the board, AI ensures that every line of code is reviewed with the same level of scrutiny, bringing a level of consistency that is hard to achieve in human-only reviews.
Instantaneous Feedback: One of the most significant advantages of AI in code reviews is the ability to analyze and provide feedback in real-time to developers. This immediacy helps identify issues within the context of discussion of the code review - rather than in later development cycle stages, thus reducing the cost and effort of fixing bugs down the line.
Learning and Adaptation: Advanced AI systems can learn from past reviews, developer corrections, and evolving coding practices. This learning capability means that AI assistants can continuously improve, offering more relevant and accurate feedback over time.
Knowledge Sharing and Augmentation: By integrating insights from across the codebase and external sources, AI can act as a knowledge-sharing platform, suggesting best practices, offering coding tips, and even providing examples from similar projects. This feature helps break down knowledge silos and fosters a culture of continuous learning and improvement.
The integration of AI technology into the code review process is not just an incremental improvement, but a transformative change. Current AI technology can play the role of an assistant to a software development team, accelerating and offloading tedious manual analysis and bug finding.
Future advancements will see AI evolve into the role of a collaborator, capable of more complex reasoning, offering design suggestions, best practices, and even predicting or simulating the impact of code changes on software functionality and performance. AI can provide deeper insights into code quality, offer personalized feedback, and play a key role in installing a culture of learning and improvement within development teams.
The journey towards fully realizing the potential of AI in code reviews requires mindful integration and a continued partnership between human developers and their AI counterparts. The future of software development is bright, and AI is undoubtedly a leading light on this horizon.
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