Intro
Code reviews are a cornerstone of modern software development, especially for critical embedded systems. However, many teams find that their reviews have become time-consuming, tedious, and ultimately ineffective "bug-finding expeditions".
When your team spends its time manually checking for style issues or simple coding defects, you lose the opportunity to discuss what really matters: software design and architecture.
The Problem with Manual Reviews
Human reviewers are fallible. Simple issues that should be detected during manual reviews frequently slip into production code, sometimes with major security consequences like Man-in-the-Middle (MitM) vulnerabilities.
Furthermore, manual review is slow. The optimal rate for reviewing code is roughly 100 lines of code (LOC) per hour. At that pace, reviewing a standard 10 KLOC module would take an engineer 100 hours—a timeline that is often impossible in fast-paced development cycles.
How to Make Code Reviews More Efficient
To reclaim your review time, you need to automate the "tedious" parts of the process. Static analysis tools, such as Polyspace, can augment your workflow by providing an evidence-based foundation for peer reviews.
1. Automate Compliance
Instead of arguing over style and standards, use static analysis to automatically ensure compliance with coding standards like MISRA, JSF, or your own internal rules. This allows the review team to focus on whether the code meets design requirements.
2. Identify Vulnerabilities Early
Manual reviews often miss complex issues like concurrency errors or dynamic memory problems. Tools like Polyspace can detect wide categories of software defects and security vulnerabilities during the coding process. Addressing these before the formal peer review saves time and ensures a higher-quality codebase.
3. Gain Deep Insight into Run-Time Behavior
An effective review requires understanding how code will actually run. Static analysis provides detailed information on:
Control and data flow.
Possible variable ranges.
Function call graphs and data dictionaries.
This technical depth helps your team make better design decisions and can even help you prove the absence of errors, allowing you to skip the review of "good" code entirely.
Top comments (0)