Static code analysis is a form of testing that happens without executing code, naturally taking place in the early stages of the software development process. The ultimate goal is to identify problems in the early stages of software development. The variety of problems identified with different static code analysis tools range from security issues to different problems in runtime, such as GPU/CPU initialization errors. However, traditional static code analysis tools often provide generic analysis results that may not be relevant or personalized to the specific codebase being analyzed. Artificial intelligence (AI) has the potential to help.
AI can revolutionize static code analysis by providing more context-sensitive and personalized analysis results. In this blog, our aim is to explore how AI can be used to better personalize static code analysis and the benefits it can bring to software developers and development teams.
Context-sensitive analysis
AI can be used to personalize static code analysis through context-sensitive analysis. AI has the capability to take into account the context of the code being analyzed, such as the intended use case for the program, the development environment, as well as the potential test cases for the program. By taking context into consideration, AI algorithms can provide more relevant analysis results for specific use cases, help developers to identify more complex errors, and provide working solutions for the fixes.
Feedback-based learning
Unlike static code analysis tools that use traditional rules-based algorithms, AI can learn from developers and their feedback on the accuracy of analysis results and recommended problem resolutions. By incorporating feedback into the analysis and resolution generation models, AI can continuously improve the accuracy of the analysis and provide more personalized results over time. This approach can help software engineers and teams to achieve better accuracy of static code analysis results.
Feature selection
Another way AI can help to personalize static code analysis is through feature selection. AI algorithms can identify relevant features from the source code that can improve the accuracy of the analysis. By analyzing large datasets of source code, AI algorithms can identify patterns and features that are indicative of specific issues, such as performance bottlenecks and runtime environment-related errors. These features can then be used to train more accurate and comprehensive models used for static code analysis. Additionally, with internal datasets, development teams can also train AI models to detect problems that have previously occurred in their teams’ commit history.
Integration into development workflows
Just like rules-based algorithms that are currently being used in code review tools and continuous integration pipelines, AI can be integrated in a similar way. Even if a new technology is superior to existing solutions, adoption can still be jeopardized if the new tech does not integrate with existing development workflows. At Metabob, we are launching a VS Code extension, which will allow developers to identify and fix complex problems quickly and early in their existing development environment.
Conclusion
Personalized static code analysis provides several benefits for software developers and development teams. By using AI for context-sensitive analysis, feedback-based learning, and feature selection, personalized analysis can help to identify and fix complex problems early in the development process, leading to increased efficiency and more reliable software.