Testing Developer Intent With Machine Learning and Static Analysis
Safety-critical software developers need a new way of validating their code. Traditional tests and tools perform well against “hard” criteria like syntax errors and buffer overflows, but miss “soft” criteria such as evaluating developer intent – does the code do what you meant for it to do?
Static sentiment analysis is a new approach with a lot of potential. In theory, it takes advantage of machine learning and small language models to determine how well a piece of code measures up to the rest of the code base. By understanding sentiment, the analysis identifies changes in a developer’s style, misapplied design patterns, violations of coding standards, and more.
Through static sentiment analysis, developers and teams can eventually add a new dimension to their existing validation strategy and reduce the effort necessary to achieve exhaustive functional testing. In the meantime, static analysis is already being used to accelerate embedded software development.
Learn How Static Sentiment Analysis Works
In this eBook, you will learn:
- 3 types of general sentiment analysis and how they can apply to static analysis.
- What static sentiment analysis is, and how it understands safety-critical code.
- The meaning of “entropy distance” as it relates to software validation.
- Use cases and code samples of static sentiment analysis in action.
- How using static analysis with machine learning could level up development for your team.