The passage discusses how safety-critical software developers face growing difficulty validating code as systems become more complex due to increased autonomy, connectivity, usability, and security demands. These factors dramatically expand requirements and testing scenarios, often beyond what teams can realistically manage with traditional methods. Even with strong intentions, developers may be forced to compromise on features or quality to meet deadlines. The paper introduces machine learning, specifically static sentiment analysis, as a way to test developer intent by analyzing code patterns and language. This approach helps align testing with developers’ mental models, improving validation, prioritization, and overall software quality in highly complex environments.

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