Finding latent code errors via machine learning over program executions

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Abstract
This paper proposes a technique for identifying program properties that indicate errors. The technique generates machine learning models of program properties known to result from errors, and applies these models to program properties of user-written code to classify and rank properties that may lead the user to errors. Given a set of properties produced by the program analysis, the technique selects a subset of properties that are most likely to reveal an error. An implementation, the fault invariant classifier, demonstrates the efficacy of the technique. The implementation uses dynamic invariant detection to generate program properties. It uses support vector machine and decision tree learning tools to classify those properties. In our experimental evaluation, the technique increases the relevance (the concentration of fault-revealing properties) by a factor of 50 on average for the C programs, and 4.8 for the Java programs. Preliminary experience suggests that most of the fault-revealing properties do lead a programmer to an error.

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