Learning Likely Invariants to Explain Why a Program Fails
This addresses the difficulty in debugging for programmers by providing explanations for bugs, though it is incremental as it builds on existing invariant learning and bug localization techniques.
The paper tackles the problem of helping programmers understand why a program fails by automatically generating likely invariants that are violated in failed tests, showing that these invariants often correlate with actual bug fixes in real-world applications.
Debugging is difficult. Recent studies show that automatic bug localization techniques have limited usefulness. One of the reasons is that programmers typically have to understand why the program fails before fixing it. In this work, we aim to help programmers understand a bug by automatically generating likely invariants which are violated in the failed tests. Given a program with an initial assertion and at least one test case failing the assertion, we first generate random test cases, identify potential bug locations through bug localization, and then generate program state mutation based on active learning techniques to identify a predicate "explaining" the cause of the bug. The predicate is a classifier for the passed test cases and failed test cases. Our main contribution is the application of invariant learning for bug explanation, as well as a novel approach to overcome the problem of lack of test cases in practice. We apply our method to real-world bugs and show the generated invariants are often correlated to the actual bug fixes.