Automatic repair of real bugs in java: a large scale experiment on the defects4j dataset
Defects4J is a large, peer reviewed, structured dataset of real world Java bugs. Each bug in Defects4J comes with a test suite and at least one failing test case that triggers the bug. In this paper, we report on an experiment to explore the effectiveness of automatic test suite based repair on Defects4J. The result of our experiment shows that the considered state of the art repair methods can generate patches for 47 out of 224 bugs. However, those patches are only test suite adequate, which means that they pass the test suite and may potentially be incorrect beyond the test suite satisfaction correctness criterion. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly repaired with test suite based repair. This analysis shows that test suite based repair suffers from under specified bugs, for which trivial or incorrect patches still pass the test suite. With respect to practical applicability, it takes on average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All the repair systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair.
Empirical Software Engineering