Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset

TL;DR

This paper evaluates the effectiveness of automatic repair techniques on the Defects4J dataset of real Java bugs, demonstrating that nearly half can be fixed automatically and analyzing the correctness of generated patches.

Contribution

It provides a baseline for automatic Java bug repair, analyzing real bug fixes and highlighting challenges like under-specified bugs and incorrect patches.

Findings

47 bugs can be automatically repaired with state-of-the-art methods

9 bugs are correctly fixed with test-suite based repair

Average repair time is 14.8 minutes

Abstract

Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J is provided 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 repair on Defects4J. The result of our experiment shows that 47 bugs of the Defects4J dataset can be automatically repaired by state-of- the-art repair. This sets a baseline for future research on automatic repair for Java. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly fixed with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial and incorrect patches still pass the test suite. With respect to practical applicability, it takes in average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All their systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair.