Mining Fix Patterns for FindBugs Violations

TL;DR

This paper analyzes and leverages fix patterns for FindBugs violations using neural networks and clustering, demonstrating their effectiveness in suggesting fixes that developers accept and apply, and applying these patterns to real bugs.

Contribution

It introduces a neural network-based approach to automatically identify and evaluate fix patterns for violations, aiding automated repair efforts.

Findings

Majority of generated fixes accepted by developers

Patterns applicable to real bugs in Defects4J benchmark

Discrepancies in violation distributions reveal prioritization insights

Abstract

In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in the distributions of violations that are detected and those that are fixed, in terms of occurrences, spread and categories, which can provide insights into prioritizing violations. To automatically identify patterns in violations and their fixes, we propose an approach that utilizes convolutional neural networks to learn features and clustering to regroup similar instances. We then evaluate the usefulness of the identified fix patterns by applying them to unfixed violations. The results show that developers will accept and merge a majority (69/116) of fixes generated from the inferred fix patterns. It is also noteworthy that the yielded patterns are applicable to four real bugs in the Defects4J major benchmark for software testing and automated repair.