Mining Fix Patterns for FindBugs Violations

Abstract

Several static analysis tools, such as Splint or FindBugs, have been proposed to the software development community to help detect security vulnerabilities or bad programming practices. However, the adoption of these tools is hindered by their high false positive rates. If the false positive rate is too high, developers may get acclimated to violation reports from these tools, causing concrete and severe bugs being overlooked. Fortunately, some violations are actually addressed and resolved by developers. We claim that those violations that are recurrently fixed are likely to be true positives, and an automated approach can learn to repair similar unseen violations. However, there is lack of a systematic way to investigate the distributions on existing violations and fixed ones in the wild, that can provide insights into prioritizing violations for developers, and an effective way to mine code and fix patterns which can help developers easily understand the reasons of leading violations and how to fix them.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.

TBar: revisiting template-based automated program repair

“…Mutating the integer division expressions to return a float value, by mutating its divisor or divider to make them be of type float. Released by Liu et al [35], it is not implemented in any APR tool yet. where literal1 and literal2 are of the same type literals, but having different values (e.g., literal1 is true, literal2 is false).…”

“…Nevertheless, its precision (ratio of correct vs. plausible patches) is lower than some recent tools such as CapGen and SimFix which employs sophisticated techniques to select fix ingredients. Nonetheless, it is noteworthy that, despite using fix patterns catalogued in the literature, we can fix three bugs (namely Cl-86,L-47,M-11) which had never been fixed by any APR system: M-11 is fixed by a pattern found by a standalone fix pattern mining tool [35] but which was not encoded by any APR system yet. Cl-86 and L-47 are fixed by patterns that were not applied to Defects4J.…”

iFixR: bug report driven program repair

“…A common, and reliable, strategy in automatic program repair is to generate concrete patches based on fix patterns [26] (also referred to as fix templates [51] or program transformation schemas [18]). Several APR systems [14,18,26,31,46,48,49,51,60,73] in the literature implement this strategy by using diverse sets of fix patterns obtained either via manual generation or automatic mining of bug fix datasets. In this work, we consider the pioneer PAR system by Kim et al [26].…”

Watch out for this commit! A study of influential software changes

“…Their approach aims at learning code change templates to be systematically applied to refactor code. Li et al leveraged convolutional neural networks, which is one of the deep neural network techniques, to automatically cluster commons fix patterns from more than 88 000 bug‐fixing changes. Those fix patterns can successfully fix real bugs …”