FixMiner: Mining relevant fix patterns for automated program repair

Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurrence of bugs across projects, the associated similar patches can be leveraged to extract generic fix actions. While
the literature includes various approaches leveraging similarity among patches
to guide program repair, these approaches often do not yield fix patterns that
are tractable and reusable as actionable input to APR systems.
In this paper, we propose a systematic and automated approach to mining
relevant and actionable fix patterns based on an iterative clustering strategy
applied to atomic changes within patches. The goal of FixMiner is thus to
infer separate and reusable fix patterns that can be leveraged in other patch
generation systems. Our technique, FixMiner, leverages Rich Edit Script
which is a specialized tree structure of the edit scripts that captures the ASTlevel context of the code changes. FixMiner uses different tree representations
of Rich Edit Scripts for each round of clustering to identify similar changes.
These are abstract syntax trees, edit actions trees, and code context trees.
We have evaluated FixMiner on thousands of software patches collected
from open source projects. Preliminary results show that we are able to mine
accurate patterns, efficiently exploiting change information in Rich Edit Scripts.
We further integrated the mined patterns to an automated program repair
prototype, PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J benchmark. Beyond this quantitative performance, we show that
the mined fix patterns are sufficiently relevant to produce patches with a high
probability of correctness: 81% of PARFixMiner’s generated plausible patches are
correct.