FixMiner: Mining Relevant Fix Patterns for Automated Program Repair (1810.01791v2)

Abstract: 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 AST-level 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.

• The paper introduces FixMiner, which employs a three-stage clustering strategy on enhanced edit scripts to extract precise fix patterns.
• It effectively reduces the comparison space and generated correct patches for 26 Defects4J bugs, demonstrating robust scalability.
• The reusable patterns enhance automated patch generation systems, paving the way for more precise and context-aware debugging tools.

Essay on "FixMiner: Mining Relevant Fix Patterns for Automated Program Repair"

The paper "FixMiner: Mining Relevant Fix Patterns for Automated Program Repair" introduces a novel approach for deriving fix patterns from software repositories to facilitate Automated Program Repair (APR). The method involves the extraction and clustering of fine-grained code changes, yielding reusable fix patterns applicable to automated patch generation systems.

Methodology and Contributions

The authors propose FixMiner, which systematically mines fix patterns leveraging an iterative clustering approach based on Rich Edit Script representations of code changes. Key aspects of this methodology include:

1. Rich Edit Script Definition: The approach starts by constructing enhanced edit scripts that capture AST-level context, operations performed, and involved tokens for code changes. Unlike traditional edit scripts, this enriched form provides the necessary context to infer more precise change patterns.
2. Three-Fold Clustering Strategy:
   • The method employs a three-stage clustering process, initially grouping changes based on context (AST nodes), followed by change operations, and finally the specific code tokens involved.
   • At each clustering stage, the method identifies identical tree structures to form clusters, representing recurring patterns across software projects.
3. Evaluation on Large Datasets: The methodology was applied to a substantial dataset, resulting in over 11,000 patches across various open-source projects. This large-scale application demonstrates the robustness and scalability of the approach.

The paper highlights distinctive contributions, such as the development of a reusable pattern mining tool that can directly contribute to patch generation, thereby reducing manual efforts involved in program repair.

Numerical Results and Claims

The evaluation presents FixMiner's effectiveness through several metrics:

• Reduction in Comparison Space: The method reduced the potential comparison space by a significant percentage at each fold, illustrating its efficiency in handling large datasets.
• Relevance of Patterns: The method successfully derived patterns that correspond to known fix templates, validating its alignment with existing APR techniques.
• Patch Generation: The integration of mined patterns into an APR system demonstrated the capability to generate correct patches for 26 bugs from the Defects4J benchmark, showcasing both its correctness and efficiency, as reflected in a high percentage of plausible patches actually being correct.

Implications and Future Directions

The implications of this work are substantial in both theoretical and practical dimensions:

• Practical Deployment: The paper informs the design of future APR systems by emphasizing the importance of context-aware pattern mining. Such systems can become more precise in generating patches that align with developer expectations.
• Theoretical Advancement: By presenting a systematic pattern mining strategy that differs from traditional manual efforts, this work contributes to the evolution of empirical software engineering research methodologies.
• Speculative Trajectories: The paper opens avenues for extending the approach to other programming languages and integrating more sophisticated machine learning techniques into the clustering stages. Moreover, the notion of adapting mined patterns for predictive change recommendations in IDEs represents an exciting frontier.

Conclusion

Overall, "FixMiner: Mining Relevant Fix Patterns for Automated Program Repair" provides substantial advancements in APR research. The proposed methodology not only demonstrates the feasibility of automated fix pattern mining but also integrates effectively with pattern-based repair systems, thereby enhancing the reliability and performance of automated debugging tools. This work lays a promising foundation for leveraging mined patterns in automated systems, advocating a broader adoption of context-aware repair techniques in future software maintenance paradigms.