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Related Experiment Video

Updated: Jun 18, 2025

A Cost Effective and Adaptable Scratch Migration Assay

A Cost Effective and Adaptable Scratch Migration Assay

Published on: June 30, 2020

Test-based patch clustering for automatically-generated patches assessment.

Matias Martinez¹, Maria Kechagia², Anjana Perera³

¹Universitat Politècnica de Catalunya, Barcelona, Spain.

Empirical Software Engineering

|July 29, 2024

Summary

This summary is machine-generated.

Automated Program Repair (APR) often overfits patches. Our xTestCluster approach clusters patches by dynamic behavior, reducing the number of patches needing human review by 50% and saving developer time.

Keywords:

Automated program repair Java Patch assessment Patch overfitting Test case generation

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Area of Science:

Software Engineering
Automated Program Repair
Software Testing

Background:

Automated Program Repair (APR) techniques frequently suffer from overfitting, where generated patches appear correct but fail to fix underlying bugs or introduce new defects.
This overfitting necessitates costly human validation of patches, hindering practical adoption of APR tools.
Existing methods lack efficient ways to manage and assess the large number of plausible patches generated by APR tools.

Purpose of the Study:

To introduce xTestCluster, a novel, lightweight, test-based patch clustering approach to reduce the burden of patch validation.
To minimize the number of plausible patches requiring human review, thereby accelerating the identification of correct patches.
To provide developers with enhanced information for patch assessment by leveraging dynamic patch behavior.

Main Methods:

Developed xTestCluster, a test-based clustering approach that analyzes the dynamic behavior of software patches.
Applied xTestCluster after the patch generation phase to cluster patches from multiple APR tools.
Utilized newly generated test cases to group patches based on their failure patterns on these tests.

Main Results:

xTestCluster successfully clustered 902 plausible patches from 21 Java APR tools.
The approach achieved a median reduction of 50% in the number of patches requiring developer review.
Generated new test cases that effectively exposed behavioral differences among generated patches.

Conclusions:

xTestCluster significantly reduces the time and effort required for developers to validate APR-generated patches.
The technique provides valuable new test cases that aid in distinguishing between correct and incorrect patches.
xTestCluster complements existing patch assessment methods, improving the overall efficiency and reliability of Automated Program Repair.