Automatic program repair (APR) aims to reduce the cost of manually fixing software defects. However, APR suffers from generating a multitude of overfitting patches, those patches that fail to correctly repair the defect beyond making the tests pass. This paper presents a novel overfitting patch detection system called ODS to assess the correctness of APR patches. ODS first statically compares a patched program and a buggy program in order to extract code features at the abstract syntax tree (AST) level, for the single programming language Java. Then, ODS uses supervised learning with the captured code features and patch correctness labels to automatically learn a probabilistic model. The learned ODS model can then finally be applied to classify new and unseen program repair patches. We conduct a large-scale experiment to evaluate the effectiveness of ODS on patch correctness classification based on 10,302 patches from Defects4J, Bugs.jar and Bears benchmarks. The empirical evaluation shows that ODS is able to correctly classify 71.9 percent of program repair patches from 26 projects, which improves the state-of-the-art. ODS is applicable in practice and can be employed as a post-processing procedure to classify the patches generated by different APR systems.

Automatic program repair papers tend to repeatedly use the same benchmarks. This poses a threat to the external validity of the findings of the program repair research community. In this paper, we perform an empirical study of automatic repair on a benchmark of bugs called QuixBugs, which has been little studied. In this paper, (1) We report on the characteristics of QuixBugs; (2) We study the effectiveness of 10 program repair tools on it; (3) We apply three patch correctness assessment techniques to comprehensively study the presence of overfitting patches in QuixBugs. Our key results are: (1) 16/40 buggy programs in QuixBugs can be repaired with at least a test suite adequate patch; (2) A total of 338 plausible patches are generated on the QuixBugs by the considered tools, and 53.3% of them are overfitting patches according to our manual assessment; (3) The three automated patch correctness assessment techniques, RGT_Evosuite, RGT_{InputSampling} and GT_Invariants, achieve an accuracy of 98.2%, 80.8% and 58.3% in overfitting detection, respectively. To our knowledge, this is the largest empirical study of automatic repair on QuixBugs, combining both quantitative and qualitative insights. All our empirical results are publicly available on GitHub in order to facilitate future research on automatic program repair.