Automated Classification of Overfitting Patches With Statically Extracted Code Features

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