Automatic Program Repair (APR) techniques have shown the potential of reducing debugging costs while improving software quality by generating patches for fixing bugs automatically. However, they often generate many overfitting patches which pass only a specific test-suite but do not fix the bugs correctly. This paper proposes MIPI, a novel approach to reducing the number of overfitting patches generated in the APR. We leverage recent advances in deep learning to exploit the similarity between the patched method’s name (which often encloses the developer’s intention about the code) and the semantic meaning of the method’s body (which represents the actual implemented behavior) for identifying and removing overfitting patches generated by APR tools. Experiments with a large dataset of patches for QuixBugs and Defects4J programs show the promise of our approach. Specifically, in a total of 1,191 patches generated by 23 existing APR tools, MIPI successfully filters out 254 (32%) of the total 797 overfitting patches with a precision of 90% while preserving 93% of the correct patches. MIPI is more precise and less damaging to the APR than existing heuristic patch assessment techniques, achieving a higher recall than automated testing-based techniques that do not have access to the test oracle. In addition, MIPI is highly complementary to existing automated patch assessment techniques.

Information retrieval-based bug localization (IRBL) aims at finding buggy files using a bug report as a query. IRBL performance is highly dependent on the query quality. To improve the query quality for IRBL, automatic query expansion (AQE) method has been proposed for identifying query-related terms from the first-retrieved source files. This approach inevitably depends on two determinant of post- retrieval results, the retrieval model and the initial query quality. We propose a novel word embedding-based AQE technique, WEQE, to avoid the heavy dependency of the current AQE approach. Word embedding model enables to fetch terms semantically related to a query by representing words in a vector space. Our method embeds the words from both the global corpus and project-specific-corpus. The initial query is extended by adding words semantically similar to it based on vector representations from our embedding model. We validated the effectiveness of WEQE by using 4,583 bug reports from seven projects, four IRBL models, and two em-bedding models. Our large-scale experimental results show that WEQE can improve the average precision for bug localization for at least 42% of all queries. Our expanded queries on the best IRBL model achieve a 6% higher mean average precision for bug localization than the initial query.

Recently, Automatic Program Repair (APR) has shown a high capability of repairing software bugs automatically. In general, most of the APR techniques require test suites to validate automatically generated patches. However, the test suites used for patch validation might contain thousands of test cases. Running these whole test suites to validate every program variant makes the validation process not only time-consuming but also expensive. To mitigate this issue and to enhance the patch validation in APR, we introduce (1) MPTPS (Modification Point-aware Test Prioritization and Sampling), which iteratively records test execution. Based on the failed test information, it performs test prioritization, then sampling to reduce the test execution time by moving forward the test cases that are most likely to fail in the test suite; and (2) a new fitness function that refines the existing one to improve repair efficiency. We implemented our MPPEngine approach in the Astor workspace by extending jGenProg. And the experiments on the Defects4j benchmark against jGenProg show that, on average, jGenProg consumes 79.27 s to validate one program variant, where MPPEngine takes only 33.70 s for results in 57.50% of validation time reduction. Also, MPPEngine outperforms jGenProg by finding patches for six more bugs than jGenProg.