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.

A bug report is a technical document describing bugs that have occurred in the software. Finding the source code files to resolve a reported bug is a laborious task. To automate this process, information retrieval-based bug localization (IRBL) techniques have been proposed. These techniques assess the relevance between the bug report and source files, providing developers with a ranked list of source files. They rely heavily on text tokens, making it essential to remove noisy tokens from the input tokens. To address the problem of prevalent noisy tokens deteriorating IRBL performance, we define impactful noisy words as misguiding terms and investigate their prevalence and impact. We employed a deep learning model combined with explainable AI techniques to detect misguiding terms, leveraging their semantic embedding capabilities. We conducted extensive experiments on 24 open-source software projects and three IRBL models. By removing misguiding terms, the mean reciprocal rank of bug localization improved by 19%, 17%, and 27% for three models on average and up to 120%. The proposed approach effectively distinguishes between beneficial terms and noise, leading to superior IRBL performance compared to the existing noise detection approaches, with consistent improvements observed across 24 projects.

Template-based automatic program repair (T-APR) techniques depend on the quality of bug-fixing templates. For such templates to be of sufficient quality for T-APR techniques to succeed, they must satisfy three criteria: applicability, fixability, and efficiency. Existing template mining approaches select templates based only on the first criteria, and are thus suboptimal in their performance. This study proposes a multi-objective optimization-based bug-fixing template mining method for T-APR in which we estimate template quality based on nine code abstraction tasks and three objective functions. Our method determines the optimal code abstraction strategy (i.e., the optimal combination of abstraction tasks) which maximizes the values of three objective functions and generates a final set of bug-fixing templates by clustering template candidates to which the optimal abstraction strategy is applied. Our preliminary experiment demonstrated that our optimized strategy can improve templates’ applicability and efficiency by 7% and 146% over the existing mining technique, respectively. We therefore conclude that the multi-objective optimization-based template mining technique effectively finds high-quality bug-fixing templates.