Multi-objective Optimization-based Bug-fixing Template Mining for Automated Program Repair

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.