Towards efficient and effective automatic program repair

Abstract

Automatic Program Repair (APR) has recently been an emerging research area, addressing an important challenge in software engi- neering. APR techniques, if effective and efficient , can greatly help software debugging and maintenance. Recently proposed APR tech- niques can be generally classified into two families, namely search- based and semantics-based APR methods. To produce repairs, search- based APR techniques generate huge populations of possible re- pairs, i.e., search space, and lazily search for the best one among the search space. Semantics-based APR techniques utilize con- straint solving and program synthesis to make search space more tractable, and find those repairs that conform to semantics con- straints extracted via symbolic execution. Despite recent advances in APR, search-based APR still suffers from search space explo- sion problem, while the semantics-based APR could be hindered by limited capability of constraint solving and program synthesis. Furthermore, both APR families may be subject to overfitting, in which generated repairs do not generalize to other test sets. This thesis works towards enhancing both effectiveness and ef- ficiency in order for APR to be practically adopted in foreseeable future. To achieve this goal, other than using test cases as the pri- mary criteria for traversing the search space, we designed a new feature used for a new search-based APR technique to effectively traverse the search space, wherein bug fix history is used to evaluate the quality of repair candidates. We also developed a deductive- reasoning-based repair technique that combines search-based and semantics-based approaches to enhance the repair capability, while ensuring the soundness of generated repairs. We also leveraged machine-learning techniques to build a predictive model that pre- dicts whether an APR technique is effective in fixing particular bugs. In the future, we plan to synergize many existing APR tech- niques, improve our predictive model, and adopt the advances of other fields such as test case generation and program synthesis for APR.