Members of the defense committee:
- Prof. Dr Yves Le Traon, University of Luxembourg, Supervisor
- Ass-Prof. Dr Tegawendé Bissyandé, University of Luxembourg, Co-advisor, Chairman
- Dr Dongsun Kim, Furiosa.ai, South Korea, Co-advisor, Vice Chairman
- Prof. Dr Andreas Zeller, Saarland University, Member
- A-Prof. Dr David Lo, Singapore Management University, Member
Abstract:
Error-free software is a myth. Debugging thus accounts for a significant portion of software maintenance and absorbs a large part of software cost. In particular, the manual task of fixing bugs is tedious, error-prone and time-consuming. In the last decade, automatic bug-fixing, also referred to as automated program repair (APR) has boomed as a promising endeavour of software engineering towards alleviating developers’ burden. Several potentially promising techniques have been proposed making APR an increasingly prominent topic in the software engineering community. In production, APR will drastically reduce time-to-fix delays and limit downtime. In a development cycle, APR can help suggest changes to accelerate debugging.
As an emergent domain, however, program repair has many open problems that the community is still exploring. Our work contributes to this momentum on two angles: the repair of program for functionality bugs, and the repair of programs for method naming issues. The thesis starts with highlighting findings on key empirical studies that we have performed to inform future repair approaches. Then, we focus on template-based program repair scenarios and explore deep learning models for inferring accurate and relevant patterns. Finally, we integrate these patterns into APR pipelines, which yield state of the art repair tools.