The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Learning representations for effective and explainable software bug detection and fixing
Author:	Li, Yi
View Online:	njit-etd2023-040 (xviii, 260 pages ~ 8.5 MB pdf)
Department:	Department of Informatics
Degree:	Doctor of Philosophy
Program:	Information Systems
Document Type:	Dissertation
Advisory Committee:	Lee, Michael J. (Committee co-chair) Neamtiu, Iulian (Committee co-chair) Weiss, Tomer (Committee member) Phan, Hai Nhat (Committee member) Nguyen, Tien N. (Committee member)
Date:	2023-08
Keywords:	Code representation Deep learning Explainable AI Software engineering Software quality control Software security
Availability:	Unrestricted
Abstract:	Software has an integral role in modern life; hence software bugs, which undermine software quality and reliability, have substantial societal and economic implications. The advent of machine learning and deep learning in software engineering has led to major advances in bug detection and fixing approaches, yet they fall short of desired precision and recall. This shortfall arises from the absence of a 'bridge,' known as learning code representations, that can transform information from source code into a suitable representation for effective processing via machine and deep learning. This dissertation builds such a bridge. Specifically, it presents solutions for effectively learning code representations using four distinct methods—context-based, testing results-based, tree-based, and graph-based—thus improving bug detection and fixing approaches, as well as providing developers insight into the foundational reasoning. The experimental results demonstrate that using learning code representations can significantly enhance explainable bug detection and fixing, showcasing the practicability and meaningfulness of the approaches formulated in this dissertation toward improving software quality and reliability.