國立虎尾科技大學 |

Learning Representations for Effective and Explainable Software Bug Detection and Fixing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Learning Representations for Effective and Explainable Software Bug Detection and Fixing./
Author:	Li, Yi.
Description:	1 online resource (281 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Information science. -
Online resource:	click for full text (PQDT)
ISBN:	9798380605274

Learning Representations for Effective and Explainable Software Bug Detection and Fixing.
Li, Yi.

Learning Representations for Effective and Explainable Software Bug Detection and Fixing. - 1 online resource (281 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--New Jersey Institute of Technology, 2023.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380605274Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

Code representationsIndex Terms--Genre/Form:

554714
Electronic books.

Learning Representations for Effective and Explainable Software Bug Detection and Fixing.
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502 $a Thesis (Ph.D.)--New Jersey Institute of Technology, 2023.
504 $a Includes bibliographical references
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