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Learning Representations for Effecti...

Li, Yi.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning Representations for Effective and Explainable Software Bug Detection and Fixing./
Author:	Li, Yi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	281 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Information science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30419404
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 281 p.

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

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

This item must not be sold to any third party vendors.

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.

ISBN: 9798380605274Subjects--Topical Terms:

554358
Information science.
Subjects--Index Terms:

Code representations

Learning Representations for Effective and Explainable Software Bug Detection and Fixing.
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100 1 $a Li, Yi. $3 911053
245 1 0 $a Learning Representations for Effective and Explainable Software Bug Detection and Fixing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 281 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
500 $a Advisor: Lee, Michael J.;Neamtiu, Iulian.
502 $a Thesis (Ph.D.)--New Jersey Institute of Technology, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
590 $a School code: 0152.
650 4 $a Information science. $3 554358
650 4 $a Computer science. $3 523869
653 $a Code representations
653 $a Deep learning
653 $a Explainable AI
653 $a Software engineering
653 $a Software quality control
653 $a Machine learning
690 $a 0723
690 $a 0800
690 $a 0984
710 2 $a New Jersey Institute of Technology. $b Department of Informatics. $3 3684451
773 0 $t Dissertations Abstracts International $g 85-04B.
790 $a 0152
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30419404