Dissertation/Thesis Abstract

The author has requested that access to this graduate work be delayed until 2020-01-29. After this date, this graduate work will be available on an open access basis.
Semantically Enhanced Traceability across Software and System-Related Natural Language Artifacts
by Guo, Jin L. C., Ph.D., University of Notre Dame, 2017, 181; 13836191
Abstract (Summary)

This dissertation focuses on accurate trace link creation for software projects. Trace links represent established associations between software requirements, design, code, test cases and other such artifacts. Traceability describes the potential to create and maintain trace links during the software development life cycle. In most safety-critical domains, the need for traceability is prescribed by certifying bodies.

Creating trace links manually is time consuming and error prone. Automated solutions use information retrieval and machine learning techniques to generate trace links; however, current techniques fail to understand semantics of the software artifacts or to integrate domain knowledge into the tracing process and therefore tend to deliver imprecise and inaccurate results. Therefore, this dissertation proposes a series of traceability solutions with different semantic enhancement strategies that aim at improving the quality of trace link generation between regulations, software requirements and design documents written in natural language. The first approach augments software artifacts with an ontology of domain terms and relations. To further increase the trace link accuracy, an intelligent tracing system DoCIT is proposed that is able to reason over artifact semantics through use of a domain ontology and a set of trace heuristics. Finally, a deep learning based tracing method is presented that represents and compares artifact semantics in an implicit but fully automated way.

The main contribution of this dissertation is in addressing the lack of semantic knowledge in current traceability solutions by developing techniques which extract semantic knowledge, integrate it into the tracing process, and thereby deliver more accurate and trustworthy traceability solutions.

Indexing (document details)
Advisor: Cleland Huang, Jane
Commitee: Bleland-Huang, Jane, Chiang, David, Hayes, Jane, McMillan, Collin
School: University of Notre Dame
Department: Computer Science and Engineering
School Location: United States -- Indiana
Source: DAI-B 80/06(E), Dissertation Abstracts International
Subjects: Computer Engineering, Artificial intelligence, Computer science
Keywords: Domain knowledge, Software artifact processing, Software engineering, Software traceability
Publication Number: 13836191
ISBN: 9780438833241
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy