File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Book Chapter: Emerging aspects of software fault localization

TitleEmerging aspects of software fault localization
Authors
KeywordsAutomated test case generation tool
Fault localization tool
Machine-learning approach
Metamorphic relations
Issue Date20-Apr-2023
PublisherWiley
Abstract

In this final chapter of the Handbook, we introduce emerging, innovative methods in software fault localization. First, we present scientific and systematic hypothesis-testing techniques and show they may be applied in practice. Second, for fault localization in the absence of a test oracle, we present a semi-proving methodology based on metamorphic relations and symbolic evaluation. It hinges on causes and effects instead of statistical probabilities. Third, we present an approach to predict the effectiveness of fault localization tools using machine learning. Lastly, we discuss why manually produced test cases are not ideal for fault localization and explain how to mitigate the problem by using automatically generated test cases.


Persistent Identifierhttp://hdl.handle.net/10722/354580
ISBN

 

DC FieldValueLanguage
dc.contributor.authorTse, TH-
dc.contributor.authorLo, D-
dc.contributor.authorGroce, A-
dc.contributor.authorPerscheid, M-
dc.contributor.authorHirschfeld, R-
dc.contributor.authorWong, WE-
dc.date.accessioned2025-02-21T00:35:04Z-
dc.date.available2025-02-21T00:35:04Z-
dc.date.issued2023-04-20-
dc.identifier.isbn9781119291800-
dc.identifier.urihttp://hdl.handle.net/10722/354580-
dc.description.abstract<p>In this final chapter of the Handbook, we introduce emerging, innovative methods in software fault localization. First, we present scientific and systematic hypothesis-testing techniques and show they may be applied in practice. Second, for fault localization in the absence of a test oracle, we present a semi-proving methodology based on metamorphic relations and symbolic evaluation. It hinges on causes and effects instead of statistical probabilities. Third, we present an approach to predict the effectiveness of fault localization tools using machine learning. Lastly, we discuss why manually produced test cases are not ideal for fault localization and explain how to mitigate the problem by using automatically generated test cases.<br></p>-
dc.languageeng-
dc.publisherWiley-
dc.relation.ispartofHandbook of Software Fault Localization: Foundations and Advances-
dc.subjectAutomated test case generation tool-
dc.subjectFault localization tool-
dc.subjectMachine-learning approach-
dc.subjectMetamorphic relations-
dc.titleEmerging aspects of software fault localization-
dc.typeBook_Chapter-
dc.identifier.doi10.1002/9781119880929.ch13-
dc.identifier.scopuseid_2-s2.0-85161195512-
dc.identifier.spage529-
dc.identifier.epage579-
dc.identifier.eisbn9781119880929-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats