A literature review of automatic traceability links recovery for software change impact analysis

Aung, TWW; Huo, H; Sui, Y

A literature review of automatic traceability links recovery for software change impact analysis

Aung, TWW Huo, H

Sui, Y

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Program Comprehension, 2020, pp. 14-24
Issue Date:: 2020-07-13

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Field	Value	Language
dc.contributor.author	Aung, TWW
dc.contributor.author	Huo, H https://orcid.org/0000-0003-2440-714X
dc.contributor.author	Sui, Y https://orcid.org/0000-0002-9510-6574
dc.date.accessioned	2021-01-27T23:56:53Z
dc.date.available	2021-01-27T23:56:53Z
dc.date.issued	2020-07-13
dc.identifier.citation	IEEE International Conference on Program Comprehension, 2020, pp. 14-24
dc.identifier.isbn	9781450379588
dc.identifier.uri	http://hdl.handle.net/10453/145617
dc.description.abstract	© 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. In large-scale software development projects, change impact analysis (CIA) plays an important role in controlling software designevolution. Identifying and accessing the effects of software changesusing traceability links between various software artifacts is a common practice during the software development cycle. Recently,research in automated traceability-link recovery has received broadattention in the software maintenance community to reduce themanual maintenance cost of trace links by developers. In this study,we conducted a systematic literature review related to automatictraceability link recovery approaches with a focus on CIA. We identified 33 relevant studies and investigated the following aspects ofCIA: traceability approaches, CIA sets, degrees of evaluation, tracedirection and methods for recovering traceability link between artifacts of different types. Our review indicated that few traceabilitystudies focused on designing and testing impact analysis sets, presumably due to the scarcity of datasets. Based on the findings, weurge further industrial case studies. Finally, we suggest developingtraceability tools to support fully automatic traceability approaches,such as machine learning and deep learning.
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	IEEE International Conference on Program Comprehension
dc.relation.ispartof	ICPC '20: 28th International Conference on Program Comprehension
dc.relation.isbasedon	10.1145/3387904.3389251
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A literature review of automatic traceability links recovery for software change impact analysis
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-27T23:56:06Z
pubs.publication-status	Published

Abstract:

© 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. In large-scale software development projects, change impact analysis (CIA) plays an important role in controlling software designevolution. Identifying and accessing the effects of software changesusing traceability links between various software artifacts is a common practice during the software development cycle. Recently,research in automated traceability-link recovery has received broadattention in the software maintenance community to reduce themanual maintenance cost of trace links by developers. In this study,we conducted a systematic literature review related to automatictraceability link recovery approaches with a focus on CIA. We identified 33 relevant studies and investigated the following aspects ofCIA: traceability approaches, CIA sets, degrees of evaluation, tracedirection and methods for recovering traceability link between artifacts of different types. Our review indicated that few traceabilitystudies focused on designing and testing impact analysis sets, presumably due to the scarcity of datasets. Based on the findings, weurge further industrial case studies. Finally, we suggest developingtraceability tools to support fully automatic traceability approaches,such as machine learning and deep learning.

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http://hdl.handle.net/10453/145617