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Enabling Traceability Reuse for Impact Analyses: A Feasibility Study in a Safety Context

Borg, Markus LU ; Gotel, Orlena and Wnuk, Krzysztof LU (2013) 7th International Workshop on Traceability in Emerging Forms of Software Engineering In Proceedings of the 7th International Workshop on Traceability in Emerging Forms of Software Engineering p.72-78
Abstract
Engineers working on safety critical software development must explicitly specify trace links as part of Impact Analyses (IA), both to code and non-code development artifacts. In large-scale projects, constituting information spaces of thousands of artifacts, conducting IA is tedious work relying on extensive system understanding. We propose to support this activity by enabling engineers to reuse knowledge from previously completed IAs. We do this by mining the trace links in documented IA reports, creating a semantic network of the resulting traceability, and rendering the resulting network amenable to visual analyses. We studied an Issue Management System (IMS), from within a company in the power and automation domain, containing 4,845... (More)
Engineers working on safety critical software development must explicitly specify trace links as part of Impact Analyses (IA), both to code and non-code development artifacts. In large-scale projects, constituting information spaces of thousands of artifacts, conducting IA is tedious work relying on extensive system understanding. We propose to support this activity by enabling engineers to reuse knowledge from previously completed IAs. We do this by mining the trace links in documented IA reports, creating a semantic network of the resulting traceability, and rendering the resulting network amenable to visual analyses. We studied an Issue Management System (IMS), from within a company in the power and automation domain, containing 4,845 IA reports from 9 years of development relating to a single safety critical system. The domain has strict process requirements guiding the documented IAs. We used link mining to extract trace links, from these IA reports to development artifacts, and to determine their link semantics. We constructed a semantic network of the interrelated development artifacts, containing 6,104 non-code artifacts and 9,395 trace links, and we used two visualizations to examine the results. We provide initial suggestions as to how the knowledge embedded in such a network can be (re-)used to advance support for IA. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
impact analysis, issue management, traceability, data mining, semantic networks, visualization
in
Proceedings of the 7th International Workshop on Traceability in Emerging Forms of Software Engineering
pages
7 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
7th International Workshop on Traceability in Emerging Forms of Software Engineering
external identifiers
  • wos:000335673000013
  • scopus:84888616923
project
EASE
language
English
LU publication?
yes
id
ad3e8f51-c226-44f6-b14e-98a02b3c824c (old id 3787703)
date added to LUP
2013-05-22 13:08:35
date last changed
2017-01-08 05:42:16
@inproceedings{ad3e8f51-c226-44f6-b14e-98a02b3c824c,
  abstract     = {Engineers working on safety critical software development must explicitly specify trace links as part of Impact Analyses (IA), both to code and non-code development artifacts. In large-scale projects, constituting information spaces of thousands of artifacts, conducting IA is tedious work relying on extensive system understanding. We propose to support this activity by enabling engineers to reuse knowledge from previously completed IAs. We do this by mining the trace links in documented IA reports, creating a semantic network of the resulting traceability, and rendering the resulting network amenable to visual analyses. We studied an Issue Management System (IMS), from within a company in the power and automation domain, containing 4,845 IA reports from 9 years of development relating to a single safety critical system. The domain has strict process requirements guiding the documented IAs. We used link mining to extract trace links, from these IA reports to development artifacts, and to determine their link semantics. We constructed a semantic network of the interrelated development artifacts, containing 6,104 non-code artifacts and 9,395 trace links, and we used two visualizations to examine the results. We provide initial suggestions as to how the knowledge embedded in such a network can be (re-)used to advance support for IA.},
  author       = {Borg, Markus and Gotel, Orlena and Wnuk, Krzysztof},
  booktitle    = {Proceedings of the 7th International Workshop on Traceability in Emerging Forms of Software Engineering},
  keyword      = {impact analysis,issue management,traceability,data mining,semantic networks,visualization},
  language     = {eng},
  pages        = {72--78},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Enabling Traceability Reuse for Impact Analyses: A Feasibility Study in a Safety Context},
  year         = {2013},
}