Trust-Based Requirements Traceability. Ali, N., Gu�h�neuc, Y., & Antoniol, G. In Sim, S. E. & Ricca, F., editors, Proceedings of the 19<sup>th</sup> International Conference on Program Comprehension (ICPC), pages 111–120, June, 2011. IEEE CS Press. 10 pages.![Trust-Based Requirements Traceability [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Information retrieval (IR) approaches have proven useful in recovering traceability links between free-text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trustrace, inspired by Web trust models to improve the precision and recall of traceability links: Trustrace uses any traceability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, Histrace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with Histrace to build Trustrace\textrmVSM, \textrmHistrace in which we use Histrace as one expert adding knowledge to the traceability links extractted from CVS/SVN change logs. We apply Trustrace\textrmVSM, \textrmHistrace on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that Trustrace\textrmVSM, \textrmHistrace improves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.
@INPROCEEDINGS{Ali11-ICPC-TrustTraceability,
AUTHOR = {Nasir Ali and Yann-Ga�l Gu�h�neuc and Giuliano Antoniol},
BOOKTITLE = {Proceedings of the 19<sup>th</sup> International Conference on Program Comprehension (ICPC)},
TITLE = {Trust-Based Requirements Traceability},
YEAR = {2011},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Susan E. Sim and Filippo Ricca},
MONTH = {June},
NOTE = {10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {111--120},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Requirements and features</b>,
Venue: <c>ICPC</c>},
URL = {http://www.ptidej.net/publications/documents/ICPC11c.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/ICPC11c.ppt.pdf},
ABSTRACT = {Information retrieval (IR) approaches have proven useful
in recovering traceability links between free-text documentation and
source code. IR-based traceability recovery approaches produce ranked
lists of traceability links between pieces of documentation and
source code. These traceability links are then pruned using various
strategies and, finally, validated by human experts. In this paper we
propose two contributions to improve the precision and recall of
traceability links and, thus, reduces the required human experts'
manual validation effort. First, we propose a novel approach,
Trustrace, inspired by Web trust models to improve the precision and
recall of traceability links: Trustrace uses any traceability
recovery approach to obtain a set of traceability links, which
rankings are then re-evaluated using a set of other traceability
recovery approaches. Second, we propose a novel traceability recovery
approach, Histrace, to identify traceability links between
requirements and source code through CVS/SVN change logs using a
Vector Space Model (VSM). We combine a traditional recovery
traceability approach with Histrace to build
Trustrace<sup>{\textrm{VSM},~\textrm{Histrace}}</sup> in which we use
Histrace as one expert adding knowledge to the traceability links
extractted from CVS/SVN change logs. We apply
Trustrace<sup>{\textrm{VSM},~\textrm{Histrace}}</sup> on two case
studies to compare its traceability links with those recovered using
only the VSM-based approach, in terms of precision and recall. We
show that Trustrace<sup>{\textrm{VSM},~\textrm{Histrace}}</sup>
improves with statistical significance the precision of the
traceability links while also improving recall but without
statistical significance.}
}
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IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trustrace, inspired by Web trust models to improve the precision and recall of traceability links: Trustrace uses any traceability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, Histrace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with Histrace to build Trustrace<sup>\\textrmVSM, \\textrmHistrace</sup> in which we use Histrace as one expert adding knowledge to the traceability links extractted from CVS/SVN change logs. We apply Trustrace<sup>\\textrmVSM, \\textrmHistrace</sup> on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. 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In this paper we \r\n propose two contributions to improve the precision and recall of \r\n traceability links and, thus, reduces the required human experts' \r\n manual validation effort. First, we propose a novel approach, \r\n Trustrace, inspired by Web trust models to improve the precision and \r\n recall of traceability links: Trustrace uses any traceability \r\n recovery approach to obtain a set of traceability links, which \r\n rankings are then re-evaluated using a set of other traceability \r\n recovery approaches. Second, we propose a novel traceability recovery \r\n approach, Histrace, to identify traceability links between \r\n requirements and source code through CVS/SVN change logs using a \r\n Vector Space Model (VSM). 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