Requirements Traceability for Object Oriented Systems by Partitioning Source Code. Ali, N., Gu�h�neuc, Y., & Antoniol, G. In Pinzger, M. & Poshyvanyk, D., editors, Proceedings of the 18<sup>th</sup> Working Conference on Reverse Engineering (WCRE), pages 45–54, October, 2011. IEEE CS Press. 10 pages.![Requirements Traceability for Object Oriented Systems by Partitioning Source Code [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Requirements traceability ensures that source code is consistent with documentation and that all requirements have been implemented. During software evolution, features are added, removed, or modified, the code drifts away from its original requirements. Thus traceability recovery approaches becomes necessary to re-establish the traceability relations between requirements and source code. This paper presents an approach (Coparvo) complementary to existing traceability recovery approaches for object-oriented programs. Coparvo reduces false positive links recovered by traditional traceability recovery processes thus reducing the manual validation effort. Coparvo assumes that information extracted from different entities (e.g., class names, comments, class variables, or methods signatures) are different information sources; they may have different level of reliability in requirements traceability and each information source may act as a different expert recommending traceability links. We applied Coparvo on three data sets, Pooka, SIP Communicator, and iTrust, to filter out false positive links recovered via the information retrieval approach ıe vector space model. The results show that Coparvo significantly improves the of the recovered links accuracy and also reduces up to $83%$ effort required to manually remove false positive links.
@INPROCEEDINGS{Ali11-WCRE-Partitioning,
AUTHOR = {Nasir Ali and Yann-Ga�l Gu�h�neuc and Giuliano Antoniol},
BOOKTITLE = {Proceedings of the 18<sup>th</sup> Working Conference on Reverse Engineering (WCRE)},
TITLE = {Requirements Traceability for Object Oriented Systems by
Partitioning Source Code},
YEAR = {2011},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Martin Pinzger and Denys Poshyvanyk},
MONTH = {October},
NOTE = {10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {45--54},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Requirements and features</b>,
Venue: <c>WCRE</c>},
URL = {http://www.ptidej.net/publications/documents/WCRE11b.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/WCRE11b.ppt.pdf},
ABSTRACT = {Requirements traceability ensures that source code is
consistent with documentation and that all requirements have been
implemented. During software evolution, features are added, removed,
or modified, the code drifts away from its original requirements.
Thus traceability recovery approaches becomes necessary to
re-establish the traceability relations between requirements and
source code. This paper presents an approach (Coparvo) complementary
to existing traceability recovery approaches for object-oriented
programs. Coparvo reduces false positive links recovered by
traditional traceability recovery processes thus reducing the manual
validation effort. Coparvo assumes that information extracted from
different entities (e.g., class names, comments, class variables, or
methods signatures) are different information sources; they may have
different level of reliability in requirements traceability and each
information source may act as a different expert recommending
traceability links. We applied Coparvo on three data sets, Pooka, SIP
Communicator, and iTrust, to filter out false positive links
recovered via the information retrieval approach \ie{} vector space
model. The results show that Coparvo significantly improves the of
the recovered links accuracy and also reduces up to $83\%$ effort
required to manually remove false positive links.}
}
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We applied Coparvo on three data sets, Pooka, SIP Communicator, and iTrust, to filter out false positive links recovered via the information retrieval approach ıe vector space model. 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