Prereqir: Recovering Pre-Requirements via Cluster Analysis. Hayes, J. H., Antoniol, G., & Gu�h�neuc, Y. In Zaidman, A., Di Penta, M., & Hassan, A., editors, Proceedings of the 15<sup>th</sup> Working Conference on Reverse Engineering (WCRE), pages 165–174, October, 2008. IEEE CS Press. 10 pages.
Paper abstract bibtex High-level software artifacts, such as requirements, domain-specific requirements, and so on, are an important source of information that is often neglected during the reverse- and re-engineering processes. We posit that domain specific pre-requirements information (PRI) can be obtained by eliciting the stakeholders' understanding of generic systems or domains. We discuss the semi-automatic recovery of domain-specific PRI that can then be used during reverse- and re-engineering, for example, to recover traceability links or to assess the degree of obsolescence of a system with respect to competing systems and the clients' expectations. We present a method using partition around medoids and agglomerative clustering for obtaining, structuring, analyzing, and labeling textual PRI from a group of diverse stakeholders. We validate our method using PRI for the development of a generic Web browser provided by 22 different stakeholders. We show that, for a similarity threshold of about 0.36, about 55\NOof the PRI were common to two or more stakeholders and 42\NOwere outliers. We automatically label the common and outlier PRI (82\NOcorrectly labeled), and obtain 74\NOaccuracy for the similarity threshold of 0.36 (78\NOfor a threshold of 0.5). We assess the recall and precision of the method, and compare the labeled PRI to a generic Web browser requirements specification.
@INPROCEEDINGS{Hayes08-WCRE-PREREQIR,
AUTHOR = {Jane Huffman Hayes and Giuliano Antoniol and
Yann-Ga�l Gu�h�neuc},
BOOKTITLE = {Proceedings of the 15<sup>th</sup> Working Conference on Reverse Engineering (WCRE)},
TITLE = {Prereqir: Recovering Pre-Requirements via Cluster
Analysis},
YEAR = {2008},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Andy Zaidman and Di Penta, Massimilano and Ahmed Hassan},
MONTH = {October},
NOTE = {10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {165--174},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Requirements and features</b>,
Venue: <c>WCRE</c>},
URL = {http://www.ptidej.net/publications/documents/WCRE08.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/WCRE08.ppt.pdf},
ABSTRACT = {High-level software artifacts, such as requirements,
domain-specific requirements, and so on, are an important source of
information that is often neglected during the reverse- and
re-engineering processes. We posit that domain specific
pre-requirements information (PRI) can be obtained by eliciting the
stakeholders' understanding of generic systems or domains. We discuss
the semi-automatic recovery of domain-specific PRI that can then be
used during reverse- and re-engineering, for example, to recover
traceability links or to assess the degree of obsolescence of a
system with respect to competing systems and the clients'
expectations. We present a method using partition around medoids and
agglomerative clustering for obtaining, structuring, analyzing, and
labeling textual PRI from a group of diverse stakeholders. We
validate our method using PRI for the development of a generic Web
browser provided by 22 different stakeholders. We show that, for a
similarity threshold of about 0.36, about 55\NOof the PRI were common
to two or more stakeholders and 42\NOwere outliers. We automatically
label the common and outlier PRI (82\NOcorrectly labeled), and obtain
74\NOaccuracy for the similarity threshold of 0.36 (78\NOfor a
threshold of 0.5). We assess the recall and precision of the method,
and compare the labeled PRI to a generic Web browser requirements
specification.}
}
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We posit that domain specific pre-requirements information (PRI) can be obtained by eliciting the stakeholders' understanding of generic systems or domains. We discuss the semi-automatic recovery of domain-specific PRI that can then be used during reverse- and re-engineering, for example, to recover traceability links or to assess the degree of obsolescence of a system with respect to competing systems and the clients' expectations. We present a method using partition around medoids and agglomerative clustering for obtaining, structuring, analyzing, and labeling textual PRI from a group of diverse stakeholders. We validate our method using PRI for the development of a generic Web browser provided by 22 different stakeholders. We show that, for a similarity threshold of about 0.36, about 55\\NOof the PRI were common to two or more stakeholders and 42\\NOwere outliers. 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