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Traceability links between software artifacts can assist in several software development tasks. There are some automatic traceability recovery methods that help with managing the massive number of software artifacts and their relationships, but they do not work well for software artifacts whose descriptions are different in terms of language or abstraction level. To overcome these weakness, we propose the Connecting Links Method (CLM), which recovers transitive traceability links between two artifacts by intermediating a third artifact. In order to apply CLM for general use without limitation in terms of software artifact type, we have designed a standardized method to calculate the relation score of transitive traceability links using the scores of direct traceability links between three artifacts. Furthermore, we propose an improvement of CLM by considering software version. We evaluated CLM by applying it to three software products and found that it is more effective for software artifacts whose language type or vocabulary are different compared to previous methods using textual similarity.
Ryosuke TSUCHIYA
Center for Technology Innovation - Systems Engineering at Hitachi, Ltd.
Kazuki NISHIKAWA
Dept. Computer Science at Waseda University
Hironori WASHIZAKI
Dept. Computer Science at Waseda University
Yoshiaki FUKAZAWA
Dept. Computer Science at Waseda University
Yuya SHINOHARA
Dept. Computer Science at Waseda University
Keishi OSHIMA
Center for Technology Innovation - Systems Engineering at Hitachi, Ltd.
Ryota MIBE
Center for Technology Innovation - Systems Engineering at Hitachi, Ltd.
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Ryosuke TSUCHIYA, Kazuki NISHIKAWA, Hironori WASHIZAKI, Yoshiaki FUKAZAWA, Yuya SHINOHARA, Keishi OSHIMA, Ryota MIBE, "Recovering Transitive Traceability Links among Various Software Artifacts for Developers" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1750-1760, September 2019, doi: 10.1587/transinf.2018EDP7331.
Abstract: Traceability links between software artifacts can assist in several software development tasks. There are some automatic traceability recovery methods that help with managing the massive number of software artifacts and their relationships, but they do not work well for software artifacts whose descriptions are different in terms of language or abstraction level. To overcome these weakness, we propose the Connecting Links Method (CLM), which recovers transitive traceability links between two artifacts by intermediating a third artifact. In order to apply CLM for general use without limitation in terms of software artifact type, we have designed a standardized method to calculate the relation score of transitive traceability links using the scores of direct traceability links between three artifacts. Furthermore, we propose an improvement of CLM by considering software version. We evaluated CLM by applying it to three software products and found that it is more effective for software artifacts whose language type or vocabulary are different compared to previous methods using textual similarity.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7331/_p
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@ARTICLE{e102-d_9_1750,
author={Ryosuke TSUCHIYA, Kazuki NISHIKAWA, Hironori WASHIZAKI, Yoshiaki FUKAZAWA, Yuya SHINOHARA, Keishi OSHIMA, Ryota MIBE, },
journal={IEICE TRANSACTIONS on Information},
title={Recovering Transitive Traceability Links among Various Software Artifacts for Developers},
year={2019},
volume={E102-D},
number={9},
pages={1750-1760},
abstract={Traceability links between software artifacts can assist in several software development tasks. There are some automatic traceability recovery methods that help with managing the massive number of software artifacts and their relationships, but they do not work well for software artifacts whose descriptions are different in terms of language or abstraction level. To overcome these weakness, we propose the Connecting Links Method (CLM), which recovers transitive traceability links between two artifacts by intermediating a third artifact. In order to apply CLM for general use without limitation in terms of software artifact type, we have designed a standardized method to calculate the relation score of transitive traceability links using the scores of direct traceability links between three artifacts. Furthermore, we propose an improvement of CLM by considering software version. We evaluated CLM by applying it to three software products and found that it is more effective for software artifacts whose language type or vocabulary are different compared to previous methods using textual similarity.},
keywords={},
doi={10.1587/transinf.2018EDP7331},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Recovering Transitive Traceability Links among Various Software Artifacts for Developers
T2 - IEICE TRANSACTIONS on Information
SP - 1750
EP - 1760
AU - Ryosuke TSUCHIYA
AU - Kazuki NISHIKAWA
AU - Hironori WASHIZAKI
AU - Yoshiaki FUKAZAWA
AU - Yuya SHINOHARA
AU - Keishi OSHIMA
AU - Ryota MIBE
PY - 2019
DO - 10.1587/transinf.2018EDP7331
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - Traceability links between software artifacts can assist in several software development tasks. There are some automatic traceability recovery methods that help with managing the massive number of software artifacts and their relationships, but they do not work well for software artifacts whose descriptions are different in terms of language or abstraction level. To overcome these weakness, we propose the Connecting Links Method (CLM), which recovers transitive traceability links between two artifacts by intermediating a third artifact. In order to apply CLM for general use without limitation in terms of software artifact type, we have designed a standardized method to calculate the relation score of transitive traceability links using the scores of direct traceability links between three artifacts. Furthermore, we propose an improvement of CLM by considering software version. We evaluated CLM by applying it to three software products and found that it is more effective for software artifacts whose language type or vocabulary are different compared to previous methods using textual similarity.
ER -