TY - JOUR
T1 - Semantic metrics for software products
AU - Mili, A.
AU - Jaoua, A.
AU - Frias, M.
AU - Helali, Rasha Gaffer Mohamed
N1 - Funding Information:
Acknowledgments This publication was made possible by a grant from the Qatar National Research Fund NPRP04-1109-1-174. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the QNRF.
PY - 2014/9
Y1 - 2014/9
N2 - Like all engineering disciplines, software engineering relies on quantitative analysis to support rationalized decision making. Software engineering researchers and practitioners have traditionally relied on software metrics to quantify attributes of software products and processes. Whereas traditional software metrics are typically based on a syntactic analysis of software products, we introduce and discuss metrics that are based on a semantic analysis: our metrics do not reflect the form or structure of software products, but rather the properties of their function. At a time when software systems grow increasingly large and complex, the focus on diagnosing, identifying and removing every fault in the software product ought to relinquish the stage to a more measured, more balanced, and more realistic approach, which emphasizes failure avoidance, in addition to fault avoidance and fault removal. Semantic metrics are a good fit for this purpose, reflecting as they do a system's ability to avoid failure rather than its proneness to being free of faults.
AB - Like all engineering disciplines, software engineering relies on quantitative analysis to support rationalized decision making. Software engineering researchers and practitioners have traditionally relied on software metrics to quantify attributes of software products and processes. Whereas traditional software metrics are typically based on a syntactic analysis of software products, we introduce and discuss metrics that are based on a semantic analysis: our metrics do not reflect the form or structure of software products, but rather the properties of their function. At a time when software systems grow increasingly large and complex, the focus on diagnosing, identifying and removing every fault in the software product ought to relinquish the stage to a more measured, more balanced, and more realistic approach, which emphasizes failure avoidance, in addition to fault avoidance and fault removal. Semantic metrics are a good fit for this purpose, reflecting as they do a system's ability to avoid failure rather than its proneness to being free of faults.
KW - Error maskability
KW - Functional redundancy
KW - Requirements flexibility
KW - Semantic metrics
KW - State redundancy
KW - Syntactic metrics
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U2 - 10.1007/s11334-014-0233-3
DO - 10.1007/s11334-014-0233-3
M3 - Article
AN - SCOPUS:84906258816
SN - 1614-5046
VL - 10
SP - 203
EP - 217
JO - Innovations in Systems and Software Engineering
JF - Innovations in Systems and Software Engineering
IS - 3
ER -