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Multivariate Analysis and Rough Sets: Two Approaches for Software‐quality Analysis
Author(s) -
Meskens Nadine,
Levecq Philippe,
Lebon Florence
Publication year - 2002
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/1475-3995.00360
Subject(s) - computer science , quality (philosophy) , flexibility (engineering) , software quality , software , set (abstract data type) , conformity , multivariate analysis , reliability (semiconductor) , risk analysis (engineering) , multivariate statistics , data mining , operations research , management science , machine learning , software development , statistics , mathematics , engineering , medicine , philosophy , power (physics) , physics , epistemology , quantum mechanics , political science , law , programming language
Computer programs are becoming more and more complex and voluminous in most firms. This raises the cost of maintenance. To minimize this cost, qualities must be given to computer programs that assure them good evolution, i.e., reliability, efficiency, flexibility, conformity, etc. This cost minimization must be preceded by a diagnosis that recommend a set of renovation actions to be undertaken in order to improve the software quality. The objective of this study is to associate a set of renovation action to each profile’s program expressed in terms of metrics. To do this, two approaches are taken. First, a multivariate analysis is used to allocate each program to a predefined category reflecting the diagnosis of the program’s health. Second, we try to work out a diagnosis of the health of these programs by developing a quality model composed of a set of decision rules supporting software quality improvement decisions. We have finally implemented this set of rules in the form of an expert system tool to allow the classification of new programs by the decision‐maker.