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Fuzzy classification for software criticality analysis
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DOI：10.1016/S0957-4174(96)00048-6.png)
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Abstract
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Managing software development and maintenance projects requires predictions about components of the software system that are likely to have a high error rate or that need high development effort. The value of any expert system is determined by the accuracy and cost of such predictions. Fuzzy classification techniques are introduced as a basis for constructing rule-based quality models that can identify outlying software components that might cause potential quality problems. The suggested approach and its advantages towards common classification and decision techniques is illustrated with experimental results. A module quality model--with respect to changes--provides both quality of fit (according to past data) and predictive accuracy (according to ongoing projects). Its portability is shown by applying it to industrial real-time projects. Copyright (C) 1996 Elsevier Science
Keywords:
NEURAL-NETWORK
DECISION TREES
QUALITY
SYSTEMS
METRICS
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