Abstract
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. Fuzzy knowledge-based techniques are introduced as a basis for constructing ruelbased quality models that can identify outlying software components that might cause potential quality problems. The suggested approach and ist 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 showed by applying it to industrial real-time projects.
A preliminary version of this report will be published in the Software Quality Journal, Chapman & Hall, 1996.
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© 1997 Betriebswirtschaftlicher Verlag Dr. Th. Gabler GmbH, Wiesbaden
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Ebert, C. (1997). Applying Knowledge-Based Techniques to Software Quality Management. In: Lehner, F., Dumke, R., Abran, A. (eds) Software Metrics. Information Engineering und IV-Controlling. Deutscher Universitätsverlag, Wiesbaden. https://doi.org/10.1007/978-3-322-99929-0_13
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DOI: https://doi.org/10.1007/978-3-322-99929-0_13
Publisher Name: Deutscher Universitätsverlag, Wiesbaden
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