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A Hybrid Approach to Analyze Empirical Software Engineering Data and its Application Predict Module Fault-proneness in Maintenance

urn:nbn:de:0011-px-492688 (148 KByte PDF)

MD5 Fingerprint: 5fd3fbb615f277a02a48b0563945919f

Created on: 09.08.2000

	Kaiserslautern, 1999, VII, 26 pp. : Ill., Lit. IESE-Report, 026.99/E Reportnr.: IESE-Report 026.99/E

	English
	Research Report, Electronic Publication
	Fraunhofer IESE ()
	case study; experience factory; fault proneness; hybrid approach; knowledge discovery; logistic regression; rough set; software maintenance