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

 
: Morasca, S.; Ruhe, G.

:
Fulltext urn:nbn:de:0011-px-492688 (147 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
Report, Electronic Publication
Fraunhofer IESE ()
case study; experience factory; fault proneness; hybrid approach; knowledge discovery; logistic regression; rough set; software maintenance

: http://publica.fraunhofer.de/documents/PX-49268.html