A hybrid faulty module prediction using association rule mining and logistic regression analysis

ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

Volumn , Issue , 2008, Pages 279-281

  Kamei, Yasutaka ^a   Monden, Akito ^a   Morisaki, Shuji ^a   Matsumoto, Ken Ichi ^a  

^a NARA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

Association rule mining; Empirical study; Fault prone module prediction; Logistic regression analysis

Indexed keywords

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; LOGISTICS; METAL ANALYSIS; MINING; SOFTWARE ENGINEERING; STATISTICS;

ASSOCIATION RULE MINING; CLASSIFICATION TREES; CONVENTIONAL MODELS; EMPIRICAL STUDY; FAULT-PRONE; FAULT-PRONE MODULE PREDICTION; INTERESTINGNESS; LINEAR DISCRIMINANT MODELS; LOGISTIC MODELS; LOGISTIC REGRESSION ANALYSIS; LOGISTIC REGRESSION MODELS; PREDICTION METHODS; PREDICTION PERFORMANCE; RULE INTERESTINGNESS;

REGRESSION ANALYSIS;

EID: 62949103673     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1414004.1414051     Document Type: Conference Paper

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