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    STUDIA INFORMATICA - Issue no. 4 / 2013  
         
  Article:   ON THE SOFTWARE METRICS INFLUENCE IN RELATIONAL ASSOCIATION RULE-BASED SOFTWARE DEFECT PREDICTION.

Authors:  ZSUZSANNA MARIAN.
 
       
         
  Abstract:   Software defect prediction tries to automatically identify defective software modules, in order to help software testers focus their time and effort on those modules which are likely to contain faults. So far many different machine learning algorithms have been used for this classification task. We have introduced a new software defect prediction method, called DPRAR, which uses relational association rules to classify modules, represented by a vector of software metric values, as faulty or non-faulty. In this paper we investigate how different feature elimination techniques influence the results of the DPRAR method. We also consider two methods for computing the scores for a module, which are two values showing how close the module is to the faulty instances and the non-faulty instances.Experiments on an open source dataset as well as comparisons to related work are provided.

Key words and phrases. relational association rules, software metrics, software defectprediction.
2010 Mathematics Subject Classification. 62H30, 68N99.
 
         
     
         
         
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