Identifying recurring association rules in software defect prediction

Association rule mining discovers patterns of co-occurrences of attributes as association rules in a data set. The derived association rules are expected to be recurrent, that is, the patterns recur in future in other data sets. This paper defines the recurrence of a rule, and aims to find a criteria to distinguish between high recurrent rules and low recurrent ones using a data set for software defect prediction. An experiment with the Eclipse Mylyn defect data set showed that rules of lower than 30 transactions showed low recurrence. We also found that the lower bound of transactions to select high recurrence rules is dependent on the required precision of defect prediction.

[1]  Qiang Yang,et al.  Mining web logs for prediction models in WWW caching and prefetching , 2001, KDD '01.

[2]  Akito Monden,et al.  Assessing the Cost Effectiveness of Fault Prediction in Acceptance Testing , 2013, IEEE Transactions on Software Engineering.

[3]  Qinbao Song,et al.  Software defect association mining and defect correction effort prediction , 2006 .

[4]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[5]  Akito Monden,et al.  A hybrid faulty module prediction using association rule mining and logistic regression analysis , 2008, ESEM '08.

[6]  Akito Monden,et al.  Defect Data Analysis Based on Extended Association Rule Mining , 2007, Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007).

[7]  Bart Baesens,et al.  Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings , 2008, IEEE Transactions on Software Engineering.

[8]  Witold Pedrycz,et al.  A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[9]  Akito Monden,et al.  A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction , 2012, 2012 19th Asia-Pacific Software Engineering Conference.

[10]  Taghi M. Khoshgoftaar,et al.  The Detection of Fault-Prone Programs , 1992, IEEE Trans. Software Eng..

[11]  Niclas Ohlsson,et al.  Predicting Fault-Prone Software Modules in Telephone Switches , 1996, IEEE Trans. Software Eng..

[12]  Taghi M. Khoshgoftaar,et al.  MODELING SOFTWARE QUALITY WITH CLASSIFICATION TREES , 2001 .