Software defect prediction based on class-association rules

Although there have lots of studies on using static code attributes to identify defective software modules, there still have many challenges. For instance, it is difficult to implement the Apriori-type algorithm to predict defects by learning from an imbalanced dataset. For more accurate and understandable defect prediction, a novel approach based on class-association rules algorithm is proposed. Class-association rules are looked as a separate class label, which is a specific type of association rules that explores the relationship between attributes and categories. In an empirical comparison with four datasets, the novel approach is superior to other four classification techniques and accordingly, proved it's valuable for defect prediction.

[1]  Ömer Faruk Arar,et al.  Software defect prediction using cost-sensitive neural network , 2015, Appl. Soft Comput..

[2]  Xing Zhang,et al.  A new approach to classification based on association rule mining , 2006, Decis. Support Syst..

[3]  Francisco Herrera,et al.  An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics , 2013, Inf. Sci..

[4]  Ali Selamat,et al.  A survey on software fault detection based on different prediction approaches , 2014, Vietnam Journal of Computer Science.

[5]  Dulal Chandra Sahana.  Software Defect Prediction Based on Classication Rule Mining , 2013 .

[6]  Tim Menzies,et al.  Data Mining Static Code Attributes to Learn Defect Predictors , 2007, IEEE Transactions on Software Engineering.

[7]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[8]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[9]  Qinbao Song,et al.  Using Coding-Based Ensemble Learning to Improve Software Defect Prediction , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Hafsa Zafar,et al.  Finding focused itemsets from software defect data , 2012, 2012 15th International Multitopic Conference (INMIC).

[11]  Thomas J. Ostrand,et al.  \{PROMISE\} Repository of empirical software engineering data , 2007 .

[12]  Gennady Agre,et al.  On Supervised and Unsupervised Discretization , 2007 .

[13]  Mian M. Awais,et al.  Improving Recall of software defect prediction models using association mining , 2015, Knowl. Based Syst..

[14]  Xin Yao,et al.  Using Class Imbalance Learning for Software Defect Prediction , 2013, IEEE Transactions on Reliability.

[15]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[16]  Cagatay Catal,et al.  Performance Evaluation Metrics for Software Fault Prediction Studies , 2012 .

[17]  Tao Wang,et al.  Naive Bayes Software Defect Prediction Model , 2010, 2010 International Conference on Computational Intelligence and Software Engineering.

[18]  Yiming Ma,et al.  Improving an Association Rule Based Classifier , 2000, PKDD.

[19]  Divya Tomar,et al.  Prediction of Defective Software Modules Using Class Imbalance Learning , 2016, Appl. Comput. Intell. Soft Comput..

[20]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[21]  Mian M. Awais,et al.  Identifying Association between Longer Itemsets and Software Defects , 2013, ICONIP.

[22]  Jan Vanthienen,et al.  Software Defect Prediction Based on Association Rule Classification , 2010 .