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Machine Learning Techniques to Predict Software Defect

Machine Learning Techniques to Predict Software Defect

Ramakanta Mohanty, Vadlamani Ravi
Copyright: © 2014 |Pages: 13
ISBN13: 9781466652026|ISBN10: 1466652020|EISBN13: 9781466652033
DOI: 10.4018/978-1-4666-5202-6.ch129
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MLA

Mohanty, Ramakanta, and Vadlamani Ravi. "Machine Learning Techniques to Predict Software Defect." Encyclopedia of Business Analytics and Optimization, edited by John Wang, IGI Global, 2014, pp. 1422-1434. https://doi.org/10.4018/978-1-4666-5202-6.ch129

APA

Mohanty, R. & Ravi, V. (2014). Machine Learning Techniques to Predict Software Defect. In J. Wang (Ed.), Encyclopedia of Business Analytics and Optimization (pp. 1422-1434). IGI Global. https://doi.org/10.4018/978-1-4666-5202-6.ch129

Chicago

Mohanty, Ramakanta, and Vadlamani Ravi. "Machine Learning Techniques to Predict Software Defect." In Encyclopedia of Business Analytics and Optimization, edited by John Wang, 1422-1434. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-5202-6.ch129

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Abstract

The past 10 years have seen the prediction of software defects proposed by many researchers using various metrics based on measurable aspects of source code entities (e.g. methods, classes, files or modules) and the social structure of software project in an effort to predict the software defects. However, these metrics could not predict very high accuracies in terms of sensitivity, specificity and accuracy. In this chapter, we propose the use of machine learning techniques to predict software defects. The effectiveness of all these techniques is demonstrated on ten datasets taken from literature. Based on an experiment, it is observed that PNN outperformed all other techniques in terms of accuracy and sensitivity in all the software defects datasets followed by CART and Group Method of data handling. We also performed feature selection by t-statistics based approach for selecting feature subsets across different folds for a given technique and followed by the feature subset selection. By taking the most important variables, we invoked the classifiers again and observed that PNN outperformed other classifiers in terms of sensitivity and accuracy. Moreover, the set of ‘if- then rules yielded by J48 and CART can be used as an expert system for prediction of software defects.

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