Predicting Software Faults in Large Space Systems using Machine Learning Techniques

  • Bhekisipho Twala Department of Electrical and Electronic Engineering Science, University of Johannesbug
Keywords: Software metrics, machine learning, classifiers, ensemble, fault-proneness prediction

Abstract

Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of engineering problems including the prediction of failure, fault, and defect-proneness as the space system software becomes complex. One of the most active areas of recent research in ML has been the use of ensemble classifiers. How ML techniques (or classifiers) could be used to predict software faults in space systems, including many aerospace systems is shown, and further use ensemble individual classifiers by having them vote for the most popular class to improve system software fault-proneness prediction. Benchmarking results on four NASA public datasets show the Naive Bayes classifier as more robust software fault prediction while most ensembles with a decision tree classifier as one of its components achieve higher accuracy rates.

Defence Science Journal, 2011, 61(4), pp.306-316, DOI:http://dx.doi.org/10.14429/dsj.61.1088

Author Biography

Bhekisipho Twala, Department of Electrical and Electronic Engineering Science, University of Johannesbug
Prof (Dr) Bhekisipho Twala received his BA (Economics and Statistics) from University of Swaziland, in 1993; MSc(Computational Statistics) from Southampton University, in 1995, and PhD (Machine Learning and Statistics) from the Open University, in 2005. Currently working as a Professor for Artificial Intelligence and Statistical Science at the Department of Electrical and Electronic Engineering Science, University of Johannesbug, South Africa. Currently involved in developing novel and innovative solutions (using AI technologies) to key research problems in the field of electrical and electronic engineering science. His broad research interests include multivariate statistics, classification methods, knowledge discovery and reasoning with uncertainty, sensor data fusion and inference, and the interface between statistics and computing.
Published
2011-07-18
How to Cite
Twala, B. (2011). Predicting Software Faults in Large Space Systems using Machine Learning Techniques. Defence Science Journal, 61(4), 306-316. https://doi.org/10.14429/dsj.61.1088