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MODEL FOR SOFTWARE ERRORS PREDICTION USING MACHINE LEARNING TO IMPROVE THE SOFTWARE RELIABILITY
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Kotaiah bonthu1,Raees Ahmed Khan2, Muralidhar Vejendla3
*1, babasaheb bhimrao ambedkar university, Indiababa, Email : kotaiah.bonthuklce@gmail.com
2, babasaheb bhimrao ambedkar university, India, Email : khanraees@yahoo.com
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Abstract
.The Software projects become critical systems now a days. Measuring software reliability in a continuous and disciplined manner leads to accurate estimation of project costs and schedules, and improving product and process qualities. Also, detailed analysis of software metric data gives important clues about the locations of possible errors in a programming code. The objective of this paper is to establish a method for identifying software errors using machine learning methods. We used machine learning methods to construct a two step model that predicts potentially modules with errors within a given set of software modules with respect to their metric data by using Artificial Neural Networks. The data set used in the experiments is organized in two forms for learning and predicting purposes; the training set and the testing set. The experiments show that the two step model enhances error prediction performance to improve the Software Reliability.
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Keywords
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Machine Learning Techniques; Software Reliability; Software Error Prediction; Artificial Neural Networks
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URL: http://dx.doi.org/10.7321/jscse.v3.n3.47
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