Software Engineering Framework for Software Defect Management Using Machine Learning Techniques with Azure

Abstract

TheAzure presence of bugs in a software release has become inevitable. The loss incurred by a company due to the presence of bugs in a software release is phenomenal. Modern methods of testingTesting and debugging have shifted focus from ‘detecting’ to ‘predicting’ bugs in the code. The existing models of bug prediction have not been optimized for commercial use. Moreover, the scalability of these models has not been discussed in depth yet. Taking into account the varying costs of fixing bugs, depending on which stage of the software development cycle the bug is detected in, this chapter uses two approaches—one model which can be employed when the ‘cost of changing code’ curve is exponential and the other model can be used otherwise. The cases where each model is best suited are discussed. This chapter proposes a model that can be deployed on a cloud platform for software development companies to use. The model in this chapter aims to predict the presence or absence of a bug in the code, using machine learningMachine learning classification models. Using Microsoft Azure’sMicrosoft Azure machine learning platform, this model can be distributed as a web service worldwide, thus providing bug predictionBug Prediction as a service (BPaaS)Bug Prediction as a service.