An Empirical Study of Classification Models Using AUC-ROC Curve for Software Fault Predictions

Authors

  • Dr. M. Aruna Safali  Associate Professor, CSE, Dhanekula Institute of Engineering and Technology, Vijayawada, Andhra Pradesh, India

Keywords:

AUC, ROC, TPR, FPR, KNN

Abstract

Software bug prediction is the process of identifying software modules that are likely to have bugs by using some fundamental project resources before the real testing starts. Due to high cost in correcting the detected bugs, it is advisable to start predicting bugs at the early stage of development instead of at the testing phase. There are many techniques and approaches that can be used to build the prediction models, such as machine learning. We have studied nine different types of datasets and seven types of machine learning techniques have been identified. As for performance measures, both graphical and numerical measures are used to evaluate the performance of models. A few challenges exist when constructing a prediction model. In this study, we have narrowed down to nine different types of datasets and seven types of machine learning techniques have been identified. As for the performance measure, both graphical and numerical measures are used to evaluate the performance of the models. There are a few challenges in constructing the prediction model. Thus, more studies need to be carried out so that a well-formed result is obtained. We also provide a recommendation for future research based on the results we got from this study.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-02-28

Issue

Volume 9, Issue 1, January-February-2023

Section

Research Articles

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Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. M. Aruna Safali, " An Empirical Study of Classification Models Using AUC-ROC Curve for Software Fault Predictions" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 1, pp.261-271, January-February-2023.