Binary Class Classification of Software Faults in Software Modules using Popular Machine Learning Techniques
Devika S1, Lekshmy P L2
1Devika S*, Department of Computer Science and Engineering, LBS Institute of Technology for Women.
2Lekshmy P L, Department of Computer Science and Engineering, LBS Institute of Technology for Women.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 13, 2020. | Manuscript published on April 15, 2020. | PP: 14-18 | Volume-6, Issue-6, April 2020. | Retrieval Number: F1221046620/2019©BEIESP | DOI: 10.35940/ijisme.F1221.046620
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Software engineering is an important area that deals with development and maintenance of software. After developing a software, it is always important to track its performance. One has to always see whether the software functions according to customer requirements. To ensure this, faulty and non- faulty modules must be identified. For this purpose, one can make use of a model for binary class classification of faults. Different technique’s outputs differ in one or the other way with respect to the following: fault dataset used, complexity, classification algorithm implemented, etc. Various machine learning techniques can be used for this purpose. But this paper deals with the best classification algorithms available till date and they are decision tree, random forest, naive bayes and logistic regression (tree-based techniques and bayesian based techniques). The motive behind developing such a project is to identify the faulty modules within a software before the actual software testing takes place. As a result, the time consumed by testers or the workload of the testers can be reduced to an extent. This work is very well useful to those working in software industry and also to those people carrying out research in software engineering where the lifecycle of development of a software is discussed.
Keywords: Software fault prediction, Decision Tree regression, software fault dataset, Machine Learning.