Software Fault Prediction Exploration Using Machine Learning Techniques
P. Patchaiammal1, R. Thirumalaiselvi2

1P. Patchaiammal, Research Scholar, Bharath University, Chennai (Tamil Nadu), India.
2R. Thirumalaiselvi, Assistant Professor, Department of Computer Science, Govt. Arts College (Men), Nandanam, Chennai (Tamil Nadu), India.
Manuscript received on 23 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 07 May 2019 | PP: 109-113 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1022376S19/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Fault prediction is one of the major activity of quality assurance. Fault prediction plays a significant role in the reduction of software cost and time. Even though, there are so many prediction techniques that are available in software engineering there is a need for stable software fault prediction methodology. In this research work, four types of Machine Learning techniques such as supervised, unsupervised, semi-supervised, and reinforcement learning are discussed. According to the study, to predict the fault, a fusion of classification and reduction Machine Learning technique is necessary. This report also introduces hypothesis set for fault prediction taxonomy.
Keywords: Machine Learning (ML), Software Development Methodologies, Hypothesis, Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning.
Scope of the Article: Machine Learning