Software Defect Prediction: State of the Art Survey
Swadesh Kumar1, Rajesh Kumar Singh2, Awadhesh Kumar Maurya3
1Swadesh Kumar*, Lecturer, Department of Information Technology, Mahamaya Polytechnic of Information Technology, Chandauli (U.P), India.
2Rajesh Kumar Singh, Assistant Professor, Department of Information Technology, Dr. R. L. Avadh University, Ayodhya (U.P), India.
3Awadhesh Kumar Maurya, Assistant Professor, Department of Information Technology, Dr. R. L. Avadh University, Ayodhya (U.P), India.
Manuscript received on 27 May 2022. | Revised Manuscript received on 02 June 2022. | Manuscript published on 30 June 2022. | PP: 32-35 | Volume-11 Issue-7, June 2022. | Retrieval Number: 100.1/ijitee.G99930611722 | DOI: 10.35940/ijitee.G9993.0611722
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© The Authors. 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 has evolved into a critical component in today’s world. The quantity of faults in a software product is connected to its quality, which is also restricted by time and cost. In terms of both quality and cost, software faults are costly. The practice of tracing problematic components in software prior to the product’s launch is known as software defect prediction. Defects are unavoidable, but we should strive to keep the number of defects to a bare minimum. Defect prediction results in shorter development times, lower costs, less rework, higher customer satisfaction, and more dependable software. As a result, defect prediction procedures are critical for achieving software quality and learning from prior errors.In this study, we conduct a review of the literature from the last two decades and look into recent advancements in the field of defect prediction. 
Keywords: Attribute Selection, Defect Prediction, Software Quality, Defect Detection.
Scope of the Article: Regression and Prediction