Machine Learning Techniques to Predict Defects by using Testing Parameters
Prasanth Yalla1, Venkata Naresh Mandhala2, Valavala Abhishiktha3, Chitturi Saisree4, Kandepi Manogna5
1Prasanth Yalla, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, A.P., India.
2Venkata Naresh Mandhala, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, A.P., India.
3Valavala Abhishiktha, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, A.P., India.
4Chitturi Saisree, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, A.P., India.
5Kandepi Manogna, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, A.P., India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7829-7834 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5396118419/2019©BEIESP | DOI: 10.35940/ijrte.D5396.118419
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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: Since ages, the software development plays a very crucial role in the arena of software engineering. An important part here is to believe that Artificial Intelligence and Machine Learning also started its way. In the process, several metrics were analyzed, composed and some predictions were made. These predictions are very much useful to analyze the defects based on machine learning. This can be done by using various system test parameters. We found certain techniques which are used to estimate the defects based on various aspects. These features are retrieved right from the inception of the software development. In this project, we present an advance view on wide variety of Machine Learning approaches, along with different capable areas of the defects by taking their parameters.
Keywords: Defect Prediction, Machine Learning, Defects, Metrics, Accuracy.
Scope of the Article: Machine Learning.