A Probabilistic and Deterministic based Defect Prediction through Defect Association Learning in Software Development
Rohini B. Jadhav1, Shashank D. Joshi2, Umesh G. Thorat3
1Prof. R.B.Jadhav pursued Bachelor of Computer Engineering from Bharati Vidyapeeth Deemed University College of Engineering, Pune, India.
2Prof. Dr. S. D Joshi completed PhD in Computer Engineering from Bharati Vidyapeeth deemed to be University, Pune, India.
3Mr. Umesh Thorat pursued Bachelor of Mechanical Engineering from Sinhagad College of Engineering, Pune, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4264-4270 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8778038620/2020©BEIESP | DOI: 10.35940/ijrte.F8778.038620
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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 development is a multitasking activity by an individual or group of team. Every one activity engages diverse tasks and complication. To accomplish quality improvement, it is essential to make every activity task free of defects. But locating and correcting defects is more expensive and time-intense. In the past, many potential methods have been used to predict potential drawbacks in the program based on the theory of probability facts. Because the probability method applies a random variable and probability distributions to find a solution, the result is always in a possible range that can be true at some time or may also be wrong. Therefore, an additional calculation method coupled with the probability of making it more accurate and new in predicting the defect of the program. In this paper, we propose a Probabilistic and Deterministic based Defect Prediction (PD-DP) through Defect Association Learning (DAL). The PD-DP implements a Probability association method (PAM) and Deterministic association method (DAM) to predict the software defect accurately in software development. The experimental evaluation of the PP-DP in compare to existing prediction methods shows enhancement in prediction accuracy.
Keywords: Software Defect Prediction, Probabilistic, Deterministic, Association Learning.
Scope of the Article: Deep learning.