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Optimized Fuzzy Classifier Approach for Predicting Defects

Kaushalya Thopate1 Diya Shaikh2 Muaz Shaikh3 Pushkraj Shahane4
1Asst Prof. Department of Computer Science Engineering Vishwakarma Institute of Technology, Pune, Maharashtra, India. 234 Students, Department of Computer Science Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India.

Published Online: November-December 2024

Pages: 07-10

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Abstract

The product and process quality in developing software is characterized by software attributes. Certain attributes of quality of software like the density of defect and rate of failure form the measures of the software product and its development process. Care is taken to utilize software metrics and code level measurement and defect data in the building of defect predictors or software quality models. The assumption is the software metrics can gather the end product quality. In general, the building of software metric models takes place through data that defect which is collected from a system release that is developed previously or software projects that are similar. By validating these models, fault proneness is readily predicted of program modules that are development currently. The available quality improvement resources can be justified through the application of a low-quality Fault-Prone (FP) prediction to those programs. High software reliability and quality are achieved by using the available resources effectively.

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