Software data Reduction Orders with Bug Prediction using Machine Learning

Patnala Lavanya, Murali Krishna Vasantha

Abstract


Software Bug Prediction is an esteemed issue that has occurred in software development and within the process of maintenance, which concerns with the success of overall software. This can be because in earlier innovate prediction of the software faults improves the software quality, reliability, efficiency and reduces the software cost. Bug-prediction techniques are aiming towards prediction of the software modules that are faulty in order that it can be beneficial within the upcoming phases of software development. Difference performance criteria are being employed so as to spice up the performance of the already existing ways. However, the most in performing the prediction of the software faults is ignored constantly. Classification is that the most used technique that's getting used for the exclusion of faulty from non-faulty modules. The work which is completed previously under this subject has been applied using different techniques. However, it's challenging task in developing robust bug prediction model and lots of approaches are proposed within the literature. This project represents a software bug prediction model by machine learning (ML) algorithms.


References


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