Improving Intelligence Metrics using Frequency Domain Convolutions for Improving Bug Prediction

Background: The novelty of the work lies in the formulation of these frequency-based generators, which reflects the lowest level of information loss in the intermediate calculations. The core idea behind the approach presented in this work is that a module with complex logic involved may have more probability of bugs. Software defect prediction is the area of research that enables the development and operations team to have the probability of bug proneness of the software. Many researchers have deployed multiple variations of machine learning and deep learning algorithms to achieve better accuracy and more insights into predictions. Objective: To prevent this fractional data loss from different derived metrics generations, a few optimal transformational engines capable of carrying forward formulations based on lossless computations have been deployed. Methods: A model Sodprhym has been developed to model refined metrics. Then, using some classical machine learning algorithms, accuracy measures have been observed and compared with the recently published results, which used the same datasets and prediction techniques. Results: The teams could establish watchdogs thanks to the automated detection, but it also gave them time to reflect on any potentially troublesome modules. For quality assurance teams, it has therefore become a crucial step. Software defect prediction looks forward to evaluating error-prone modules likely to contain bugs. Conclusion: Prior information can definitely align the teams with deploying more and more quality assurance checks on predicted modules. Software metrics are the most important component for defect prediction if we consider the different underlying aspects that define the defective module. Later we deployed our refined approach in which we targeted the metrics to be considered.