Adaptive cat optimisation with attention based Bi-LSTM for automatic software bug detection

The detection of software bugs is a critical issue in the software maintenance and development process because it is connected to all software successes. Therefore, earlier software bug detection is essential for improving software's efficiency, dependability, quality, and cost. Furthermore, accurate software bug prediction is a critical and challenging task. This article thus develops the effective software bug prediction model. The pre-processing, feature selection, and bug detection phases of the proposed model are the most important ones. The input bug dataset is initially pre-processed. Remove instances of duplicate data from the dataset during pre-processing. The feature selection is carried out by the adaptive cat swam optimisation algorithm (ACS) following the pre-processing step. At last, the proposed approach uses a Bi-long short term memory (Bi-LSTM) for bug expectation. Bug prediction is done with the promise and the NASA dataset. Based on accuracy, the proposed model performs better than the simulation results.