Enhancing Software Reliability and Fault Detection Using Hybrid Brainstorm Optimization-Based LSTM Model

Abstract

An essential attribute of software quality is software reliability. To achieve higher reliability, the testing phase with detected and corrected flaws is incorporated in the software development. The fault correction process (FCP) includes the fault detection process (FDP) to develop the software reliability growth model (SRGM). This is difficult to integrate because due to several reasons, including the effects of staffing levels and the interdependence of faults. It limits the applicability of the analytical model. Because of the adoption of data-driven methodologies such as Artificial Intelligence (AI) technology, no precise FCP and FDP assumptions are necessary. In this article, we proposed a hybrid long short-term memory (LSTM) with BrainStorm Optimization and Late Acceptance Hill Climbing (BSO-LAHC) algorithm of a stepwise prediction model for software fault detection and correction. The fault detection and correction procedure has great influence by considering the testing effort. While compared to the existing methods, the proposed hybrid with the BSO-LAHC algorithm demonstrated superior results by using Firefox and bug tracking system Bugzilla datasets. The proposed model’s effectiveness is confirmed via empirical study. Based on the Bugzilla and firefox datasets, the proposed mean square error performance is 1.92 and 21.44 respectively. Additionally, the proposed method is less expensive and takes less time to execute. In Bugzilla version 5.0.4, releases 2 and 3 had a determination coefficient of 99.2% and 98.9%, respectively. The FCP is 27% more effective than previous approaches, and the FDP is 32% more effective.