COMPUTERIZED SOFTWARE QUALITY EVALUATION WITH NOVEL ARTIFICIAL INTELLIGENCE APPROACH

Abstract

Software quality assurance has grown in importance in the fast-paced world of software development. One of trickiest parts of creating and maintaining software is predicting how well it will perform. The term "computer evaluation" refers to use of advanced AI techniques in software quality assurance, replacing human evaluations and paving the way for a new era in software evaluation. We proposed Hybrid Elephant herding optimized Conditional Long short-term memory (HEHO-CLSTM) to estimate Software Quality Prediction. Software quality prediction and assurance has grown in importance in ever-changing world of software development. Software quality prediction encompasses a wide range of activities aimed at improving the quality of software systems via the use of data-driven approaches for prediction, evaluation and enhancement. We have collected Software Defects data and we feature extracted the attributes using linear discriminant Analysis (LDA). The suggested system improves the accuracy, AUC and Buggy instance compared with the current methods.

Keywords

• artificial intelligence (ai)
• software quality
• software quality assurance (sqa)
• software fault prediction (sfp)
• software quality prediction