AI-Powered Software Testing a Novel Framework for Enhancing Bug Detection and Code Reliability
Keywords:
AI-powered testing, Bug detection, Code reliability, Software testing frameworks, Machine learning, Anomaly detection, Software quality assurance, Automated testingAbstract
The complexity of existing software is shown in the requirement for specific test protocols to ensure robustness, functionality, and performance. If traditional software testing methods are unable to cover the existing defects and weak parts of such a large software pool, then it is a limit of the traditional methods and thus, new methods are required to enable detecting such defects and weaknesses inside the software pool. This article suggests a new framework within the area of software testing based on artificial intelligence (AI). This is also included since one of the goals of the framework is to facilitate the development of tools that can be used to detect bugs as well as increase the robustness of the code. Extending the above architecture to natural language processes, machine learning and machine learning models of aberrant behaviour allows the intelligent solution of the problem of automated testing. In the study, a comparison is made between the proposed solution and existing testing practices citing benefits in terms of efficiency, accuracy, and the proportion of defects that are addressed. The frameworks work in practical application which is evidenced by the outcome of the case studies and controlled tests filling in a solution that is effective for the software problems that are rampant in contemporary society.
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