Intelligent Software Testing for Test Case Analysis Framework Using ChatGPT with Natural Language Processing and Deep Learning Integration

Abstract

Effective testing scenarios are necessary to guarantee the dependability and Caliber of software. Conventional techniques for creating these scenarios frequently involve a great deal of manual labor and might not fully cover all software requirements. In order to improve the automation and Caliber of software testing scenario development, this study investigates the combination of Natural Language Processing (NLP) and Deep Learning (DL) approaches with ChatGPT, an advanced language model by OpenAI. The suggested method automatically creates a variety of thorough test cases by utilizing ChatGPT's sophisticated natural language processing capabilities. To evaluate the model's capacity to comprehend intricate software requirements and generate pertinent situations, a comparison between conventional scenario-generation techniques and those improved by ChatGPT is carried out. The process is divided into four stages: Requirement parsing, in which natural language software requirements are analyzed and interpreted using NLP models; scenario generation, in which a transformer-based model is used to generate testing scenarios that are logical and appropriate for the environment. an automation pipeline that uses Hugging Face Transformers and Python to speed up the scenario generating process and evaluation metrics that evaluate the created scenarios according to requirement coverage and relevance coherence. The effectiveness of this method is illustrated through a case study on evaluating an Optical Character Recognition (OCR) system for private documents. The results show that integrating ChatGPT with NLP and DL greatly enhances the depth of testing scenarios, speeds up the generation process, and lowers manual labor. The potential of ChatGPT to automate and optimize software testing is demonstrated in this study, providing a more effective and flexible solution for a variety of testing scenarios.