A comprehensive study of AI in test case generation: Analysing industry trends and developing a predictive model

Abstract

For decades, it has been proven that software testing is a vital component of the software development lifecycle and ensures reliability, functionality, and performance. However, traditional test case generation methods face challenges such as high time and resource demands and susceptibility to human error, especially in large-scale and complex software systems.
The paper provides an extensive exploration of artificial intelligence (AI) applications in software test case generation, focusing on analyzing current industry practices and creating a predictive model designed to optimize this critical aspect of software quality assurance. To address these limitations, the adoption of AI techniques for automating and improving test case generation has gained significant traction. This research pursues two key objectives: first, to thoroughly analyze existing AI-driven testing techniques and strategies for test case generation through an extensive review of academic literature, industry reports, and case studies. This analysis delves into search-based, machine-learning approaches and natural language processing (NLP) techniques. Furthermore, it evaluates their application across different testing levels—unit, integration, system, and acceptance testing—and software domains like web applications, mobile platforms, embedded systems, and safety-critical environments. The analysis highlights current industry practices and identifies areas where AI can significantly enhance efficiency and effectiveness in software testing.
The second objective involves designing and implementing a predictive model for optimal test case generation using advanced AI techniques. The model employs machine learning frameworks, including deep learning architectures like recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer-based models. By training on diverse datasets, including historical test data, software requirements specifications (SRS), source code, and execution logs, the model ensures broad applicability. Its focus includes maximizing test coverage, minimizing test suite size, and prioritizing test cases based on their fault-revealing potential, making the testing process more efficient and effective. The architecture accommodates various input formats, enabling a comprehensive, context-aware test case generation process.
This research makes a significant contribution to software testing by offering a detailed analysis of AI-driven test case generation practices and introducing a robust predictive model to address existing challenges. The findings present practical solutions for software development professionals and researchers, improving software quality, reducing costs, and accelerating development timelines.