Jul 2026· Big Data and Cognitive Computing· Vol 10, pp. 233· 1 citation· 50 references
TL;DR
An application-focused review of 35 selected empirical studies focusing on the use of AI during software testing, based on PRISMA guidelines, reveals that large language models, machine learning, and computer vision can significantly improve testing efficiency.
Abstract
The rapid evolution of software complexity demands more efficient and autonomous testing mechanisms. Artificial intelligence (AI) has emerged as a solution to the limitations of traditional manual testing in software development, which is time-consuming, prone to human error, and unable to scale with the increasing size and complexity of modern software systems. In this context, this paper presents an application-focused review of 35 selected empirical studies focusing on the use of AI during software testing, based on PRISMA guidelines. We introduce a comprehensive taxonomy categorizing current research into six core fields, including test case generation, defect prediction, and AI model verification. The analysis reveals that large language models, machine learning, and computer vision can significantly improve testing efficiency. Key findings demonstrate that AI can autonomously repair broken test scripts, generate robust synthetic data, enable codeless web testing, and accurately predict system defects before execution. Furthermore, advanced techniques such as reinforcement learning and deep learning successfully validate complex environments, including cloud robotics and quantum software. However, our qualitative and quantitative synthesis also highlights that challenges, such as generative AI “hallucinations” and the brittleness of Continuous Integration and Continuous Deployment (CI/CD) integration, persist. Ultimately, this review proposes a tailored research roadmap for robust industrial adoption, showing that AI is changing the way software is tested, shifting it from a predominantly reactive and static activity toward a proactive, intelligence-driven discipline.
In today's fast-changing software landscape, the need for effective software testing has grown increasingly vital for guaranteeing quality, reliability, and security in the software-driven world. With the increasing capabilities of Artificial Intelligence (AI), it has the promise of overcoming the known shortcomings of traditional testing methods that are still largely manual, rule-based, and reactive in their approach to quality assurance. The scope of research conducted on AI tools in testing has covered diverse areas such as machine learning, deep learning, natural language processing, and generative AI, showing the potential of these tools in different testing tasks, and identifying some ongoing challenges. In this paper, we'll discuss how traditional and AI-powered tools are used in four critical areas test management, test case management, defect management, and version management and our study results prove that AI-powered testing is better than traditional testing in each of these areas.
Rajat Sharma, Shahid Ali· VLSI & Embedded Systems...· 0 citations
The increasing complexity, scale, and dynamic behavior of contemporary software systems have intensified the limitations of conventional rule-based testing approaches. Artificial intelligence (AI), particularly machine learning, deep learning, reinforcement learning, and intelligent decision-making techniques, provides a basis for developing adaptive test automation systems capable of selecting actions, prioritizing test scenarios, learning from execution outcomes, and responding to changing system conditions. This research and review paper examines how AI-enabled decision-making principles can be conceptually transferred to automated software quality engineering. The analysis is based exclusively on the supplied literature, which primarily investigates intelligent decision-making, reinforcement learning, deep reinforcement learning, autonomous maneuvering, trajectory planning, and adaptive control in unmanned aerial vehicle environments. Although these studies are not directly concerned with software testing, their methodological foundations provide useful analogies for intelligent test selection, adaptive execution, test prioritization, and autonomous quality decision-making. The paper develops a conceptual AI-enabled software testing framework consisting of software-state representation, intelligent test generation, reinforcement-based test selection, adaptive execution, defect-oriented prioritization, and continuous feedback. The findings indicate that reinforcement-learning-based decision mechanisms are particularly relevant for environments where testing decisions must be repeatedly optimized under changing conditions. However, the transfer of these techniques requires careful treatment of differences between physical autonomous systems and software environments. The paper concludes that AI-enabled test automation can improve testing efficiency and adaptability when learning mechanisms are combined with controlled validation, risk-based decision criteria, and human oversight.
N. Gunasekara, Tharushi Senanayake· The American Journal of Inte...· 0 citations
The increasing complexity, scale, and release frequency of contemporary software systems have exposed limitations in conventional testing practices, particularly in exhaustive test execution, regression validation, and early defect identification. This research proposes an intelligent framework that integrates artificial intelligence (AI)-based test automation with software defect prediction to establish a proactive quality-engineering process. The proposed framework combines requirement and code analysis, automated test generation, execution prioritization, defect-risk estimation, feedback-driven model refinement, and quality reporting within a unified architecture. The methodological foundation is a conceptual synthesis of AI-driven test automation principles, with particular emphasis on automation, intelligent prioritization, and predictive quality assurance as discussed by Ramamurthy (2023). The supplied reference corpus also demonstrates how intelligent sensing, pattern recognition, resource optimization, and data-driven classification can conceptually inform automated quality-monitoring architectures, although most of these studies originate outside software testing. The proposed model therefore treats cross-domain evidence as methodological inspiration rather than direct empirical validation. The analysis indicates that combining predictive defect-risk scores with automated test selection can potentially reduce redundant testing, concentrate computational resources on high-risk software components, and improve feedback speed. However, model reliability depends on historical defect data, feature quality, distributional stability, explainability, and integration with existing development pipelines. The framework contributes a structured basis for AI-assisted software quality engineering while identifying empirical validation, benchmark datasets, and explainable prediction as priorities for future research.
Haruto Tanaka, Yuki Nakamura· Frontiers in Emerging Multid...· 0 citations
Modern software systems are characterized by continuous integration, frequent releases, heterogeneous architectures, and increasingly complex interaction patterns. These conditions place substantial pressure on conventional test-case design, particularly where manually authored tests struggle to achieve adequate coverage within constrained development cycles. This research examines an AI-enabled approach to test-case generation and optimization for modern software development by synthesizing evidence from studies concerning augmented reality, simulation-based learning, computational visualization, embedded-system monitoring, and AI-driven software quality engineering. The proposed methodology conceptualizes test generation as a pipeline comprising requirement interpretation, test-objective identification, candidate test generation, execution-oriented prioritization, redundancy reduction, and continuous optimization. Particular emphasis is placed on the relationship between intelligent automation and software quality engineering, where AI-driven frameworks can transform testing from a predominantly scripted activity into an adaptive quality-assurance process (Ramamurthy, 2023). The analysis indicates that AI can provide substantial benefits in generating diverse test scenarios, prioritizing high-value cases, and adapting test suites to changing software conditions. However, optimization effectiveness depends on the quality of requirements, training or heuristic signals, system observability, and validation mechanisms. The research therefore positions AI-enabled testing not as a replacement for engineering judgment but as an augmentation mechanism that improves scalability, coverage, and prioritization while retaining human oversight for critical decisions.
D. Perera, Nethmi Fernando· International Journal of Nex...· 0 citations
Software testing is moving away from rigid, hand-written scripts toward AI systems that can adapt on their own. This review traces how quality engineering has changed, from rule-based automation to self-adjusting test frameworks, and looks at the technology behind Autonomous Quality Agents: Large Language Models (LLMs) that generate code from requirements, Computer Vision that handles visual regression, and Reinforcement Learning that drives exploratory testing. It also examines two ongoing problems: the difficulty of understanding how AI models make decisions, and the extra work needed to keep older, script-based automation running. The review closes with a proposed framework for where autonomous software assurance is headed next. This proposed framework, termed Autonomous Quality Assurance (AQA), is organised around three layers, perception (visual and DOM-based sensing), cognition (LLM-driven reasoning and test generation), and governance (interpretability and verification), intended to give practitioners and researchers a shared structure for locating where a given tool or technique sits today and what would need to mature before autonomous testing can be trusted at industrial scale.
Vanshita Agarwal· International journal for ad...· 0 citations
Software bugs remain one of the most costly chal-lenge in software engineering, consuming significant development time and resources. Recent advances in Artificial Intelligence (AI), particularly deep learning and large language models (LLMs), have shown remarkable potential in automating the detection and repair of software errors. This study presents a comprehensive survey and comparative analysis of AI-based techniques for error detection and automated program repair (APR). We catego-rize existing approaches into traditional search-based methods, learning-based neural machine translation models, and emerg-ing LLM-based repair systems. We evaluate these techniques across standard benchmarks, including Defects4J and SWE-bench, comparing their effectiveness in terms of bugs fixed, patch correctness, and scalability. Our analysis reveals that while LLM-based approaches significantly outperform traditional methods in repair capability, challenges remain in patch correctness validation, computational cost, and generalization to real-world codebases. Our results show that LLM-based tools, such as ChatRepair, can correctly fix 114 out of 395 benchmark bugs at just $0.42 per fix, fixing 2.6× as many bugs as the best traditional method (a 165% increase). We discuss open challenges and propose future research directions toward more reliable AI-assisted software development.
A. Aladwani, Sultan Alsamaani, Turki Alrumaykhani et al.· International Journal of Adv...· 0 citations