2025· International Journal of Emerging Research in Engineering and Technology· 0 citations
TL;DR
A new intelligent framework for automating the end-to-end QA process using machine learning techniques, autonomous agents and feedback optimization is introduced, which will have adaptive components which will allow the system to learn and become better at testing through telemetry, test results and previously occurred defects patterns.
Abstract
In modern software applications, especially complex and evolving ones, intelligent automation frameworks for QA processes play a crucial role. Static Test Cases and Rule-Based Automation are insufficient in terms of scalability, flexibility and defect prediction in CI/CD centric environments. In this paper, a new intelligent framework for automating the end-to-end QA process using machine learning techniques, autonomous agents and feedback optimization is introduced. The frame7work will have adaptive components which will allow the system to learn and become better at testing through telemetry, test results and previously occurred defects patterns. Also, it will provide orchestration abilities for distributed testing and dynamic high risk component identification in complex software systems. This paper introduces three major contributions: First, an adaptive test case generation technique based on intelligent monitoring of system's behavior, Second, defect prediction module based on reinforcement learning to dynamically update the risk profile, Third, a decentralized multi-agent collaboration framework for effective test automation. The experiments show clear improvement in percentage of detected defects; reduced cost of maintenance and shorter release cycle compared to traditional automation frameworks.
It is concluded 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 dynamic behavior of contemporary software systems have created substantial challenges for conventional test automation approaches. Traditional automation frameworks generally depend on predefined scripts, deterministic rules, and manually maintained test artifacts, which limits their adaptability when applications, interfaces, requirements, and execution environments change continuously. This research and review paper examines the conceptual foundations of Artificial Intelligence (AI)-based test automation frameworks for next-generation software quality engineering by integrating perspectives from human-like computing, functionalism, semantic representation, conceptual spaces, distributed intelligence, and machine-intelligence measurement. The study develops a conceptual framework in which AI-supported test automation is organized around five interconnected capabilities: intelligent requirement interpretation, semantic test modeling, adaptive test generation, autonomous execution and maintenance, and predictive quality-risk analysis. The theoretical synthesis indicates that human-like and symbolic approaches can provide interpretability and structured reasoning, whereas conceptual-space and semantic approaches can support contextual representation of software behavior. Distributed intelligence perspectives further suggest opportunities for scalable quality engineering across heterogeneous testing environments. The analysis also incorporates AI-driven project-risk prediction as a complementary decision-support mechanism for prioritizing testing resources and identifying high-risk software components. Findings indicate that the most promising next-generation architecture is not a completely autonomous testing system but a human-centered, adaptive framework combining machine intelligence with explainable representations and continuous feedback. The paper identifies important limitations involving semantic ambiguity, model reliability, maintenance complexity, and the absence of universal evaluation criteria. It concludes that AI-based test automation can substantially strengthen software quality engineering when intelligence is integrated as an adaptive reasoning layer rather than treated merely as an alternative mechanism for script generation.
Priyanka Sharma· American Journal Of Applied...· 0 citations
The evolution from reactive to proactive AI systems represents a paradigm shift in software engineering, enabling autonomous agents to anticipate requirements, plan complex workflows, and execute multi-step development tasks without human intervention. This paper presents a novel multi-agent architecture for autonomous code generation and software maintenance in enterprise environments. Our framework integrates specialized AI agents for requirements analysis, code synthesis, testing, and deployment, coordinated through a hierarchical planning system with adaptive decision-making capabilities. The system employs reinforcement learning to optimize task allocation, learn from code review feedback, and improve over time. Experimental evaluation on real-world enterprise codebases demonstrates that our proactive agent system achieves 87.3% automated issue resolution, reduces bug fix latency by 62%, and maintains code quality metrics comparable to human developers. The framework successfully handles complex refactoring tasks, security vulnerability remediation, and feature implementation with minimal human oversight, representing a significant advancement toward fully autonomous software engineering workflows.
Saher Elsayed, Samer Abubaker, M. Ali et al.· Annual International Compute...· 0 citations
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.
Guilherme Martins, Nelson N. Tenório, Jorge Bernardino· Big Data and Cognitive Compu...· 1 citation
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
The application of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized quality engineering by making intelligent automation, predictive analytics and adaptive software testing possible. However, traditional quality engineering techniques can often prove difficult to implement, scale, integrate and control defects in modern software environments. In this study, we present a novel quality engineering transformation framework through the integration of machine learning and automated quality assurance processes to increase software reliability, reduce test time and improve prediction capabilities for software defects. The AI-enabled framework presented in the research uses intelligent analytics to perform adaptive risk assessment, automated optimization of test cases and quality monitoring at all stages of software engineering. Furthermore, the framework makes use of the behavioral learning model to optimize testing techniques based on previous project experiences and knowledge. The benefits of the experiment in comparison with traditional quality engineering techniques can be easily illustrated with the help of experimental findings; they include increased efficiency and accuracy in detecting software defects, resource optimization, faster test processing and improved overall software quality.
K. Ramamurthy· International Journal of Art...· 0 citations
