Aug 2026· International Journal of Next-Generation Engineering and Technology· 0 citations
TL;DR
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.
Abstract
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.
Agile software development emphasizes rapid iteration, continuous integration, frequent releases, and incremental delivery, making regression testing a central software quality challenge. Conventional regression testing approaches often depend on manually selected test suites, static prioritization rules, and repeated execution of tests that provide limited incremental fault-detection value. This paper develops a conceptual intelligent regression testing framework that applies artificial intelligence (AI) techniques to test selection, prioritization, execution, failure classification, and continuous learning within Agile development pipelines. The methodological foundation combines supervised learning, representation learning, historical test-result analysis, change-impact assessment, and feedback-driven optimization. Because the supplied literature primarily concerns AI-based detection and classification in biomedical signal-processing applications rather than software testing, the paper explicitly treats these studies as methodological evidence for transferable AI patterns rather than direct empirical evidence for regression testing. The framework consequently emphasizes feature extraction, automated classification, adaptive prediction, and real-time decision support. A conceptual evaluation indicates that AI-assisted regression testing can improve the alignment between code changes and test execution priorities, reduce redundant execution, and create feedback loops capable of adapting to changing Agile projects. However, model drift, insufficient historical data, explainability, false prioritization, and integration complexity remain significant constraints. The analysis positions intelligent regression testing as an adaptive decision-support layer rather than a complete replacement for conventional testing practices.
Tomas Kazlauskas· The American Journal of Engi...· 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 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, but model reliability depends on historical defect data, feature quality, distributional stability, explainability, and integration with existing development pipelines.
Haruto Tanaka, Yuki Nakamura· Frontiers in Emerging Multid...· 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...· 2 citations
The proposed framework integrates AI-assisted test case optimization with automated web testing and employs AI-based models to prioritize and optimize test case execution, provides visual analytics on performance trends, and supports seamless integration with CI/CD tools like Jenkins and GitHub Actions.
Amireddy Sainath Reddy, N. N. Kumar· International Journal for Re...· 0 citations
Cloud native applications are built and released through continuous integration and continuous delivery pipelines that call for fast feedback and frequent deployment. Traditional test automation, which depends on scripts that are written once and maintained by hand, was never really designed for this pace of change. When a microservice is redeployed, refactored, or scaled several times a day, the scripts that were written to test it tend to fall behind, and quality assurance teams end up spending more time repairing tests than writing new ones. This paper looks at how artificial intelligence can be used to build a test automation framework that adjusts itself as the application changes, rather than breaking every time something moves. We describe a framework, referred to here as AutoQA CN, that brings together three capabilities: automatic generation of new test cases from API specifications and usage data, self-healing of broken test scripts through similarity-based and learned matching, and risk-based prioritisation of test execution using a reinforcement learning agent. The framework is built to sit alongside existing continuous integration tooling in a containerised environment rather than replace it outright. We evaluate the approach through an illustrative case study across three representative microservice scenarios and compare it with a conventional scripted automation baseline on three measures: maintenance effort, time to detect faults, and pipeline throughput. The results point to meaningful reductions in maintenance work and faster fault detection, along with a modest gain in throughput. We close by discussing where these gains are likely to hold up in practice, where the approach still needs human oversight, and what would need to happen for a framework like this to be trusted in production settings.
Ihor Diakonov· Journal of Artificial Intell...· 0 citations
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