Aug 2026· Frontiers in Emerging Multidisciplinary Sciences· 0 citations
TL;DR
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.
Abstract
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.
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
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
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.
Dinesh Perera, Nethmi Fernando· International Journal of Nex...· 0 citations
The rapid adoption of cloud-native architectures has fundamentally transformed software engineering practices by introducing highly distributed, scalable, and dynamically evolving application environments. While cloud-native applications offer flexibility, elasticity, and resilience, they also introduce significant challenges related to software quality assurance, defect management, testing complexity, and system reliability. Traditional software quality engineering approaches often struggle to cope with the velocity and complexity of modern DevOps and continuous deployment ecosystems. In this context, Artificial Intelligence (AI) has emerged as a transformative technology capable of augmenting software quality engineering processes through automated testing, intelligent defect prediction, and reliability optimization mechanisms. This research investigates the integration of AI-driven methodologies within software quality engineering frameworks for cloud-native applications. The study examines machine learning-based automated testing strategies, predictive analytics models for software defect identification, and AI-enabled reliability enhancement techniques designed for distributed cloud environments. A conceptual framework is proposed that integrates automated testing, defect prediction, and reliability optimization into a unified quality engineering lifecycle. The research methodology employs a comprehensive literature analysis and comparative evaluation of contemporary AI-assisted software quality techniques. The findings indicate that AI-driven testing significantly improves test coverage and execution efficiency, while predictive defect analytics reduces maintenance costs and accelerates fault detection. Furthermore, reliability optimization algorithms enhance system availability and service resilience by proactively identifying performance anomalies and infrastructure failures. The study contributes a holistic perspective on AI-augmented software quality engineering and highlights emerging opportunities for intelligent quality assurance in cloud-native software ecosystems.
Vishwa Ramalingam· International Journal of Art...· 0 citations
Software reliability remains a major concern in modern software engineering due to the increasing complexity of software systems and the rapid pace of development. Software Failure Prediction (SFP) aims to identify fault-prone modules before deployment, enabling organizations to reduce maintenance costs and improve system quality. Artificial Intelligence (AI) and Machine Learning (ML) techniques provide data-driven approaches for analyzing software metrics, defect repositories, and execution logs to predict failures. This review examines AI/ML-based approaches for software failure prediction and efficiency optimization, with explicit attention to benchmark datasets, dataset quality, preprocessing, class imbalance, feature selection, model families, validation strategies, and evaluation measures. Representative NASA and PROMISE/Jureczko datasets are characterized in terms of software-unit type, metric families, binary defect labels, and imbalance. The review emphasizes Precision, Recall, F1-score, ROC-AUC, and MCC in addition to accuracy and compares traditional ML, ensemble, and deep-learning approaches. It further discusses missing data, concept drift, and cross-project prediction as key factors affecting real-world generalization. The analysis indicates that no single model is universally optimal; robust software failure prediction requires dataset-aware preprocessing, leakage-safe validation, imbalance-aware evaluation, and an explicit trade-off among predictive performance, computational efficiency, and interpretability.
Prasad Mathapati, S. G. Gollagi, Zebashireen Fahim Shaikh· International journal of res...· 0 citations
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