Aug 2026· International journal of data science and machine learning· Vol 6, pp. 112-120· 0 citations
TL;DR
The analysis indicates that machine learning can shift test automation from static execution toward adaptive quality intelligence, however, model drift, insufficient representative test data, explainability, false positives, computational overhead, and continuous maintenance remain significant constraints.
Abstract
Continuous software quality engineering requires testing approaches that can operate with high frequency, adapt to changing software behavior, and provide actionable quality information without becoming a bottleneck in the delivery pipeline. Conventional automation improves execution speed but remains constrained when test selection, prioritization, failure interpretation, and maintenance depend heavily on manually encoded rules. This research and review paper examines a machine learning-driven approach to test automation in which learning models are integrated with test generation, execution prioritization, defect prediction, failure classification, and continuous feedback mechanisms. The methodological framework is developed by synthesizing the supplied literature on machine learning, deep learning, temporal modeling, generative adversarial networks, and sequence prediction, together with the compulsory software quality engineering study by Ramamurthy (2023). Although most of the supporting studies originate from domains such as weather forecasting, radar analysis, and medical image segmentation, their methodological contributions provide transferable principles for software testing, particularly temporal dependency modeling, automated feature learning, synthetic-data generation, and predictive decision-making. The analysis indicates that machine learning can shift test automation from static execution toward adaptive quality intelligence. However, model drift, insufficient representative test data, explainability, false positives, computational overhead, and continuous maintenance remain significant constraints. The proposed framework therefore emphasizes a closed feedback loop in which machine learning supports, rather than completely replaces, deterministic testing and human quality judgment.
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
Test automation is vital for software release in modern times, it often results in false positives due to failed tests, which leads to the waste of precious engineer's time and, hence, loss of trust in the system. The proposed research project seeks to design a predictive model that can estimate the possibility of false positive failure before test automation. Our model was trained using the data on 12,400 test cases from 840 automated tests that were conducted during 18 months of continuous integration process. In total, there were 42 features selected as input data for training – they include data from logs, information about code changes, environmental factors, and failure history. Five machine learning algorithms were tested, XGBoost algorithm being the most successful with accuracy being 91.2%, while F1-score equalled 90.5%. Testage, code churn, and failure rate were the most important features. After deploying our tool into production CI pipeline, we noticed a reduction inthe number of test cycles of up to 34% as well as increase of defect detection speed by 60%.
Gitanjali Kumari, M. K. Shukla, Nawnit Kumar et al.· 2026 6th International Confe...· 0 citations
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.
Denis Kazlauskas· The American Journal of Engi...· 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
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.
Prof. Prasad Mathapati, Dr.Shantappa G Gollagi, Prof. Zebashireen Fahim Shaikh· International journal of res...· 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