Aug 2026· VLSI & Embedded Systems 2026· pp. 53-67· 0 citations· 21 references
TL;DR
How traditional and AI-powered tools are used in four critical areas test management, test case management, defect management, and version management is discussed and the study results prove that AI-powered testing is better than traditional testing in each of these areas.
Abstract
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.
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 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
Cloud-native technologies, which include microservices, containers, Kubernetes, and serverless computing, have been growing rapidly in recent times and have revolutionized the world of software development. Although the emergence of these technologies is beneficial in making software more scalable, flexible, and deployable, there exist certain difficulties that come about with them in software testing and security. Traditional methods of testing have found themselves ill-equipped to deal with such complex and dynamic environments, where artificial intelligence emerges as an ideal solution. The survey aims to study the role of AI-enabled test engineering strategies in creating secure cloud-native systems. This includes investigating the use of Machine Learning, Deep Learning, Natural Language Processing, and Reinforcement Learning in planning, testing, and deploying software. This survey will focus on various AI-enabled strategies in automated test case creation, test data creation, defect detection, test priority analysis, and deployment. Security issues such as malware detection, unauthorized access, runtime security threats, and decentralized access control are some other aspects to be covered in this survey. Some of the advantages of using AI-driven strategies for testing include greater automation, accuracy, and quicker deployment. On the downside, there are certain challenges such as the need for high-quality data, explainability, and computational complexity.
Dipesh Garg· International Journal of Nex...· 0 citations
Generative Artificial Intelligence (AI) is changing the FinTech industry, by making the financial services faster, smarter, and more efficient. AI- tools such as ChatGPT, Gemini, Microsoft Copilot, and other large language models are helping financial institutions improve customer service, automate routine work, detect fraud, manage risks, and support better decision-making. These technologies are also helping banks and financial companies provide better digital services to their customers. At the same time, the increasing use of generative AI has created several challenges. These include data privacy, cyber security risk, AI bias, misinformation, ethical concerns, legal issues. These challenges need proper attention to ensure the safe and responsible use of AI in financial services. The main purpose of this study is to examine the role of Generative AI in the FinTech industry, identify its opportunities and challenges and understands it contribution to digital transformation. The study is based on entirely secondary data only. The data collected from research articles, e-journals, books, government publications, industry reports, case studies, reports of international organization, and various websites. The findings of the research show that generative AI is becoming an important technology in the FinTech sector. It improves customer experience, increases work efficiency, supports innovations and help reduce operating costs. However, financial institutions must also address issues related to data security, privacy, transparency, and ethical use of AI. The study suggests that financial institutions should adopt responsible AI- practices strengthen cyber security system, improve employee skills through regular training and follow government regulations and ethical guidelines. These measures can help in building trust and support the safe and sustainable use of Generative AI in the future of FinTech.
Sawarna Sawarna, Manoj Kumar· Journal of Commerce, Economi...· 0 citations
Software defect prediction (SDP) becomes extremely important in enhancing the quality of software. Recent progresses in machine learning and ensemble learning have led to a great improvement on the prediction model of defects. In this research, a test defect prediction model combining state of (XGBoost, LightGBM, CatBoost) and balanced ensemble (EasyEnsemble, RUSBoost, Balanced Random Forest). The model is assessed on 5 standard AEEEM benchmark problems (EQ, JDT, LC, ML, PDE) with SMOTE oversampling and on the hold-out test strategy. The results of the experiment show RUSBoost- based model is more effective than the past models of defect prediction on EQ data with an AUC of 0.946, CatBoost model has an AUC of 0.850 on the JDT dataset, XGBoost method that uses on the LC sample has an AUC of 0.782, which is better than classical and other machine-learning-based methods published before, RUSBoost model is superior to the traditional and deep-learning-based models, which have the largest AUC of 0.757 to date on the ML dataset, XGBoost classifier obtains the high performance on the PDE dataset, with an AUC of 0.816, which is better than the classical and neural network baselines.
Hamed Fawareh, Abdulrhman Alkhmali, Mohammad A. Hassan· WSEAS Transactions on Comput...· 0 citations
This paper demonstrates an AI-assisted search process to aid in literature surveys within a fast-moving research area, and assesses the usefulness and validity of these results.
Jonathan P. Bowen, Sin-Hao Chen· Applied Sciences· 0 citations
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