Predictive Analytics in QA Automation: Article Swipe

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Gazi Mahabubul Alam , Mohammed Majid Bakhsh , Nusrat Yasmin Nadia , Saiful Islam ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.60087/jklst.v4.n2.005

An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve software quality and expedite testing procedures, hence redefining defect prevention for American businesses. This study uses a systematic methodology that combines machine learning-based defect prediction with real-world case studies, analyzing defect trends and evaluating the effectiveness of predictive models. The results show that enterprises leveraging predictive analytics in QA automation experience higher defect detection rates reduced testing overhead, and faster release cycles. The study identifies key machine learning models, such as Random Forests, Support Vector Machines (SVM), and Neural Networks, which have demonstrated significant accuracy in defect prediction. It also discusses the integration of predictive analytics within DevOps and CI/CD pipelines, enabling continuous monitoring and proactive defect prevention. Defect prediction skills will be significantly improved in the future by developments in Explainable AI (XAI), deep learning models, and Natural Language Processing (NLP). In addition to supporting data-driven decision-making, model transparency, and continuous learning frameworks, this article offers important advice for businesses looking to integrate predictive analytics into their QA procedures. U.S. businesses may go from reactive to proactive QA approaches by adopting predictive analytics, which will guarantee better software quality, lower expenses, and an enhanced user experience

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Mechanical Engineering

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Analytics Computer science Automation Data science Engineering Mechanical engineering

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Type: article
Language: en
Landing Page: https://doi.org/10.60087/jklst.v4.n2.005
PDF: https://jklst.org/index.php/home/article/download/v2.n2.005/v2.n2.005
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OpenAlex ID: https://openalex.org/W4411048516

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OpenAlex ID: https://openalex.org/W4411048516

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DOI: https://doi.org/10.60087/jklst.v4.n2.005

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Title: Predictive Analytics in QA Automation:

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-05-15

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Authors: Gazi Mahabubul Alam, Mohammed Majid Bakhsh, Nusrat Yasmin Nadia, Saiful Islam

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Landing page: https://doi.org/10.60087/jklst.v4.n2.005

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Concepts: Analytics, Computer science, Automation, Data science, Engineering, Mechanical engineering

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