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Transforming User Stories into Java Scripts: Advancing Qa Automation in The Us Market With Natural Language Processing

Author

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  • Ankur Sarkar
  • S A Mohaiminul Islam
  • MD Shadikul Bari

Abstract

With constant updates in software development, it is paramount that higher reliability of the software is achieved by having sound testing procedures for the software. The tradition ways of creating test script are manual and time-consuming and can accommodate a lot human error as well as do not adapt to Agile and DevOps environments properly. This research presents an alternative solution that can be used to address the problem: an apparatus based on Natural Language Processing technologies that enables the transition from user stories to test scripts written in Java. The advantage of the proposed framework is that it can support the interpretation of user stories written in natural language and transform these into strictly structured test cases that are compatible with Selenium, JUnit, or Cucumber. As such, a fundamental objective of this framework is to minimize the time needed to write test script and at the same time be accurate and consistent. It covers problems typical to many projects like vagueness in requirements description, increased size of systems under test, and specific terminology in the domain area, making the generated test scripts covering both typical and extraordinary situations. Besides, it meets specifications that are particular to particular sectors like H-HIPAA for health facilities and H-PCI-DSS for facilities that deal with finances. The outcome of leveraging the exaction of the conceived framework into prototypes/practical applications from industries such as financial, healthcare, and e-commerce illustrate the raise in efficacy and scalability in QA line functions. By increasing the time to perform manual test by 80%, detecting defects at a higher percentage compared to the manual method and test coverage of the application, the framework provides more accurate results than the other methods. Additionally, incorporating the framework into CI/CD pipelines means that developers can TEST their codes quickly and have an almost real-time feedback based on the software that has been DEVOPed for implementation, without having to slow down the processes by running a lot of test more than once.

Suggested Citation

  • Ankur Sarkar & S A Mohaiminul Islam & MD Shadikul Bari, 2024. "Transforming User Stories into Java Scripts: Advancing Qa Automation in The Us Market With Natural Language Processing," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 9-37.
    • Handle: RePEc:das:njaigs:v:7:y:2024:i:01:p:9-37:id:293
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    References listed on IDEAS

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    1. Yue Kang & Zhao Cai & Chee-Wee Tan & Qian Huang & Hefu Liu, 2020. "Natural language processing (NLP) in management research: A literature review," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 139-172, April.
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    Cited by:

    1. Sandeep Pochu & Sai Rama Krishna Nersu & Srikanth Reddy Kathram, 2024. "Zero Trust Principles in Cloud Security: A DevOps Perspective," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 660-671.
    2. Sandeep Pochu & Sai Rama Krishna Nersu & Srikanth Reddy Kathram, 2024. "Enhancing Cloud Security with Automated Service Mesh Implementations in DevOps Pipelines," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 90-103.
    3. Sandeep Pochu & Sai Rama Krishna Nersu & Srikanth Reddy Kathram, 2024. "Multi-Cloud DevOps Strategies: A Framework for Agility and Cost Optimization," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 104-119.
    4. Md Shaikat Alam Joy & Gazi Touhidul Alam & Mohammed Majid Bakhsh, 2024. "Transforming QA Efficiency: Leveraging Predictive Analytics to Minimize Costs in Business-Critical Software Testing for the US Market," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 77-89.
    5. Dr. Alejandro García, 2024. "AI at the Crossroads of Health and Society: Emerging Paradigms," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 150-160.

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