IDEAS home Printed from https://ideas.repec.org/a/nwe/iitfed/y2024i1p250-256.html

Business logic unit testing optimization in .NET using AI

Author

Listed:
  • Maria Marzovanova

    (University of National and World Economy, Sofia, Bulgaria)

  • Veska Mihova

    (University of National and World Economy, Sofia, Bulgaria)

Abstract

Unit testing the business logic is crucial in the development of business information systems. In a dynamic and increasingly demanding environment, unit testing is one of the tools for assuring quality and stability of software solutions. The traditional way of adding unit tests to a business logic layer is a slow and prone to errors process, but with the evolving artificial intelligence assistance, software developers and QA specialists have more optimization opportunities. This paper presents research results on the benefits of using AI assistance in generating test cases, based on code analysis, generating tests, test selection and prioritization, improving the efficiency and effectiveness of the testing process in .NET application.

Suggested Citation

  • Maria Marzovanova & Veska Mihova, 2025. "Business logic unit testing optimization in .NET using AI," Innovative Information Technologies for Economy Digitalization (IITED), University of National and World Economy, Sofia, Bulgaria, issue 1, pages 250-256, October.
    • Handle: RePEc:nwe:iitfed:y:2024:i:1:p:250-256
    as

    Download full text from publisher

    File URL: https://www.unwe.bg/doi/iited/2025/IITED.2025.31.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Plamen Milev, 2025. "Design Solutions for an Information System with User Configuration in a University Environment," Ikonomiceski i Sotsialni Alternativi, University of National and World Economy, Sofia, Bulgaria, issue 1, pages 100-115, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nwe:iitfed:y:2024:i:1:p:250-256. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Vanya Lazarova (email available below). General contact details of provider: https://edirc.repec.org/data/unweebg.html .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.