71cf539319367b8b5f1340a 20241127121354223 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 11 30 2024 13 12 Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam. Ngoc Thi Bich Do https://orcid.org/0009-0004-3250-0154 Chi Quynh Nguyen https://orcid.org/0009-0007-6197-2486 Faculty, Department of Computer Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam. Unit testing is a critical phase in the software development lifecycle, essential for ensuring the quality and reliability of code. However, the manual creation of unit test scripts and the preparation of corresponding test data can be a time consuming and labor intensive process. To address these challenges, several automated approaches have been explored, including search based, constraint based, random-based, and symbolic execution based techniques for generating unit tests. In recent years, the rapid advancement of large language models (LLMs) has opened new avenues for automating various tasks, including the automatic generation of unit test scripts and test data. Despite their potential, using LLMs in a straightforward manner to generate unit tests may lead to low test coverage. This means that a significant portion of the source code, including specific statements or branches, may remain untested, which can reduce the effectiveness of the tests. To overcome this limitation, the paper presents a novel approach that not only automates the generation of unit test scripts and test data but also improves test coverage. The proposed solution begins by using an LLM tool (such as ChatGPT) to generate initial unit test scripts and data from the source code. To enhance test coverage, the specification document of the source code is also input into the LLM to generate additional test data. Following this, a coverage checking tool is used to evaluate the test coverage and identify untested statements or branches. The LLM is then applied again to generate new test data aimed specifically at addressing these gaps. The initial experimental results indicate that this method significantly improves test coverage, demonstrating its potential to enhance automated unit testing processes. 11 30 2024 11 30 2024 13 18 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Based on my understanding, this article has no conflicts of interest. This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted with objectivity and without any external influence. The content of this article does not necessitate ethical approval or consent to participate with supporting documentation. The adequate resources of this article are publicly accessible. The authorship of this article is contributed equally to all participating individuals. 10.35940/ijitee.L9996.13121124 https://www.ijitee.org/portfolio-item/L999613121124/