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Chain-of-Thought and Chain-of-Verification Prompting for Grammar-based Test Case Generation
http://doi.org/10.5626/JOK.2025.52.1.29
Software testing is an essential but cost-intensive work in the software development process. Automatic test case generation tools are utilized to distinguish between the correct and the incorrect solutions more effectively than manually generating them. Many researchers have recently proposed deep learning-based methods to generate test cases automatically for given logical specifications of problems or programs. In this work, we propose teaching the large language models (LLMs) such as ChatGPT and Google Gemini to generate ‘test case grammars’ from problem specifications, particularly using the chain-of-thought (CoT) prompting. Additionally, we implemented it using the CoT to verify and by providing the details of generalized rules to the LLMs, termed “chain-of-verification” (CoVe). We further evaluate our method with the publicly available dataset, DeepMind CodeContests dataset, which consists of numerous programming problems ranging from beginner to advanced level and is submitted by programming students with test cases for verifying the correctness of programs.
A Weight-based Multi-domain Recommendation System for Alleviating the Cold-Start Problem
http://doi.org/10.5626/JOK.2021.48.10.1090
A recommendation system predicts users’ ratings based on users’ past behaviors and item preferences. One of the most famous types of recommendation systems is the collaborative filtering method that predicts users’ ratings based on the rating information from users with similar preferences. In order to predict the preferences of users, we need adequate information about users’ interactive information on items. Otherwise, it is very difficult to make accurate predictions for users without adequate information. This phenomenon is called the cold-start problem. In this paper, we propose a multi-domain recommendation system that utilizes the rating information of multiple domains. We propose a method that calculates the weight of each auxiliary domain to maximize the confidence of predicted ratings from multiple auxiliary domains and verify the performance of the proposed method through extensive experiments. As a result, we demonstrate that our algorithm produces better recommendation results compared to the classical algorithms simply utilized in multiple domain settings.
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