From Fine-tuning to Output: An Empirical Investigation of Test Smells in Transformer-Based Test Code Generation

Author:

Aljohani Ahmed1ORCID,Do Hyunsook2ORCID

Affiliation:

1. University of North Texas, Denton, USA

2. University of North Texas, Denton, United States

Publisher

ACM

Reference38 articles.

1. Saranya Alagarsamy, Chakkrit Tantithamthavorn, and Aldeida Aleti. 2023. A3Test: Assertion-Augmented Automated Test Case Generation. arXiv preprint arXiv:2302.10352 (2023).

2. Wajdi Aljedaani, Anthony Peruma, Ahmed Aljohani, Mazen Alotaibi, Mohamed Wiem Mkaouer, Ali Ouni, Christian D Newman, Abdullatif Ghallab, and Stephanie Ludi. 2021. Test smell detection tools: A systematic mapping study. Evaluation and Assessment in Software Engineering (2021), 170--180.

3. Are test smells really harmful? An empirical study

4. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901.

5. Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022. Codet: Code generation with generated tests. arXiv preprint arXiv:2207.10397 (2022).

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