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Unit Testing

  • An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation, (TSE2024)

    • Abstract: Unit tests play a key role in ensuring the correctness of software. However, manually creating unit tests is a laborious task, motivating the need for automation. Large Language Models (LLMs) have recently been applied to various aspects of software development, including their suggested use for automated generation of unit tests, but while requiring additional training or few-shot learning on examples of existing tests. This paper presents a large-scale empirical evaluation on the effectiveness...
    • Labels: program testing, unit testing, empirical study

  • ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation, (TSE2024)

    • Abstract: Recent advancements in large language models (LLMs) have demonstrated exceptional success in a wide range of general domain tasks, such as question answering and following instructions. Moreover, LLMs have shown potential in various software engineering applications. In this study, we present a systematic comparison of test suites generated by the ChatGPT LLM and the state-of-the-art SBST tool EvoSuite. Our comparison is based on several critical factors, including correctness, readability, code...
    • Labels: program testing, unit testing, empirical study

  • Domain Adaptation for Code Model-Based Unit Test Case Generation, (ISSTA2024)

    • Abstract: Recently, deep learning-based test case generation approaches have been proposed to automate the generation of unit test cases. In this study, we leverage Transformer-based code models to generate unit tests with the help of Domain Adaptation (DA) at a project level. Specifically, we use CodeT5, a relatively small language model trained on source code data, and fine-tune it on the test generation task. Then, we apply domain adaptation to each target project data to le...
    • Labels: program testing, unit testing

  • Evaluating and Improving ChatGPT for Unit Test Generation, (FSE2024)

    • Abstract: Unit testing plays an essential role in detecting bugs in functionally-discrete program units (e.g., methods). Manually writing high-quality unit tests is time-consuming and laborious. Although the traditional techniques are able to generate tests with reasonable coverage, they are shown to exhibit low readability and still cannot be directly adopted by developers in practice. Recent work has shown the large potential of large language models (LLMs) in unit test generation. By being pre-trained ...
    • Labels: program testing, unit testing, empirical study, code generation

  • On the Evaluation of Large Language Models in Unit Test Generation, (ASE2024)

    • Abstract: Unit testing is an essential activity in software development for verifying the correctness of software components. However, manually writing unit tests is challenging and time-consuming. The emergence of Large Language Models (LLMs) offers a new direction for automating unit test generation. Existing research primarily focuses on closed-source LLMs (e.g., ChatGPT and CodeX) with fixed prompting strategies, leaving the capabilities of advanced open-source LLMs with various prompting settings une...
    • Labels: program testing, unit testing, empirical study

  • Towards Understanding the Effectiveness of Large Language Models on Directed Test Input Generation, (ASE2024)

    • Abstract: Automatic testing has garnered significant attention and success over the past few decades. Techniques such as unit testing and coverage-guided fuzzing have revealed numerous critical software bugs and vulnerabilities. However, a long-standing, formidable challenge for existing techniques is how to achieve higher testing coverage. Constraint-based techniques, such as symbolic execution and concolic testing, have been well-explored and integrated into the existing approaches. With the popularity ...
    • Labels: program testing, unit testing, empirical study

  • UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing, (ISSTA2024)

    • Abstract: The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community. However, existing code LLMs often demonstrate unsatisfactory capabilities in generating accurate, complete tests since they were trained on code snippets collected without differentiating between code for testing and for other purposes. In this paper, we present a large-scale dataset, UniTSyn, which can enhance LLMs for Unit Test Synthesis. A...
    • Labels: program testing, unit testing, benchmark