TDD Without Tears: Towards Test Case Generation from Requirements through Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2401.07576v1
• Date: Mon, 15 Jan 2024 10:21:58 GMT
• Title: TDD Without Tears: Towards Test Case Generation from Requirements through Deep Reinforcement Learning
• Authors: Wannita Takerngsaksiri, Rujikorn Charakorn, Chakkrit Tantithamthavorn, Yuan-Fang Li
• Abstract summary: Test-driven development (TDD) mandates writing test cases based on requirements before writing the actual code. While writing test cases is the centerpiece of TDD, it is time-consuming, expensive, and often shunned by developers. We introduce PyTester, a Text-to-Testcase generation approach that can automatically generate correct, executable, complete, and effective test cases.
• Score: 22.331330777536046
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Test-driven development (TDD) is a widely-employed software development practice that mandates writing test cases based on requirements before writing the actual code. While writing test cases is the centerpiece of TDD, it is time-consuming, expensive, and often shunned by developers. To address these issues associated with TDD, automated test case generation approaches have recently been investigated. Such approaches take source code as input, but not the requirements. Therefore, existing work does not fully support true TDD, as actual code is required to generate test cases. In addition, current deep learning-based test case generation approaches are trained with one learning objective, i.e., to generate test cases that are exactly matched with the ground-truth test cases. However, such approaches may limit the model's ability to generate different yet correct test cases. In this paper, we introduce PyTester, a Text-to-Testcase generation approach that can automatically generate syntactically correct, executable, complete, and effective test cases while being aligned with a given natural language requirement. We evaluate PyTester on the public APPS benchmark dataset, and the results show that our Deep RL approach enables PyTester, a small language model, to outperform much larger language models like GPT3.5, StarCoder, and InCoder. Our findings suggest that future research could consider improving small over large LMs for better resource efficiency by integrating the SE domain knowledge into the design of reinforcement learning architecture.

Related papers

• BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping [64.8477128397529]
  We propose a training-required and training-free test-time adaptation framework. We maintain a light-weight key-value memory for feature retrieval from instance-agnostic historical samples and instance-aware boosting samples. We theoretically justify the rationality behind our method and empirically verify its effectiveness on both the out-of-distribution and the cross-domain datasets.
  arXiv  Detail & Related papers  (2024-10-20T15:58:43Z)
• Multi-language Unit Test Generation using LLMs [6.259245181881262]
  We describe a generic pipeline that incorporates static analysis to guide LLMs in generating compilable and high-coverage test cases. We show how the pipeline can be applied to different programming languages, specifically Java and Python, and to complex software requiring environment mocking. Our results demonstrate that LLM-based test generation, when guided by static analysis, can be competitive with, and even outperform, state-of-the-art test-generation techniques in coverage achieved.
  arXiv  Detail & Related papers  (2024-09-04T21:46:18Z)
• Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning [50.26965628047682]
  Adapting pre-trained models to open classes is a challenging problem in machine learning. In this paper, we consider combining the advantages of both and come up with a test-time prompt tuning approach. Our proposed method outperforms all comparison methods on average considering both base and new classes.
  arXiv  Detail & Related papers  (2024-08-29T12:34:01Z)
• KAT: Dependency-aware Automated API Testing with Large Language Models [1.7264233311359707]
  KAT (Katalon API Testing) is a novel AI-driven approach that autonomously generates test cases to validate APIs. Our evaluation of KAT using 12 real-world services shows that it can improve validation coverage, detect more undocumented status codes, and reduce false positives in these services.
  arXiv  Detail & Related papers  (2024-07-14T14:48:18Z)
• Large Language Models as Test Case Generators: Performance Evaluation and Enhancement [3.5398126682962587]
  We study how well Large Language Models can generate high-quality test cases. We propose a multi-agent framework called emphTestChain that decouples the generation of test inputs and test outputs. Our results indicate that TestChain outperforms the baseline by a large margin.
  arXiv  Detail & Related papers  (2024-04-20T10:27:01Z)
• Test-Driven Development for Code Generation [0.850206009406913]
  Large Language Models (LLMs) have demonstrated significant capabilities in generating code snippets directly from problem statements. This paper investigates if and how Test-Driven Development (TDD) can be incorporated into AI-assisted code-generation processes.
  arXiv  Detail & Related papers  (2024-02-21T04:10:12Z)
• CAT-LM: Training Language Models on Aligned Code And Tests [19.526181671936243]
  Testing is an integral part of the software development process. Yet, writing tests is time-consuming and therefore often neglected. We propose the Aligned Code And Tests Language Model (CAT-LM), a GPT-style language model with 2.7 Billion parameters, trained on a corpus of Python and Java projects.
  arXiv  Detail & Related papers  (2023-10-02T19:52:22Z)
• AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation [64.9230895853942]
  Domain generalization can be arbitrarily hard without exploiting target domain information. Test-time adaptive (TTA) methods are proposed to address this issue. In this work, we adopt Non-Parametric to perform the test-time Adaptation (AdaNPC)
  arXiv  Detail & Related papers  (2023-04-25T04:23:13Z)
• Learning Deep Semantics for Test Completion [46.842174440120196]
  We formalize the novel task of test completion to automatically complete the next statement in a test method based on the context of prior statements and the code under test. We develop TeCo -- a deep learning model using code semantics for test completion.
  arXiv  Detail & Related papers  (2023-02-20T18:53:56Z)
• TeST: Test-time Self-Training under Distribution Shift [99.68465267994783]
  Test-Time Self-Training (TeST) is a technique that takes as input a model trained on some source data and a novel data distribution at test time. We find that models adapted using TeST significantly improve over baseline test-time adaptation algorithms.
  arXiv  Detail & Related papers  (2022-09-23T07:47:33Z)
• CodeT: Code Generation with Generated Tests [49.622590050797236]
  We explore the use of pre-trained language models to automatically generate test cases. CodeT executes the code solutions using the generated test cases, and then chooses the best solution. We evaluate CodeT on five different pre-trained models with both HumanEval and MBPP benchmarks.
  arXiv  Detail & Related papers  (2022-07-21T10:18:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.