Improving the Readability of Automatically Generated Tests using Large Language Models

• URL: http://arxiv.org/abs/2412.18843v1
• Date: Wed, 25 Dec 2024 09:08:53 GMT
• Title: Improving the Readability of Automatically Generated Tests using Large Language Models
• Authors: Matteo Biagiola, Gianluca Ghislotti, Paolo Tonella,
• Abstract summary: We propose to combine the effectiveness of search-based generators with the readability of LLM generated tests. Our approach focuses on improving test and variable names produced by search-based tools, while keeping their semantics unchanged.
• Score: 7.7149881834358345
• License:
• Abstract: Search-based test generators are effective at producing unit tests with high coverage. However, such automatically generated tests have no meaningful test and variable names, making them hard to understand and interpret by developers. On the other hand, large language models (LLMs) can generate highly readable test cases, but they are not able to match the effectiveness of search-based generators, in terms of achieved code coverage. In this paper, we propose to combine the effectiveness of search-based generators with the readability of LLM generated tests. Our approach focuses on improving test and variable names produced by search-based tools, while keeping their semantics (i.e., their coverage) unchanged. Our evaluation on nine industrial and open source LLMs show that our readability improvement transformations are overall semantically-preserving and stable across multiple repetitions. Moreover, a human study with ten professional developers, show that our LLM-improved tests are as readable as developer-written tests, regardless of the LLM employed.

Related papers

• Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs [54.309127753635366]
  We present the results of a replication study in which we investigate GPT-4 effectiveness in recommending and suggesting idiomatic actions. Our findings underscore the potential of LLMs to achieve tasks where, in the past, implementing recommenders based on complex code analyses was required.
  arXiv  Detail & Related papers  (2025-01-28T15:41:54Z)
• Commit0: Library Generation from Scratch [77.38414688148006]
  Commit0 is a benchmark that challenges AI agents to write libraries from scratch. Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests. Commit0 also offers an interactive environment where models receive static analysis and execution feedback on the code they generate.
  arXiv  Detail & Related papers  (2024-12-02T18:11:30Z)
• Do LLMs generate test oracles that capture the actual or the expected program behaviour? [7.772338538073763]
  Large Language Models (LLMs) are trained on an enormous amount of data to generate developer-like code and test cases. This study includes developer-written and automatically generated test cases and oracles for 24 open-source Java repositories. LLMs are better at generating test oracles rather than classifying the correct ones, and can generate better test oracles when the code contains meaningful test or variable names.
  arXiv  Detail & Related papers  (2024-10-28T15:37:06Z)
• ASTER: Natural and Multi-language Unit Test Generation with LLMs [6.259245181881262]
  We describe a generic pipeline that incorporates static analysis to guide LLMs in generating compilable and high-coverage test cases. We conduct an empirical study to assess the quality of the generated tests in terms of code coverage and test naturalness.
  arXiv  Detail & Related papers  (2024-09-04T21:46:18Z)
• Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation [11.056044348209483]
  Unit testing, crucial for identifying bugs in code modules like classes and methods, is often neglected by developers due to time constraints. Large Language Models (LLMs), like GPT and Mistral, show promise in software engineering, including in test generation.
  arXiv  Detail & Related papers  (2024-06-28T20:38:41Z)
• DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph [70.79413606968814]
  We introduce Dynamic Evaluation of LLMs via Adaptive Reasoning Graph Evolvement (DARG) to dynamically extend current benchmarks with controlled complexity and diversity. Specifically, we first extract the reasoning graphs of data points in current benchmarks and then perturb the reasoning graphs to generate novel testing data. Such newly generated test samples can have different levels of complexity while maintaining linguistic diversity similar to the original benchmarks.
  arXiv  Detail & Related papers  (2024-06-25T04:27:53Z)
• 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)
• Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
  We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
  arXiv  Detail & Related papers  (2023-12-14T19:09:22Z)
• Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing [13.743062498008555]
  We introduce MuTAP for improving the effectiveness of test cases generated by Large Language Models (LLMs) in terms of revealing bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs) Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets.
  arXiv  Detail & Related papers  (2023-08-31T08:48:31Z)
• Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models [75.75038268227554]
  Self-Checker is a framework comprising a set of plug-and-play modules that facilitate fact-checking. This framework provides a fast and efficient way to construct fact-checking systems in low-resource environments.
  arXiv  Detail & Related papers  (2023-05-24T01:46:07Z)
• LEVER: Learning to Verify Language-to-Code Generation with Execution [64.36459105535]
  We propose LEVER, a simple approach to improve language-to-code generation by learning to verify the generated programs with their execution results. Specifically, we train verifiers to determine whether a program sampled from the LLMs is correct or not based on the natural language input, the program itself and its execution results. LEVER consistently improves over the base code LLMs(4.6% to 10.9% with code-davinci) and achieves new state-of-the-art results on all of them.
  arXiv  Detail & Related papers  (2023-02-16T18:23:22Z)

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.