Harnessing the Power of LLMs: Automating Unit Test Generation for High-Performance Computing

• URL: http://arxiv.org/abs/2407.05202v1
• Date: Sat, 6 Jul 2024 22:45:55 GMT
• Title: Harnessing the Power of LLMs: Automating Unit Test Generation for High-Performance Computing
• Authors: Rabimba Karanjai, Aftab Hussain, Md Rafiqul Islam Rabin, Lei Xu, Weidong Shi, Mohammad Amin Alipour,
• Abstract summary: Unit testing is crucial in software engineering for ensuring quality. It's not widely used in parallel and high-performance computing software, particularly scientific applications. We propose an automated method for generating unit tests for such software.
• Score: 7.3166218350585135
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unit testing is crucial in software engineering for ensuring quality. However, it's not widely used in parallel and high-performance computing software, particularly scientific applications, due to their smaller, diverse user base and complex logic. These factors make unit testing challenging and expensive, as it requires specialized knowledge and existing automated tools are often ineffective. To address this, we propose an automated method for generating unit tests for such software, considering their unique features like complex logic and parallel processing. Recently, large language models (LLMs) have shown promise in coding and testing. We explored the capabilities of Davinci (text-davinci-002) and ChatGPT (gpt-3.5-turbo) in creating unit tests for C++ parallel programs. Our results show that LLMs can generate mostly correct and comprehensive unit tests, although they have some limitations, such as repetitive assertions and blank test cases.

Related papers

• ProjectTest: A Project-level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms [48.43237545197775]
  Unit test generation has become a promising and important use case of LLMs. ProjectTest is a project-level benchmark for unit test generation covering Python, Java, and JavaScript.
  arXiv  Detail & Related papers  (2025-02-10T15:24:30Z)
• The Potential of LLMs in Automating Software Testing: From Generation to Reporting [0.0]
  Manual testing, while effective, can be time consuming and costly, leading to an increased demand for automated methods. Recent advancements in Large Language Models (LLMs) have significantly influenced software engineering. This paper explores an agent-oriented approach to automated software testing, using LLMs to reduce human intervention and enhance testing efficiency.
  arXiv  Detail & Related papers  (2024-12-31T02:06:46Z)
• 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)
• 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)
• A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites [1.4563527353943984]
  Large Language Models (LLMs) have been applied to various aspects of software development. We present AgoneTest: an automated system for generating test suites for Java projects.
  arXiv  Detail & Related papers  (2024-08-14T23:02:16Z)
• Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph [83.90988015005934]
  Uncertainty quantification is a key element of machine learning applications. We introduce a novel benchmark that implements a collection of state-of-the-art UQ baselines. We conduct a large-scale empirical investigation of UQ and normalization techniques across eleven tasks, identifying the most effective approaches.
  arXiv  Detail & Related papers  (2024-06-21T20:06:31Z)
• TESTEVAL: Benchmarking Large Language Models for Test Case Generation [15.343859279282848]
  We propose TESTEVAL, a novel benchmark for test case generation with large language models (LLMs) We collect 210 Python programs from an online programming platform, LeetCode, and design three different tasks: overall coverage, targeted line/branch coverage, and targeted path coverage. We find that generating test cases to cover specific program lines/branches/paths is still challenging for current LLMs.
  arXiv  Detail & Related papers  (2024-06-06T22:07:50Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• Fault-Aware Neural Code Rankers [64.41888054066861]
  We propose fault-aware neural code rankers that can predict the correctness of a sampled program without executing it. Our fault-aware rankers can significantly increase the pass@1 accuracy of various code generation models.
  arXiv  Detail & Related papers  (2022-06-04T22:01:05Z)
• Automated Support for Unit Test Generation: A Tutorial Book Chapter [21.716667622896193]
  Unit testing is a stage of testing where the smallest segment of code that can be tested in isolation from the rest of the system is tested. Unit tests are typically written as executable code, often in a format provided by a unit testing framework such as pytest for Python. This chapter introduces the concept of search-based unit test generation.
  arXiv  Detail & Related papers  (2021-10-26T11:13:40Z)
• Measuring Coding Challenge Competence With APPS [54.22600767666257]
  We introduce APPS, a benchmark for code generation. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems.
  arXiv  Detail & Related papers  (2021-05-20T17:58:42Z)

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.