Towards Autonomous Testing Agents via Conversational Large Language Models

• URL: http://arxiv.org/abs/2306.05152v2
• Date: Tue, 5 Sep 2023 14:34:15 GMT
• Title: Towards Autonomous Testing Agents via Conversational Large Language Models
• Authors: Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo
• Abstract summary: Large language models (LLMs) can be used as automated testing assistants. We present a taxonomy of LLM-based testing agents based on their level of autonomy.
• Score: 18.302956037305112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. Recent discoveries of the capabilities of large language models (LLMs) suggest that they can be used as automated testing assistants, and thus provide helpful information and even drive the testing process. To highlight the potential of this technology, we present a taxonomy of LLM-based testing agents based on their level of autonomy, and describe how a greater level of autonomy can benefit developers in practice. An example use of LLMs as a testing assistant is provided to demonstrate how a conversational framework for testing can help developers. This also highlights how the often criticized hallucination of LLMs can be beneficial for testing. We identify other tangible benefits that LLM-driven testing agents can bestow, and also discuss potential limitations.

Related papers

• Studying and Benchmarking Large Language Models For Log Level Suggestion [49.176736212364496]
  Large Language Models (LLMs) have become a focal point of research across various domains. This paper investigates the impact of characteristics and learning paradigms on the performance of 12 open-source LLMs in log level suggestion.
  arXiv  Detail & Related papers  (2024-10-11T03:52:17Z)
• 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)
• Learning to Ask: When LLMs Meet Unclear Instruction [49.256630152684764]
  Large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills alone. We evaluate the performance of LLMs tool-use under imperfect instructions, analyze the error patterns, and build a challenging tool-use benchmark called Noisy ToolBench. We propose a novel framework, Ask-when-Needed (AwN), which prompts LLMs to ask questions to users whenever they encounter obstacles due to unclear instructions.
  arXiv  Detail & Related papers  (2024-08-31T23:06:12Z)
• CIBench: Evaluating Your LLMs with a Code Interpreter Plugin [68.95137938214862]
  We propose an interactive evaluation framework, named CIBench, to comprehensively assess LLMs' ability to utilize code interpreters for data science tasks. The evaluation dataset is constructed using an LLM-human cooperative approach and simulates an authentic workflow by leveraging consecutive and interactive IPython sessions. We conduct extensive experiments to analyze the ability of 24 LLMs on CIBench and provide valuable insights for future LLMs in code interpreter utilization.
  arXiv  Detail & Related papers  (2024-07-15T07:43:55Z)
• Automatic benchmarking of large multimodal models via iterative experiment programming [71.78089106671581]
  We present APEx, the first framework for automatic benchmarking of LMMs. Given a research question expressed in natural language, APEx leverages a large language model (LLM) and a library of pre-specified tools to generate a set of experiments for the model at hand. The report drives the testing procedure: based on the current status of the investigation, APEx chooses which experiments to perform and whether the results are sufficient to draw conclusions.
  arXiv  Detail & Related papers  (2024-06-18T06:43:46Z)
• Test Oracle Automation in the era of LLMs [52.69509240442899]
  Large Language Models (LLMs) have demonstrated remarkable proficiency in tackling diverse software testing tasks. This paper aims to enable discussions on the potential of using LLMs for test oracle automation, along with the challenges that may emerge during the generation of various types of oracles.
  arXiv  Detail & Related papers  (2024-05-21T13:19:10Z)
• Are We Testing or Being Tested? Exploring the Practical Applications of Large Language Models in Software Testing [0.0]
  A Large Language Model (LLM) represents a cutting-edge artificial intelligence model that generates coherent content. LLM can play a pivotal role in software development, including software testing. This study explores the practical application of LLMs in software testing within an industrial setting.
  arXiv  Detail & Related papers  (2023-12-08T06:30:37Z)
• AXNav: Replaying Accessibility Tests from Natural Language [14.131076040673351]
  Large Language Models (LLMs) have been used for a variety of tasks including automation of UIs. This paper explores the requirements of a natural language based accessibility testing workflow. We build a system that takes as input a manual accessibility test (e.g., Search for a show in VoiceOver'') and uses an LLM combined with pixel-based UI Understanding models to execute the test.
  arXiv  Detail & Related papers  (2023-10-03T20:37:58Z)
• LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities [8.504639288314063]
  Test script generation is a vital component of software testing, enabling efficient and reliable automation of repetitive test tasks. Existing generation approaches often encounter limitations, such as difficulties in accurately capturing and reproducing test scripts across diverse devices, platforms, and applications. This paper investigates the application of large language models (LLM) in the domain of mobile application test script generation.
  arXiv  Detail & Related papers  (2023-09-24T07:58:57Z)
• Software Testing with Large Language Models: Survey, Landscape, and Vision [32.34617250991638]
  Pre-trained large language models (LLMs) have emerged as a breakthrough technology in natural language processing and artificial intelligence. This paper provides a comprehensive review of the utilization of LLMs in software testing.
  arXiv  Detail & Related papers  (2023-07-14T08:26:12Z)
• 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)

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.