Are We Testing or Being Tested? Exploring the Practical Applications of Large Language Models in Software Testing

• URL: http://arxiv.org/abs/2312.04860v1
• Date: Fri, 8 Dec 2023 06:30:37 GMT
• Title: Are We Testing or Being Tested? Exploring the Practical Applications of Large Language Models in Software Testing
• Authors: Robson Santos, Italo Santos, Cleyton Magalhaes, Ronnie de Souza Santos
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A Large Language Model (LLM) represents a cutting-edge artificial intelligence model that generates coherent content, including grammatically precise sentences, human-like paragraphs, and syntactically accurate code snippets. LLMs can play a pivotal role in software development, including software testing. LLMs go beyond traditional roles such as requirement analysis and documentation and can support test case generation, making them valuable tools that significantly enhance testing practices within the field. Hence, we explore the practical application of LLMs in software testing within an industrial setting, focusing on their current use by professional testers. In this context, rather than relying on existing data, we conducted a cross-sectional survey and collected data within real working contexts, specifically, engaging with practitioners in industrial settings. We applied quantitative and qualitative techniques to analyze and synthesize our collected data. Our findings demonstrate that LLMs effectively enhance testing documents and significantly assist testing professionals in programming tasks like debugging and test case automation. LLMs can support individuals engaged in manual testing who need to code. However, it is crucial to emphasize that, at this early stage, software testing professionals should use LLMs with caution while well-defined methods and guidelines are being built for the secure adoption of these tools.

Related papers

• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• 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)
• 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)
• Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph [85.51252685938564]
  Uncertainty quantification (UQ) is becoming increasingly recognized as a critical component of applications that rely on machine learning (ML) As with other ML models, large language models (LLMs) are prone to make incorrect predictions, hallucinate'' by fabricating claims, or simply generate low-quality output for a given input. We introduce a novel benchmark that implements a collection of state-of-the-art UQ baselines, and provides an environment for controllable and consistent evaluation of novel techniques.
  arXiv  Detail & Related papers  (2024-06-21T20:06:31Z)
• A Software Engineering Perspective on Testing Large Language Models: Research, Practice, Tools and Benchmarks [2.8061460833143346]
  Large Language Models (LLMs) are rapidly becoming ubiquitous both as stand-alone tools and as components of current and future software systems. To enable usage of LLMs in the high-stake or safety-critical systems of 2030, they need to undergo rigorous testing.
  arXiv  Detail & Related papers  (2024-06-12T13:45:45Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• LM-Polygraph: Uncertainty Estimation for Language Models [71.21409522341482]
  Uncertainty estimation (UE) methods are one path to safer, more responsible, and more effective use of large language models (LLMs) We introduce LM-Polygraph, a framework with implementations of a battery of state-of-the-art UE methods for LLMs in text generation tasks, with unified program interfaces in Python. It introduces an extendable benchmark for consistent evaluation of UE techniques by researchers, and a demo web application that enriches the standard chat dialog with confidence scores.
  arXiv  Detail & Related papers  (2023-11-13T15:08:59Z)
• 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)
• Towards Autonomous Testing Agents via Conversational Large Language Models [18.302956037305112]
  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.
  arXiv  Detail & Related papers  (2023-06-08T12:22:38Z)
• 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.