Towards Generating Executable Metamorphic Relations Using Large Language Models
- URL: http://arxiv.org/abs/2401.17019v3
- Date: Fri, 11 Oct 2024 09:07:22 GMT
- Title: Towards Generating Executable Metamorphic Relations Using Large Language Models
- Authors: Seung Yeob Shin, Fabrizio Pastore, Domenico Bianculli, Alexandra Baicoianu,
- Abstract summary: We propose an approach for automatically deriving executable MRs from requirements using large language models (LLMs)
To assess the feasibility of our approach, we conducted a questionnaire-based survey in collaboration with Siemens Industry Software.
- Score: 46.26208489175692
- License:
- Abstract: Metamorphic testing (MT) has proven to be a successful solution to automating testing and addressing the oracle problem. However, it entails manually deriving metamorphic relations (MRs) and converting them into an executable form; these steps are time-consuming and may prevent the adoption of MT. In this paper, we propose an approach for automatically deriving executable MRs (EMRs) from requirements using large language models (LLMs). Instead of merely asking the LLM to produce EMRs, our approach relies on a few-shot prompting strategy to instruct the LLM to perform activities in the MT process, by providing requirements and API specifications, as one would do with software engineers. To assess the feasibility of our approach, we conducted a questionnaire-based survey in collaboration with Siemens Industry Software, a worldwide leader in providing industry software and services, focusing on four of their software applications. Additionally, we evaluated the accuracy of the generated EMRs for a Web application. The outcomes of our study are highly promising, as they demonstrate the capability of our approach to generate MRs and EMRs that are both comprehensible and pertinent for testing purposes.
Related papers
- AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs.
Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
arXiv Detail & Related papers (2024-11-02T13:24:30Z) - AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML [56.565200973244146]
Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline.
Recent works have started exploiting large language models (LLM) to lessen such burden.
This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML.
arXiv Detail & Related papers (2024-10-03T20:01:09Z) - Re-Thinking Process Mining in the AI-Based Agents Era [39.58317527488534]
Large Language Models (LLMs) have emerged as powerful conversational interfaces, and their application in process mining (PM) tasks has shown promising results.
This paper proposes utilizing the AI-Based Agents (AgWf) paradigm to enhance the effectiveness of PM on LLMs.
We examine various implementations of AgWf and the types of AI-based tasks involved.
arXiv Detail & Related papers (2024-08-14T10:14:18Z) - ORLM: Training Large Language Models for Optimization Modeling [16.348267803499404]
Large Language Models (LLMs) have emerged as powerful tools for tackling complex Operations Research (OR) problem.
To tackle this issue, we propose training open-source LLMs for optimization modeling.
Our best-performing ORLM achieves state-of-the-art performance on the NL4OPT, MAMO, and IndustryOR benchmarks.
arXiv Detail & Related papers (2024-05-28T01:55:35Z) - Using Large Language Models to Understand Telecom Standards [35.343893798039765]
Large Language Models (LLMs) may provide faster access to relevant information.
We evaluate the capability of state-of-art LLMs to be used as Question Answering (QA) assistants.
Results show that LLMs can be used as a credible reference tool on telecom technical documents.
arXiv Detail & Related papers (2024-04-02T09:54:51Z) - TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation.
Specifically, task decomposition, tool selection, and parameter prediction are assessed.
Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
arXiv Detail & Related papers (2023-11-30T18:02:44Z) - Benchmarking Automated Machine Learning Methods for Price Forecasting
Applications [58.720142291102135]
We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions.
Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part.
We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
arXiv Detail & Related papers (2023-04-28T10:27:38Z) - Just Tell Me: Prompt Engineering in Business Process Management [63.08166397142146]
GPT-3 and other language models (LMs) can effectively address various natural language processing (NLP) tasks.
We argue that prompt engineering can help bring the capabilities of LMs to BPM research.
arXiv Detail & Related papers (2023-04-14T14:55:19Z)
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.