"Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering

[2411.09916] "Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2411.09916 (cs) [Submitted on 15 Nov 2024 ( v1 ), last revised 13 Apr 2026 (this version, v3)] Title: "Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering Authors: Jiessie Tie , Bingsheng Yao , Tianshi Li , Hongbo Fang , Syed Ishtiaque Ahmed , Dakuo Wang , Shurui Zhou View a PDF of the paper titled "Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering, by Jiessie Tie and 6 other authors View PDF HTML (experimental) Abstract: Software engineers are increasingly incorporating AI assistants into their workflows to enhance productivity and alleviate cognitive load. However, experiences with large language models (LLMs) such as ChatGPT vary widely. While some engineers find them useful, others deem them counterproductive due to inaccuracies in their responses. Researchers have also observed that ChatGPT often provides incorrect information. Given these limitations, it is crucial to determine how to effectively integrate LLMs into software engineering (SE) workflow. Analyzing data from 26 participants in a complex web development task, we identified nine failure types categorized into incorrect or incomplete responses, cognitive overload, and context loss. Users attempted to mitigate these issues through scaffolding, prompt clarification, and debugging. However, 17 participants ultimately chose to abandon ChatGPT due to persistent failures. Our quantitative analysis revealed that unhelpful responses increased the likelihood of abandonment by a factor of 11, while each additional prompt reduced abandonment probability by 17%. This study advances the understanding of human-AI interaction in SE tasks and outlines directions for future research and tooling support. Subjects: Software Engineering (cs.SE) Cite as: arXiv:2411.09916 [cs.SE] (or arXiv:2411.09916v3 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2411.09916 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1145/3801972 Focus to learn more DOI(s) linking to related resources Submission history From: Jiessie Tie [ view email ] [v1] Fri, 15 Nov 2024 03:29:41 UTC (5,561 KB) [v2] Tue, 10 Mar 2026 03:05:25 UTC (1,142 KB) [v3] Mon, 13 Apr 2026 23:49:05 UTC (1,142 KB) Full-text links: Access Paper: View a PDF of the paper titled "Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering, by Jiessie Tie and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2024-11 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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