Emotional Strain and Frustration in LLM Interactions in Software Engineering

• URL: http://arxiv.org/abs/2504.10050v3
• Date: Sun, 20 Apr 2025 10:41:35 GMT
• Title: Emotional Strain and Frustration in LLM Interactions in Software Engineering
• Authors: Cristina Martinez Montes, Ranim Khojah,
• Abstract summary: Large Language Models (LLMs) are increasingly integrated into various daily tasks in Software Engineering.<n>Frustration can negatively impact engineers' productivity and well-being if they escalate into stress and burnout.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) are increasingly integrated into various daily tasks in Software Engineering such as coding and requirement elicitation. Despite their various capabilities and constant use, some interactions can lead to unexpected challenges (e.g. hallucinations or verbose answers) and, in turn, cause emotions that develop into frustration. Frustration can negatively impact engineers' productivity and well-being if they escalate into stress and burnout. In this paper, we assess the impact of LLM interactions on software engineers' emotional responses, specifically strains, and identify common causes of frustration when interacting with LLMs at work. Based on 62 survey responses from software engineers in industry and academia across various companies and universities, we found that a majority of our respondents experience frustrations or other related emotions regardless of the nature of their work. Additionally, our results showed that frustration mainly stemmed from issues with correctness and less critical issues such as adaptability to context or specific format. While such issues may not cause frustration in general, artefacts that do not follow certain preferences, standards, or best practices can make the output unusable without extensive modification, causing frustration over time. In addition to the frustration triggers, our study offers guidelines to improve the software engineers' experience, aiming to minimise long-term consequences on mental health.

Related papers

• I Felt Pressured to Give 100% All the Time: How Are Neurodivergent Professionals Being Included in Software Development Teams? [0.46873264197900916]
  This study seeks to understand the work experiences of neurodivergent professionals acting in different software development roles.<n>We applied the Sociotechnical Theory (STS) to investigate how the social structures of organizations and their respective work technologies influence the inclusion of these professionals.
  arXiv  Detail & Related papers  (2025-03-12T02:28:59Z)
• Seeker: Towards Exception Safety Code Generation with Intermediate Language Agents Framework [58.36391985790157]
  In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code. We explore the use of large language models (LLMs) to improve exception handling in code. We propose Seeker, a multi-agent framework inspired by expert developer strategies for exception handling.
  arXiv  Detail & Related papers  (2024-12-16T12:35:29Z)
• Seeker: Enhancing Exception Handling in Code with LLM-based Multi-Agent Approach [54.03528377384397]
  In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code. We explore the use of large language models (LLMs) to improve exception handling in code. We propose Seeker, a multi agent framework inspired by expert developer strategies for exception handling.
  arXiv  Detail & Related papers  (2024-10-09T14:45:45Z)
• BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom's-Taxonomy-Inspired Prompts [59.83547898874152]
  We introduce BloomWise, a new prompting technique, inspired by Bloom's taxonomy, to improve the performance of Large Language Models (LLMs) The decision regarding the need to employ more sophisticated cognitive skills is based on self-evaluation performed by the LLM. In extensive experiments across 4 popular math reasoning datasets, we have demonstrated the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2024-10-05T09:27:52Z)
• Overwhelmed Software Developers [41.12484194361493]
  This study explores the experience of feeling overwhelmed in the realm of software development. We have identified seven distinct categories: communication-induced, disturbance-related, organizational, variety, technical, temporal, and positive overwhelm. We believe that software developers possess unique traits that may enable them to navigate through the storm of overwhelm more effectively.
  arXiv  Detail & Related papers  (2024-06-06T13:25:18Z)
• Overwhelmed software developers: An Interpretative Phenomenological Analysis [43.18707677931078]
  We interviewed two software developers who have experienced overwhelm recently. We uncover seven categories of overwhelm (communication, disturbance, organizational, variety, technical, temporal, and positive overwhelm) Participants reported that overwhelm can sometimes be experienced to be positive and pleasant, and it can increase their mental focus, self ambition, and productivity.
  arXiv  Detail & Related papers  (2024-01-05T12:39:08Z)
• Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMs [9.298552727430485]
  Large-scale software engineering-specific datasets that can be used to train accurate machine learning models are required. This paper explores zero-shot LLMs that are pre-trained on massive datasets but without being fine-tuned specifically for the task of detecting emotion causes in software engineering.
  arXiv  Detail & Related papers  (2023-12-15T12:16:16Z)
• Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench [83.41621219298489]
  We evaluate Large Language Models' (LLMs) anthropomorphic capabilities using the emotion appraisal theory from psychology. We collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. We conduct a human evaluation involving more than 1,200 subjects worldwide.
  arXiv  Detail & Related papers  (2023-08-07T15:18:30Z)
• Exploring the Relationship Between Personality Traits and User Feedback [9.289846887298852]
  We present a preliminary study about the effect of personality traits on user feedback. 56 university students provided feedback on different software features of an e-learning tool used in the course. Results suggest that sensitivity to frustration and lower stress tolerance may negatively impact the feedback of users.
  arXiv  Detail & Related papers  (2023-07-22T10:10:27Z)
• Large Language Models Understand and Can be Enhanced by Emotional Stimuli [53.53886609012119]
  We take the first step towards exploring the ability of Large Language Models to understand emotional stimuli. Our experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks.
  arXiv  Detail & Related papers  (2023-07-14T00:57:12Z)

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.