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 it escalates 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.

Software engineering tasks are high-stress and cognitively demanding. Additionally, there is a latent risk of software engineers presenting burnout, depression and anxiety. Established interventions in other fields centred around attention awareness have shown positive results in mental well-being.

Requirements serve as the foundation for defining what a software product should accomplish, highlighting the importance of clear and well-written specifications [1]. Deficient requirements often lead to defects in delivered software, which can be challenging and costly to rectify [2].

Abstract Context Computer workers in general, and software developers specifically, are under a high amount of stress due to continuous deadlines and, often, over-commitment. Objective This study investigates the effects of a neuroplasticity practice, a specific breathing practice, on the attention awareness, well-being, perceived productivity, and self-efficacy of computer workers. Method The intervention was a 12-week program with a weekly live session that included a talk on a well-being topic and a facilitated group breathing session. During the intervention period, we solicited one daily journal note and one weekly well-being rating. We created a questionnaire mainly from existing, validated scales as entry and exit survey for data points for comparison before and after the intervention. We replicated the intervention in a similarly structured 8-week program. The data was analyzed using Bayesian multi-level models for the quantitative part and thematic analysis for the qualitative part. Results The intervention showed improvements in participants’ experienced inner states despite an ongoing pandemic and intense outer circumstances for most. Over the course of the study, we found an improvement in the participants’ ratings of how often they found themselves in good spirits as well as in a calm and relaxed state. We also aggregate a large number of deep inner reflections and growth processes that may not have surfaced for the participants without deliberate engagement in such a program. Conclusion The data indicates usefulness and effectiveness of an intervention for computer workers in terms of increasing well-being and resilience. Everyone needs a way to deliberately relax, unplug, and recover. A breathing practice is a simple way to do so, and the results call for establishing a larger body of work to make this common practice.