Software Engineering ResearchSoftware Engineering Techniques and Practices
Abstract
Generative AI has entered software work with remarkable speed, yet trust in its output remains unsettled. This study examines how developers and learners navigate AI-assisted software work through mental models and trust. Analyzing 40,052 responses from the 2025 Stack Overflow Developer Survey, the findings reveal a durable paradox: professionals use AI more intensively than learners but trust it less, evaluating its capability for complex tasks more cautiously. The primary threat to trust is corrective labor, such as debugging time, rather than code legibility. Cluster analysis identifies four mental-model profiles—open but light users, skeptical dependents, resistant/disengaged respondents, and enthusiastic adopters—with professionals disproportionately concentrated in skeptical dependence. Ultimately, adoption does not settle the trust question; instead, mental models act as interpretive filters through which developers decide when to rely, verify, and pull back.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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