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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.12294 |
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| _version_ | 1866915553622360064 |
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| author | Balogh, Gergő Kószó, Dávid Mahalegi, Homayoun Safarpour Motealegh Tóth, László Szakács, Bence Búcsú, Áron |
| author_facet | Balogh, Gergő Kószó, Dávid Mahalegi, Homayoun Safarpour Motealegh Tóth, László Szakács, Bence Búcsú, Áron |
| contents | Understanding how software developers think, make decisions, and behave remains a key challenge in software engineering (SE). Verbalization techniques (methods that capture spoken or written thought processes) offer a lightweight and accessible way to study these cognitive aspects. This paper presents a scoping review of research at the intersection of SE and psychology (PSY), focusing on the use of verbal data. To make large-scale interdisciplinary reviews feasible, we employed a large language model (LLM)-assisted screening pipeline using GPT to assess the relevance of over 9,000 papers based solely on titles. We addressed two questions: what themes emerge from verbalization-related work in SE, and how effective are LLMs in supporting interdisciplinary review processes? We validated GPT's outputs against human reviewers and found high consistency, with a 13\% disagreement rate. Prominent themes mainly were tied to the craft of SE, while more human-centered topics were underrepresented. The data also suggests that SE frequently draws on PSY methods, whereas the reverse is rare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12294 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Show Your Title! A Scoping Review on Verbalization in Software Engineering with LLM-Assisted Screening Balogh, Gergő Kószó, Dávid Mahalegi, Homayoun Safarpour Motealegh Tóth, László Szakács, Bence Búcsú, Áron Software Engineering Understanding how software developers think, make decisions, and behave remains a key challenge in software engineering (SE). Verbalization techniques (methods that capture spoken or written thought processes) offer a lightweight and accessible way to study these cognitive aspects. This paper presents a scoping review of research at the intersection of SE and psychology (PSY), focusing on the use of verbal data. To make large-scale interdisciplinary reviews feasible, we employed a large language model (LLM)-assisted screening pipeline using GPT to assess the relevance of over 9,000 papers based solely on titles. We addressed two questions: what themes emerge from verbalization-related work in SE, and how effective are LLMs in supporting interdisciplinary review processes? We validated GPT's outputs against human reviewers and found high consistency, with a 13\% disagreement rate. Prominent themes mainly were tied to the craft of SE, while more human-centered topics were underrepresented. The data also suggests that SE frequently draws on PSY methods, whereas the reverse is rare. |
| title | Show Your Title! A Scoping Review on Verbalization in Software Engineering with LLM-Assisted Screening |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.12294 |