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Data · dataset · 2026

AI-Assisted Empirical Software Engineering: Lessons and Methodological Guidelines from a Qualitative Study [Dataset]

Listed in Teesside University Research Data Repository

Details about our qualitative empirical software engineering study supported by AI-assistance and performed in my doctoral research.

Description

The study conducted 18 semi-structured interviews with project managers, software engineers, designers, and testers to perform deep interpretive analyses aiming to understand their experiences and challenges in software development contracting stage at CESAR, a leading Brazilian Institute of Science and Technology focused on software-driven innovation projects.

The dataset includes research questions, guide interview versions, prompts submitted to some AI-assisted tool (chatGPT, Gemini, Atlas.TI) in order to help in relevant qualitative analysis activities like refining interview guide, transcription of audio interview, coding identification over transcription, text translation and revision.

Links

Where it is published

Catalogue records · 1

Topics

Inferred from text
Audio 75% · Text 75%
Provenance · 1 source records, 15 field assertions
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Teesside University Research Data Repositoryoai:data.mendeley.com/tvn4dmdffh.17 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[field].anzsrc:group:4612mapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Software Engineering']
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concepts[modality].local:modality:textenrichment · researchdata tees ac ukkeyword-concept-rules@1.0.0title+description (75%)
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