Socio-Technical Grounded Theory for Software Engineering
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Abstract (arXiv)
Grounded Theory (GT), a sociological research method designed to study social phenomena, is increasingly being used to investigate the human and social aspects of software engineering (SE). However, being written by and for sociologists, GT is often challenging for a majority of SE researchers to understand and apply. Additionally, SE researchers attempting ad hoc adaptations of traditional GT guidelines for modern socio-technical (ST) contexts often struggle in the absence of clear and relevant guidelines to do so, resulting in poor quality studies. To overcome these research community challenges and leverage modern research opportunities, this paper presents Socio-Technical Grounded Theory (STGT) designed to ease application and achieve quality outcomes. It defines what exactly is meant by an ST research context and presents the STGT guidelines that expand GT's philosophical foundations, provide increased clarity and flexibility in its methodological steps and procedures, define possible scope and contexts of application, encourage frequent reporting of a variety of interim, preliminary, and mature outcomes, and introduce nuanced evaluation guidelines for different outcomes. It is hoped that the SE research community and related ST disciplines such as computer science, data science, artificial intelligence, information systems, human computer/robot/AI interaction, human-centered emerging technologies (and increasingly other disciplines being transformed by rapid digitalisation and AI-based augmentation), will benefit from applying STGT to conduct quality research studies and systematically produce rich findings and mature theories with confidence.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2021.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 4; 2026: 1.
7 papers.
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- 2026Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers28 Aug 2026
- 2025How Far Are We from Genuinely Useful Deep Research Agents?1 Dec 2025
- 2025Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective9 Nov 2025
- 2025AI-Mediated Code Comment Improvement13 May 2025
- 2025TOAST Framework: A Multidimensional Approach to Ethical and Sustainable AI Integration in Organizations7 Jan 2025
- 2024Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development21 Mar 2024
- 2023Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges14 Jul 2023