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Preprint . 2026
License: CC BY
Data sources: ZENODO
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ZENODO
Preprint . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
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From Commits to Cognition: A Mixed-Methods Framework for Inferring Developer Mental Models from Repository Artifacts

Authors: Da Silva, Anderson Henrique;

From Commits to Cognition: A Mixed-Methods Framework for Inferring Developer Mental Models from Repository Artifacts

Abstract

Understanding how software developers think remains a fundamental challenge in empirical software engineering. While previous research has examined developer behavior through surveys, interviews, and controlled experiments, few studies have attempted to reverse-engineer cognitive patterns directly from repository artifacts. This paper presents a pilot study combining Mining Software Repositories (MSR), Reflexive Thematic Analysis, and Autoethnography to infer developer mental models from commits, code structure, documentation, and project organization. Through a longitudinal self-study analyzing 40 repositories and 2,799 commits over approximately three years (2022–2025), we identify four dimensions of developer cognition: cognitive, affective, conative, and reflective. The resulting Developer Mental Model Framework (DMMF) provides a replicable protocol for developer self-analysis and offers preliminary insights into the relationship between software artifacts and their creators’ thought processes. We present quantitative analyses of commit patterns, BERT-based sentiment classification, and temporal clustering of refactor sequences alongside qualitative themes from reflexive analysis. As a single-subject exploratory study, our primary contribution is methodological: demonstrating the feasibility of inferring cognitive patterns from repository artifacts. We discuss limitations, validity threats, and directions for multi-subject validation.

Keywords

Mining Software Repositories, Developer Cognition, Autoethnography, Thematic Analysis, Mental Models, Reflective Practice

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green