Why Do We Code? A Theory on Motivations and Challenges in Software Engineering from Education to Practice
Bibliographic record
Abstract
Motivations and challenges jointly shape how individuals enter, persist, and evolve within software engineering (SE), yet their interplay remains underexplored across the transition from education to professional practice. We conducted 15 semi-structured interviews and employed the Gioia Methodology, an adapted grounded theory methodology from organizational behavior, to inductively derive taxonomies of motivations and challenges, and build the Exposure-Pursuit-Evaluation (EPE) Process Model. Our findings reveal that impactful early exposure triggers intrinsic motivations, while non-impactful exposure requires an extrinsic push (e.g., career/ personal goals, external validation). We identify curiosity and avoiding alternatives as a distinct educational drivers, and barriers to belonging as the only challenge persisting across education and career. Our findings show that career progression challenges (e.g., navigating the corporate world) constrain extrinsic fulfillment while technical training challenges, barriers to belonging and threats to motivation constrain intrinsic fulfillment. The theory shows how unmet motivations and recurring challenges influence persistence, career shifts, or departure from the field. Our results provide a grounded model for designing interventions that strengthen intrinsic fulfillment and reduce systemic barriers in SE education and practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".