Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions
Bibliographic record
Abstract
Introduction : Digital products increasingly reshape industries, influencing human behavior and decision-making. However, the software development teams developing these systems often lack diversity, which may lead to designs that overlook the needs, equal treatment or safety of diverse user groups. These risks highlight the need for fostering diversity and inclusion in software development to create safer, more equitable technology. Method : This research is based on insights from an academic meeting in June 2023 involving 23 software engineering researchers and practitioners. We used the collaborative discussion method 1-2-4-ALL as a systematic research approach and identified six themes around the theme “challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI).” We identified benefits, harms, and future research directions for the four main themes. Then, we discuss the remaining two themes, AI & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes. Results : This research explores the key challenges and research opportunities for promoting SDDI, providing a roadmap to guide both researchers and practitioners. We underline that research around SDDI requires a constant focus on maximizing benefits while minimizing harms, especially to vulnerable groups. As a research community, we must strike this balance in a responsible way.
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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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".