Practicing Equity Diversity Inclusion (EDI) in Software Development Teams: A Systematic Literature Survey
Bibliographic record
Abstract
Human factors in successful software projects have always been a critical element in software engineering, however, it has always been overshadowed by focusing more on technology and underlying process. This work is inspired by the recent increasing interest from the software engineering research community in human factors and software development by leveraging and understanding some examples of human factors such as Equity, Diversity, and Inclusion (EDI) which are not given due research consideration earlier. We performed a systematic literature review (SLR) to review the state-of-the-art literature on practicing EDI in software development teams despite of country or culture. We found that evidence of comprehensive research about practicing EDI in software development teams is limited, the up-to-date majority focus is on the topic of diversity, whereas research on topics of practicing equity and inclusion in software development teams is sporadic. It is expected that investigating the impact of human factors in the context of EDI’s triangle will generate new knowledge. This will allow software practitioners to understand the benefits of practicing EDI in managing software development teams as well as provides opportunities to incorporate them into the core development process activities. In the end, future research directions for EDI practices in software development teams are also identified.
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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 | 0.039 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".