Navigating the Path of Women in Software Engineering: From Academia to Industry
Bibliographic record
Abstract
Context. Women remain significantly underrepresented in software engineering, leading to a lasting gender gap in the software industry. This disparity starts in education and extends into the industry, causing challenges such as hostile work environments and unequal opportunities. Addressing these issues is crucial for fostering an inclusive and diverse software engineering workforce. Aim. This study aims to enhance the literature on women in software engineering, exploring their journey from academia to industry and discussing perspectives, challenges, and support. We focus on Brazilian women to extend existing research, which has largely focused on North American and European contexts. Method. In this study, we conducted a cross-sectional survey, collecting both quantitative and qualitative data, focusing on women's experiences in software engineering to explore their journey from university to the software industry. Findings. Our findings highlight persistent challenges faced by women in software engineering, including gender bias, harassment, work-life imbalance, undervaluation, low sense of belonging, and impostor syndrome. These difficulties commonly emerge from university experiences and continue to affect women throughout their entire careers. Conclusion. In summary, our study identifies systemic challenges in women's software engineering journey, emphasizing the need for organizational commitment to address these issues. We provide actionable recommendations for practitioners.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".