Software Engineering by and for Humans in an AI Era
Bibliographic record
Abstract
The landscape of software engineering is undergoing a transformative shift driven by advancements in machine learning, Artificial Intelligence (AI), and autonomous systems. This roadmap article explores how these technologies are reshaping the field, positioning humans not only as end users but also as critical components within expansive software ecosystems. We examine the challenges and opportunities arising from this human-centered paradigm, including ethical considerations, fairness, and the intricate interplay between technical and human factors. By recognizing humans at the heart of the software lifecycle—spanning professional engineers, end users, and end user developers—we emphasize the importance of inclusivity, human-aligned workflows, and the seamless integration of AI-augmented socio-technical systems. As software systems evolve to become more intelligent and human-centric, software engineering practices must adapt to this new reality. This article provides a comprehensive examination of this transformation, outlining current trends, key challenges, and opportunities that define the emerging research and practice landscape, and envisioning a future where software engineering and AI work synergistically to place humans at the core of the ecosystem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".