Software Engineering by and for Humans in an AI Era
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it