Engineering Gameful Systems: Insights from the 9th International GAS Workshop at ICSE 2025
Bibliographic record
Abstract
As gameful systems, spanning entertainment games, serious games, and gamified applications, continue to grow in complexity and societal impact, there is an urgent need for structured, interdisciplinary approaches to their engineering. This paper presents an overview and reflection on the 9th International Workshop on Games and Software Engineering (GAS 2025), held in conjunction with the IEEE/ACM International Conference on Software Engineering (ICSE 2025). The workshop brought together researchers and practitioners from software engineering and game development to address the methodological, technical, and ethical challenges of engineering gameful systems. GAS 2025 featured a dynamic program that included a keynote, peer-reviewed paper presentations, and an interactive session exploring the use of AWS for Games. Contributions covered a wide range of topics including AI-driven development, playtesting techniques, procedural content generation, HCI/CHI innovations, and lifecycle processes tailored for gameful applications. The workshop fostered rich discussions that helped identify emerging research directions, practical tools and methodologies, and community-driven initiatives for ongoing collaboration. This paper synthesizes the key insights, themes, and outcomes of GAS 2025, highlighting its role in shaping a shared research agenda at the intersection of software engineering and game studies.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".