Games and Software Engineering
Bibliographic record
Abstract
Games are a popular form of entertainment and, due to their nature (i.e., interactive, immersive, etc.), strongly lend themselves for use beyond this original intent. Serious games, or games with a purpose, have been introduced to integrate the entertainment value games with domain specific objectives on important topics within education, health, and the environment to mention a few. In addition, gamification has been used to enhance nonentertainment applications with game elements; it aspires to foster behavioral changes, engagement, motivation, and participation in activities. In this context, the actions performed have meaning/value in the game experience in order to improve workplace performance or learn something in real life. The growing adoption of gameful experiences in all of the previous contexts make their design and development increasingly complex due to, for example, the number and variety of users, and their potential mission criticality. This complexity is nurtured, among the other factors, by a lack of theoretical grounding and adequate frameworks to engineer the intended solutions. In this paper, we report the outcomes of the 6th International Workshop on Games and Software Engineering: Engineering fun, inspiration, and motivation (GAS 2023 ) 1, which was held as part of the 44th International Conference on Software Engineering (ICSE 2022) in Pittsburgh, PA, USA on May 20, 2022. The workshop program includes two exciting keynotes discussing topics related to training and learning, and fulfilling the promise and potential of gamification. The two paper sessions examined gamification from the perspectives of software project, testing, and, design. The conclusion of the workshop is anchored by a panel of four highly qualified researchers and practitioners discussing lessons learned and the future of gamification.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".