More than just Software Surprises: Purposes, Processes, and Directions for Software Application Easter Eggs
Bibliographic record
Abstract
"Easter eggs" are features hidden inside software, and the practice of developers including them is a long-standing global phenomenon. They have seen some investigation in the context of games, but despite their prevalence in non-game software applications, their nature within this context is less clear. We perform a qualitative, investigative analysis of Easter eggs in non-game software application contexts, using primarily archival research including discussion forums, social media posts, and user-created online databases, along with select developer interviews. Our work uncovers the stories behind, motivations for creating, and intended perceptions of Easter eggs, which we present as categories of purposes with illustrative examples. This analysis also informs a categorization and discussion of processes that Easter eggs undergo concerning the social and emotional circumstances of their developers and users. Finally, we use our results to motivate future directions for applying Easter eggs in user interfaces.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".