Code Hunt: Searching for Secret Code for Fun
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
Learning to code can be made more effective and sustainable if it is perceived as fun by the learner. Code Hunt uses puzzles that players have to explore by means of clues presented as test cases. Players iteratively modify their code to match the functional behavior of secret solutions. This way of learning to code is very different to learning from a specification. It is essentially re-engineering from test cases. Code Hunt is based on the test/clue generation of Pex, a white-box test generation tool that uses dynamic symbolic execution. Pex performs a guided search to determine feasible execution paths. Conceptually, solving a puzzle is the manual process of conducting search-based test generation: the “test data” to be generated by the player is the player’s code, and the “fitness values” that reflect the closeness of the player’s code to the secret code are the clues (i.e., Pex-generated test cases). This paper is the first one to describe Code Hunt and its extensions over its precursor Pex4Fun. Code Hunt represents a high-impact educational gaming platform that not only internally leverages fitness values to guide test/clue generation but also externally offers fun user experiences where search-based test generation is manually emulated. Because the amount of data is growing all the time, the entire system runs in the cloud on Windows Azure.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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