Towards a framework for exploratory testing in video games
Bibliographic record
Abstract
Game testing ensures quality and is typically performed by human testers. Although game testing has advanced, the literature lacks research on generating human-crafted game tests. Traditional software testing techniques, such as exploratory testing, can be adapted to video games. We introduce XPloiT, a framework for exploratory testing of video games. We developed it using insights from traditional exploratory software testing, game testing, and prior research. The framework includes testing strategies tailored for video games, a recommended strategy order, and guidelines for applying each strategy. As an initial evaluation, we applied XPloiT to a platform game under development by an indie studio. XPloiT uncovered several bugs, more than half of which were previously unknown and confirmed by the developer. These findings support further development and assessment of the framework.
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 | 0.049 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".