Software testing by active learning for commercial games
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
As software systems have become larger, exhaustive testing has become increasingly onerous. This has rendered statistical software testing and machine learning techniques increasingly attractive. Drawing from both of these, we present an active learning framework for blackbox software testing. The active learning approach samples input/output pairs from a blackbox and learns a model of the system’s behaviour. This model is then used to select new inputs for sampling. This framework has been developed in the context of commercial video games, complex virtual worlds with highdimensional state spaces, too large for exhaustive testing. Beyond its correctness, developers need to evaluate the gameplay of a game, properties such as difculty . We use the learned model not only to guide sampling but also to summarize the game’s behaviour for the developer to evaluate. We present results from our semi-automated gameplay analysis by machine learning (SAGA-ML) tool applied to Electronics Arts’ FIFA Soccer game.
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 | 0.001 | 0.006 |
| 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 | 0.001 | 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