## Appendix A Full Questionnaire (Updated Version--60 Questions)
### Defect Debt--Questions 19 to 23  
19 Question 19: Have you checked that there is no error in the training data collection that would cause a significant training dataset to be lost or delayed?  
Stakeholder: Participants  
Score: 5  
Justification: Ensuring error-free training data collection is paramount. Errors in training data can introduce biases and inaccuracies that compound over time, leading to significant technical debt.  
Example: For example, if a racing game's training data incorrectly labels off-track excursions as successful maneuvers, the model may learn to drive off-track, requiring extensive retraining and data cleansing later.  
Question 20: Have you made the right choice in the hyperparameter values?  
Stakeholder: Participants  
Score: 4  
Justification: Choosing the right hyperparameters is essential for model performance and efficiency. Incorrect hyperparameter values can lead to suboptimal performance or slow convergence, impacting the overall effectiveness of the model.  
Example: For instance, incorrect learning rates can cause a model to converge too slowly or not at all, impacting the speed of iteration and potentially leading to a backlog of updates.  
Question 21: Have you made sure that there is no degradation in view prediction quality due to data changes, different code paths, etc.?  
Stakeholder: Participants  
Score: 4  
Justification: Ensuring that changes in data or code paths do not degrade view prediction quality is critical in RL games. Even minor degradation in view prediction quality can affect the player's experience and the game's overall performance.  
Example: An example is a strategy game where unit strengths may change over time; if the model cannot adapt to these changes, its strategies may become obsolete.  
Question 22: Have you quality inspected and validated the model for adequacy before releasing it to production?  
Stakeholder: Participants  
Score: 5  
Justification: Quality inspection and validation of the model before releasing it to production are essential in RL games to ensure that the model performs adequately and meets the desired performance criteria. Releasing an inadequately validated model can lead to poor player experience which is a form of technical debt that is often expensive to address post-release and potentially damage the game's reputation.  
Example: For example, a model that has not been validated for a shooter game might misclassify in-game objects, leading to frustrating gameplay and the need for urgent patches.  
Question 23: Have you implemented mechanisms for rapid adaptation and regular updates to maintain the model's efficiency and relevance in response to changes in data, features, modeling, or infrastructure?  
Stakeholder: Organizers and Participants  
Score: 4  
Justification: Implementing mechanisms for rapid adaptation is essential in the fast-paced environment of games, where data and features can change frequently. Without this, the model may quickly become outdated, accumulating technical debt.  
Example: For example, in a multiplayer online battle arena (MOBA) game, new characters and abilities are introduced regularly; without rapid adaptation, the model's strategies could become ineffective.