SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video Games Using Risk Based Testing and Machine Learning

Testing video games is an increasingly difficult task as traditional methods fail to scale with growing software systems. Manual testing is a very labor-intensive process, and therefore quickly becomes cost prohibitive. Using scripts for automated testing is affordable, however scripts are ineffective in non-deterministic environments, and knowing when to run each test is another problem altogether. The modern game's complexity, scope, and player expectations are rapidly increasing where quality control is a big portion of the production cost and delivery risk. Reducing this risk and making production happen is a big challenge for the industry currently. To keep production costs realistic up-to and after release, we are focusing on preventive quality assurance tactics alongside testing and data analysis automation. We present SUPERNOVA (Selection of tests and Universal defect Prevention in External Repositories for Novel Objective Verification of software Anomalies), a system responsible for test selection and defect prevention while also functioning as an automation hub. By integrating data analysis functionality with machine and deep learning capability, SUPERNOVA assists quality assurance testers in finding bugs and developers in reducing defects, which improves stability during the production cycle and keeps testing costs under control. The direct impact of this has been observed to be a reduction in 55% or more testing hours for an undisclosed sports game title that has shipped, which was using these test selection optimizations. Furthermore, using risk scores generated by a semi-supervised machine learning model, we are able to detect with 71% precision and 77% recall the probability of a change-list being bug inducing, and provide a detailed breakdown of this inference to developers. These efforts improve workflow and reduce testing hours required on game titles in development.

On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering

Duplicate question detection is an ongoing challenge in community question answering because semantically equivalent questions can have significantly different words and structures. In addition, the identification of duplicate questions can reduce the resources required for retrieval, when the same questions are not repeated. This study compares the performance of deep neural networks and gradient tree boosting, and explores the possibility of domain adaptation with transfer learning to improve the under-performing target domains for the text-pair duplicates classification task, using three heterogeneous datasets: general-purpose Quora, technical Ask Ubuntu, and academic English Stack Exchange. Ultimately, our study exposes the alternative hypothesis that the meaning of a "duplicate" is not inherently general-purpose, but rather is dependent on the domain of learning, hence reducing the chance of transfer learning through adapting to the domain.