How Can Game Teams Analyze Hundreds of Performance Test Reports Faster with GOT Online?
When game teams run large-scale automated performance tests across multiple devices, manually reviewing dozens or hundreds of reports becomes inefficient. GOT Online Batch Test Analysis simplifies this process by automatically aggregating test results, identifying performance bottlenecks, highlighting problematic devices, and detecting test failures such as crashes. With a single analysis process, development teams can quickly understand overall compatibility, prioritize optimization tasks, and improve the efficiency of large-scale performance testing.
About GameOptim GameOptim helps Unity developers identify memory issues, rendering bottlenecks, and performance regressions through automated profiling and cloud based performance analysis. Explore more: 🌐 Website: www.gameoptim.com https://www.gameoptim.com/?fopt=blog 📘 Blog: www.gameoptim.com/blog/ https://www.gameoptim.com/blog/ 💼 LinkedIn: www.linkedin.com/company/gameoptim/ https://www.linkedin.com/company/gameoptim/ 🎥 YouTube: GO.PerformanceLab https://www.youtube.com/@GO.PerformanceLab 💬 Discord: GameOptim https://discord.gg/4Jh6hj9gRw ⭐ GitHub: GameOptim https://github.com/GameOptim/unity mobile performance guide 💻 Dev: GameOptim https://dev.to/gameoptim Summary As automated testing becomes increasingly common, game teams often need to run the same test scenarios across multiple real devices to evaluate performance compatibility. However, analyzing large volumes of test data manually creates several challenges: Which devices have serious performance issues? Which performance metrics require optimization first? Did any test cases fail due to crashes or abnormal exits? How can teams quickly identify the root cause from hundreds of reports? To address these challenges, GOT Online introduces Batch Test Analysis , a feature designed for automated large scale testing scenarios. By analyzing multiple GOT Online Overview reports together, teams can automatically generate a structured Excel report containing: Overall test results Optimization priorities Performance issue details Crash test information Performance data overview This allows developers to move from manual report checking to data driven performance optimization. What Is GOT Online Batch Test Analysis? GOT Online Batch Test Analysis is designed for scenarios where game teams combine GOT Online with automated testing frameworks . A typical workflow includes: 1. The game build is generated after compilation. 2. Automated testing scripts install and run the game on multiple real devices. 3. Performance data is automatically collected and uploaded to GameOptim servers. 4. GOT Online analyzes multiple reports together and generates a comprehensive Excel summary. Instead of opening each report individually, developers can quickly identify performance risks across different devices and test scenarios. How Does Batch Test Analysis Work in GOT Online? In the Overview Mode report: 1. Open the Batch Test Analysis feature. 2. Select the performance test data that needs analysis. 3. Click Start Analysis . 4. GOT Online automatically generates an Excel report. The generated report includes five key worksheets: 1. Test Overview Summary 2. Optimization Queue Details 3. Critical Performance Devices 4. Test Failure Details 5. Performance Report Overview What Information Is Included in the Batch Test Analysis Report? 1. Test Overview Summary: How Can Teams Quickly Understand Test Results? The Test Overview Summary sheet provides an overall view of automated test performance. For example, when analyzing 10 test runs: 4 tests may pass performance requirements. 5 tests may fail performance requirements. 1 test may terminate unexpectedly due to a crash. This summary helps development teams quickly understand the overall device compatibility status and identify whether the project meets target performance requirements. 2. Optimization Queue Details: How Does GOT Online Prioritize Performance Issues? Large scale testing often reveals multiple performance problems across different devices. The Optimization Queue Details sheet organizes commonly detected issues and helps teams understand: Which performance problems appear most frequently. Which modules require optimization. Which devices are affected. Which optimization tasks should be addressed first. By combining multiple test results, the recommended optimization priorities become more meaningful and actionable. 3. Critical Performance Devices: How Can Developers Locate Specific Bottlenecks? Batch Test Analysis can identify performance issues down to: Specific devices Specific test scenarios Specific performance modules Developers can also access the original performance report through the provided report links for deeper investigation. This helps teams quickly move from issue discovery to detailed performance analysis. 4. Test Failure Details: How Can Teams Investigate Crashes During Automated Testing? Automated performance testing may reveal not only performance problems but also functional issues, such as: Game crashes Unexpected exits Test execution failures If a crash occurs during testing, GOT Online records relevant information including: Device information Test time Runtime duration before crash Memory usage at failure These details provide important clues for debugging and crash investigation. 5. Performance Report Overview: How Can Teams Review Overall Project Performance? The Performance Report Overview sheet summarizes important performance indicators, including: FPS Memory usage Power consumption Temperature Network transmission It also provides performance distribution across different test cases and scenes, helping teams quickly identify scenarios with potential performance bottlenecks. Why Should Game Teams Use Batch Test Analysis Instead of Manual Review? Traditional compatibility testing mainly focuses on whether a game can run on different devices. GOT Online Batch Test Analysis goes further by providing: Automated performance issue identification Device level bottleneck analysis Optimization priority recommendations Crash information during testing Performance trend overview For teams running GOT Online + automated batch testing workflows , this feature significantly reduces analysis time and helps developers focus on solving the most impactful issues. What Are the Requirements for Using Batch Test Analysis? To use GOT Online Batch Test Analysis, the test data should meet the following conditions: Each GOT Online test record should be longer than 3 minutes . At least 5 test data records are required for analysis. Once these requirements are met, teams can generate batch analysis reports directly from GOT Online Overview Mode. Important Note: Optimizing Automated Testing Scripts When using Poco based automated testing scripts with GOT Online Overview Mode, the scripts themselves may introduce additional CPU overhead. Development teams should optimize their automation scripts to ensure that testing overhead does not affect the accuracy of performance measurement. FAQ Q1: What problems does GOT Online Batch Test Analysis solve? It helps teams analyze multiple performance test reports automatically, identify bottlenecks, prioritize optimization tasks, and detect test failures such as crashes. Q2: Can Batch Test Analysis identify specific problematic devices? Yes. The report can identify affected devices, test scenarios, and performance modules, allowing developers to locate bottlenecks more precisely. Q3: Does Batch Test Analysis only analyze performance issues? No. Besides performance metrics, it can also display test failures such as crashes and abnormal exits, providing additional debugging information. Q4: How many test reports are required to use Batch Test Analysis? At least five GOT Online test records are required, and each test record should have a duration longer than three minutes. Q5: What testing workflow is Batch Test Analysis designed for? It is mainly designed for automated large scale testing workflows where GOT Online collects performance data from multiple real devices and analyzes results together.