Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Load testing is crucial to uncover functional and performance bugs in large-scale systems. Load tests generate vast amounts of performance data, which needs to be compared and analyzed in limited time across tests. This helps performance analysts to understand the resource usage of an application and to find out if an application is meeting its performance goals. The biggest challenge for performance analysts is to identify the few important performance counters in the highly redundant performance data. ,  employed a statistical technique, Principal Component Analysis (PCA) to reduce the large volume of performance counter data, to a smaller, more meaningful and manageable set. Furthermore, our methodology automates the process of comparing the important counters across load tests to identify performance gains/losses. A case study on load test data of a large enterprise application shows that our methodology can effectively guide performance analysts to identify and compare top performance counters across tests in limited time.
['Haroon Malik', 'Zhen Ming Jiang', 'Bram Adams', 'Ahmed E. Hassan', 'Parminder Flora', 'Gilbert Hamann']
I am writing a research paper on a idea Automatic Comparison of Load Tests to Support the Performance Analysis of Large Enterprise Systems
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Comparison of Load Tests to Support the Performance Analysis of Large Enterprise Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Load testing is crucial to uncover functional and performance bugs in large-scale systems. Load tests generate vast amounts of performance data, which needs to be compared and analyzed in limited time across tests. This helps performance analysts to understand the resource usage of an application and to find out if an application is meeting its performance goals. The biggest challenge for performance analysts is to identify the few important performance counters in the highly redundant performance data. ,  employed a statistical technique, Principal Component Analysis (PCA) to reduce the large volume of performance counter data, to a smaller, more meaningful and manageable set. Furthermore, our methodology automates the process of comparing the important counters across load tests to identify performance gains/losses. A case study on load test data of a large enterprise application shows that our methodology can effectively guide performance analysts to identify and compare top performance counters across tests in limited time."}]