ConfProf: White-Box Performance Profiling of Configuration Options

Modern software systems are highly customizable through configuration options. The sheer size of the configuration space makes it challenging to understand the performance influence of individual configuration options and their interactions under a specific usage scenario. Software with poor performance may lead to low system throughput and long response time. This paper presents ConfProf, a white-box performance profiling technique with a focus on configuration options. ConfProf helps developers understand how configuration options and their interactions influence the performance of a software system. The approach combines dynamic program analysis, machine learning, and feedback-directed configuration sampling to profile the program execution and analyze the performance influence of configuration options. Compared to existing approaches, ConfProf uses a white-box approach combined with machine learning to rank performance-influencing configuration options from execution traces. We evaluate the approach with 13 scenarios of four real-world, highly-configurable software systems. The results show that ConfProf ranks performance-influencing configuration options with high accuracy and outperform a state of the art technique.