CONFPROFITT: A CONFIGURATION-AWARE PERFORMANCE PROFILING, TESTING, AND TUNING FRAMEWORK

Modern computer software systems are complicated. Developers can change the behavior of the software system through software configurations. The large number of configuration option and their interactions make the task of software tuning, testing, and debugging very challenging. Performance is one of the key aspects of non-functional qualities, where performance bugs can cause significant performance degradation and lead to poor user experience. However, performance bugs are difficult to expose, primarily because detecting them requires specific inputs, as well as specific configurations. While researchers have developed techniques to analyze, quantify, detect, and fix performance bugs, many of these techniques are not effective in highly-configurable systems. To improve the non-functional qualities of configurable software systems, testing engineers need to be able to understand the performance influence of configuration options, adjust the performance of a system under different configurations, and detect configuration-related performance bugs. This research will provide an automated framework that allows engineers to effectively analyze performance-influence configuration options, detect performance bugs in highly-configurable software systems, and adjust configuration options to achieve higher long-term performance gains. To understand real-world performance bugs in highly-configurable software systems, we first perform a performance bug characteristics study from three large-scale opensource projects. Many researchers have studied the characteristics of performance bugs from the bug report but few have reported what the experience is when trying to replicate confirmed performance bugs from the perspective of non-domain experts such as researchers. This study is meant to report the challenges and potential workaround to replicate confirmed performance bugs. We also want to share a performance benchmark to provide real-world performance bugs to evaluate future performance testing techniques. Inspired by our performance bug study, we propose a performance profiling approach that can help developers to understand how configuration options and their interactions can influence the performance of a system. The approach uses a combination of dynamic analysis and machine learning techniques, together with configuration sampling techniques, to profile the program execution, analyze configuration options relevant to performance. Next, the framework leverages natural language processing and information retrieval techniques to automatically generate test inputs and configurations to expose performance bugs. Finally, the framework combines reinforcement learning and dynamic state reduction techniques to guide subject application towards achieving higher long-term performance gains