Reliable effects screening: A distributed continuous quality assurance process for monitoring performance degradation in evolving software systems

Abstract—Developers of highly configurable performance-intensive software systems often use in-house performance-oriented “regression testing ” to ensure that their modifications do not adversely affect their software’s performance across its large configuration space. Unfortunately, time and resource constraints can limit in-house testing to a relatively small number of possible configurations, followed by unreliable extrapolation from these results to the entire configuration space. As a result, many performance bottlenecks escape detection until systems are fielded. In our earlier work, we improved the situation outlined above by developing an initial quality assurance process called “main effects screening. ” This process 1) executes formally designed experiments to identify an appropriate subset of configurations on which to base the performance-oriented regression testing, 2) executes benchmarks on this subset whenever the software changes, and 3) provides tool support for executing these actions on in-the-field and in-house computing resources. Our initial process had several limitations, however, since it was manually configured (which was tedious and error-prone) and relied on strong and untested assumptions for its accuracy (which made its use unacceptably risky in practice). This paper presents a new quality assurance process called “reliable effects screening ” that provides three significant improvements to our earlier work. First, it allows developers to economically verify key assumptions during process execution. Second, it integrates several model-driven engineering tools to make process configuration and execution much easier and less error prone. Third, we evaluate this process via several feasibility studies of three large, widely used performance-intensive software frameworks. Our results indicate that reliable effects screening can detect performance degradation in large-scale systems more reliably and with significantly les