Automatic configuration tuning helps users who intend to improve software performance. However, the auto-tuners are limited by the huge configuration search space. More importantly, they focus only on performance improvement while being unaware of other important user intentions (e.g., reliability or security). To reduce the search space, researchers mainly focus on pre-selecting performance-related parameters which requires a heavy stage of dynamically running under different configurations to build performance models. Given that other important user intentions are not paid attention to, we focus on guiding users in pre-selecting performance-related parameters in general while warning about side-effects on non-performance intentions. We find that the configuration document often, if it does not always, contains rich information about the parameters’ relationship with diverse user intentions, but documents might also be long and domain-specific.

In this paper, we first conduct a comprehensive study on 13 representative software containing 7,325 configuration parameters, and derive six types of ways in which configuration parameters may affect non-performance intentions. Guided by this study, we design SafeTune, a multi-intention-aware method that pre-selects important performance-related parameters and warns about their side-effects on non-performance intentions. Evaluation on target software shows that SafeTune correctly identifies 22-26 performance-related parameters that are missed by state-of-the-art tools but have significant performance impacts (up to 14.7x). Furthermore, we illustrate eight representative cases to show that SafeTune can effectively prevent real-world and critical side-effects on other user intentions.