Extracting configuration parameter interactions using static analysis

Complex software systems come with a huge number of configuration parameters for tuning their performance as well as functionality. It is a challenge for the users of such systems to understand how various parameters interact, and causing them to use the default configuration settings to avoid problems. Studies show that a lot of performance optimization opportunities are missed when complex software systems are run with the default configuration settings. This paper proposes a graph-based representation of configuration parameter interactions that are extracted using a scaleable static analysis approach. Experimental results obtained with a case study on a data analysis framework, Apache Hadoop, suggest that the proposed approach is effective in capturing some of the interactions at the component level.