Mining performance specifications

Abstract

Functional testing is widespread and supported by a multitude of tools, including tools to mine functional specifications. In contrast, non-functional attributes like performance are often less well understood and tested. While many profiling tools are available to gather raw performance data, interpreting this raw data requires expert knowledge and a thorough understanding of the underlying software and hardware infrastructure. In this work we present an approach that mines performance specifications from running systems autonomously. The tool creates performance models during runtime. The mined models are analyzed further to create compact and comprehensive performance assertions. The resulting assertions can be used as an evidence-based performance specification for performance regression testing, performance monitoring, or as a foundation for more formal performance specifications.