Modeling runtime behavior in framework-based applications

Our research group has analyzed many industrial, framework-based applications. In these applications, simple functionality often requires excessive runtime activity. It is increasingly difficult to assess if and how inefficiencies can be fixed. Much of this activity involves the transformation of information, due to framework couplings. We present an approach to modeling and quantifying behavior in terms of what transformations accomplish.

We structure activity into dataflow diagrams that capture the flow between transformations. Across disparate implementations, we observe commonalities in how transformations use and change their inputs. We introduce vocabulary of common phenomena of use and change, and four ways to classify data and transformations using this vocabulary. The structuring and classification enable evaluation and comparison in terms abstracted from implementation specifics. We introduce metrics of complexity and cost, including behavior signatures that attribute measures to phenomena. We demonstrate the approach on a benchmark, a library, and two industrial applications.