Modern IDEs such as Eclipse offer static views of the source code, but such views ignore information about the run-time behavior of software systems. Since typical object-oriented systems make heavy use of polymorphism and dynamic binding, static views will miss key information about the run-time architecture. In this paper we show by means of a controlled experiment with 30 professional developers that for typical software maintenance tasks integrating dynamic information into the Eclipse IDE yields a significant 17.5% decrease of time spent while significantly increasing the correctness of the solutions by 33.5%. Furthermore, we describe several enhancements to the Eclipse IDE that integrate static and dynamic information, with the goal of better supporting typical software maintenance activities. We elaborate on a case study which further highlights the usefulness of dynamic information for performance optimizations. We also report on several important efficiency improvements to our dynamic information collection framework, and we present benchmarks evaluating the overhead of our approach.

Mainstream IDEs such as Eclipse support developers in managing software projects mainly by offering static views of the source code. Such a static perspective neglects any information about runtime behavior. However, object-oriented programs heavily rely on polymorphism and late-binding, which makes them difficult to understand just based on their static structure. Developers thus resort to debuggers or profilers to study the system's dynamics. However, the information provided by these tools is volatile and hence cannot be exploited to ease the navigation of the source space. In this paper we present an approach to augment the static source perspective with dynamic metrics such as precise runtime type information, or memory and object allocation statistics. Dynamic metrics can leverage the understanding for the behavior and structure of a system. We rely on dynamic data gathering based on aspects to analyze running Java systems. By solving concrete use cases we illustrate how dynamic metrics directly available in the IDE are useful. We also comprehensively report on the efficiency of our approach to gather dynamic metrics.

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