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Dynamic protocol recovery tries to recover a component's sequencing constraints by means of dynamic analysis. This problem has been tackled by several automaton learning approaches in the past. These approaches are based on the sequence of component method invocations only.We introduce a new dynamic protocol recovery technique based on object process graphs. These graphs contain information about loops and the context in which methods are being called. We describe the transformation of a set of these graphs to a protocol automaton. The additional input, compared to the sole sequence of method calls, results in a more detailed protocol.In a case study, we compare the resulting protocol automata of our approach to those of several existing automaton learning approaches.

Abstract

The fact that software ages holds for embedded automotive software as well as for any other kind of software. In comparison to IT software, the automotive domain has to deal with different kinds of requirements, such as real time properties, feedback control, and constrained resources. Therefore, used programming languages are C -to meet resource constraints -and data flow oriented graphical languagesto meet the used engineering method and notation of feedback control engineers. This makes the software quite different from what the software maintenance and reengineering community is usually working on, and their results are seldom directly applicable.In this paper, we describe results of a Bosch-internal research project that focused on the adaption of existing reengineering techniques and methods to embedded automotive software development. The goal was to make software maintenance more efficient by a) preventing software ageing and b) supporting program comprehension. Our approach was to make existing reengineering techniques usable for series development in an effective and efficient way. The result is a set of reengineering tools and practices that are specialized for the needs of the automotive domain and usable in practice.

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