Dynamic Protocol Recovery

Abstract

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.

Program understanding using ontologies and dynamic analysis

“…In fact, to the best of our knowledge, no other research group but ours is explicitly processing domain knowledge in dynamic analysis for program comprehension. It seems that most of the effort is applied to compressing or summarizing the execution traces, i.e., [Quante and Koschke, 2007] [Smit et al, 2008] [ Kuhn and Greevy, 2006].…”

Matching and Merging of Variant Feature Specifications

“…[8] define similarity measures between web-services to identify candidate services to replace a service in use when it becomes unavailable or unsuitable due to a change. Quante and Koschke [56] propose similarity measures between finite state automata generated by different reverse engineering mechanisms to compare the effectiveness of these mechanisms. Bogdanov and Walkinshaw [57] provide an algorithm for comparing LTSs without relying on the initial state or any particular states of the underlying models as the reference point.…”

Towards Succinctness in Mining Scenario-Based Specifications

“…Many studies propose variants of dynamic analysis based specification mining (e.g., [9] mine boolean expressions describing likely invariants on values of program variables, [4], [15], [31], [33] mine temporal orderings of method calls/network packets as finite state machines, [8], [32] mine frequent patterns of behavior, [7] mine implied message sequence charts). Different from the above mentioned studies, we mine a set of LSCs from traces of program executions.…”

“…In the literature, extracted invariants vary from boolean expressions capturing a relationship between two variables in a particular program point, to frequent patterns of usage behavior, to various automata and temporal rules. The extracted candidate invariants are used to support comprehension, testing, and formal verification tasks (see, e.g., [4], [7], [8], [31], [33], [32]). …”