Abstract
Given a white-box component 
with specified unsafe states, we address the problem of automatically generating an interface that captures safe orderings of invocations of 
’s public methods. Method calls in the generated interface are guarded by constraints on their parameters. Unlike previous work, these constraints are generated automatically through an iterative refinement process. Our technique, named Psyco (Predicate-based SYmbolic COmpositional reasoning), employs a novel combination of the L* automata learning algorithm with symbolic execution. The generated interfaces are three-valued, capturing whether a sequence of method invocations is safe, unsafe, or its effect on the component state is unresolved by the symbolic execution engine. We have implemented Psyco as a new prototype tool in the JPF open-source software model checking platform, and we have successfully applied it to several examples.
This research was supported by the NASA CMU grant NNA10DE60C.
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Giannakopoulou, D., Rakamarić, Z., Raman, V. (2012). Symbolic Learning of Component Interfaces. In: Miné, A., Schmidt, D. (eds) Static Analysis. SAS 2012. Lecture Notes in Computer Science, vol 7460. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33125-1_18
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