Symbolic Learning of Component Interfaces

2012_SAS_PSYCO screenshot


Given a white-box component C with specified unsafe states, we address the problem of automatically generating an interface that captures safe orderings of invocations of C’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.


Dimitra Giannakopoulou, Zvonimir Rakamaric, Vishwanath Raman
Symbolic Learning of Component Interfaces
Lecture Notes in Computer Science, 7460: 248--264, 2012.


  title = {Symbolic Learning of Component Interfaces},
  author = {Dimitra Giannakopoulou and Zvonimir Rakamaric and Vishwanath Raman},
  journal = {Lecture Notes in Computer Science},
  booktitle = {Proceedings of the 19th International Static Analysis Symposium (SAS 2012)},
  editor = {Antoine Min\'e and David Schmidt},
  publisher = {Springer},
  volume = {7460},
  pages = {248--264},
  year = {2012}