Testing concurrent systems requires exploring all possible non-deterministic interleavings that the concurrent execution may have, as any of the interleavings may reveal an erroneous behaviour of the system. This introduces a combinatorial explosion on the number of states that must be considered, which leads often to a computationally intractable problem. In the present PhD thesis, this challenge will be addressed through the development of new Partial Order Reduction techniques (POR). The cornerstone of POR theory is the notion of independence, that is used to decided whether each pair of concurrent events p and t are in a race and thus both executions p· t and t · p must be explored. A fundamental goal of this thesis is to introduce notions of conditional independence –which ensures the commutativity of the considered events p and t under certain conditions that can be evaluated in the explored state– with a DPOR algorithm in order to alleviate the combinatorial explosion problem. The new techniques that we propose in the thesis have been implemented within the SYCO tool. We have carried out accompanying experimental evaluations to prove the effectiveness and applicability of the proposed techniques. Finally, we have successfully verified a range of properties for several case studies of Software-Defined Networks to illustrate the potential of the approach, scaling to larger networks than related techniques.

@inproceedings{paperbot5372,
  title = {Conditional dynamic partial order reduction and optimality results},
  author = {Miguel Isabel},
  booktitle = {ISSTA},
  year = {2019},
  doi = {10.1145/3293882.3338987}
}