3 research outputs found
Concolic Testing Heap-Manipulating Programs
Concolic testing is a test generation technique which works effectively by
integrating random testing generation and symbolic execution. Existing concolic
testing engines focus on numeric programs. Heap-manipulating programs make
extensive use of complex heap objects like trees and lists. Testing such
programs is challenging due to multiple reasons. Firstly, test inputs for such
program are required to satisfy non-trivial constraints which must be specified
precisely. Secondly, precisely encoding and solving path conditions in such
programs are challenging and often expensive. In this work, we propose the
first concolic testing engine called CSF for heap-manipulating programs based
on separation logic. CSF effectively combines specification-based testing and
concolic execution for test input generation. It is evaluated on a set of
challenging heap-manipulating programs. The results show that CSF generates
valid test inputs with high coverage efficiently. Furthermore, we show that CSF
can be potentially used in combination with precondition inference tools to
reduce the user effort
Compositional Satisfiability Solving in Separation Logic
We introduce a novel decision procedure to the satisfiability problem in array separation logic combined with general inductively defined predicates and arithmetic. Our proposal differentiates itself from existing works by solving satisfiability through compositional reasoning. First, following Fermat’s method of infinite descent, it infers for every inductive definition a “base” that precisely characterises the satisfiability. It then utilises the base to derive such a base for any formula where these inductive predicates reside in. Especially, we identify an expressive decidable fragment for the compositionality. We have implemented the proposal in a tool and evaluated it over challenging problems. The experimental results show that the compositional satisfiability solving is efficient and our tool is effective and efficient when compared with existing solvers