Abstract
We present a method that would integrate both: compositional dynamic symbolic execution and search-based test data generation methods to achieve better code coverage for software that largely depends on floating point computations. We have implemented our method as an extension of a well-know symbolic execution engine – PEX. Our extension implements search-based testing as an optimization technique using AVM method. We present coverage comparison for several benchmark functions. Ill. 1, bibl. 17, tabl. 2 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.122.6.1814
Highlights
Compositional dynamic symbolic execution [1, 2] is a well-known technique for generating unit test data that would achieve high branch coverage for software with no floating point data types
In this paper we present a method that would integrate both: compositional dynamic symbolic execution [3] and searchbased test data generation methods to achieve better code coverage for embedded software that largely depends on floating point computations
If we analyze functions in this way we will see that the number of execution paths considered by compositional dynamic symbolic execution will be at most nb and is linear in nb
Summary
Compositional dynamic symbolic execution [1, 2] is a well-known technique for generating unit test data that would achieve high branch coverage for software with no floating point data types. If we analyze functions in this way we will see that the number of execution paths considered by compositional dynamic symbolic execution will be at most nb (where n is the number of functions f in program P, b is the search depth bound) and is linear in nb This method would end performing random search for floating point constraints that are present in program. Entire path constraint (PC) can be formulated as a disjunction of individual branch statement constraints in the following way PC(x ) pci , i 0..N. Vector x is what we are trying to find: concrete input values for method under test (MUT). Function N denotes a denormalisation function, which normalizes its input into interval [0..1]
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