@incollection {2011-CompTDG,
  author    = {Elvira Albert and Miguel G\'{o}mez-Zamalloa and
               Jos\'e Miguel Rojas and Germ\'{a}n Puebla},
  title	    = {Compositional CLP-Based Test Data Generation for
               Imperative Languages},
  booktitle = LOPSTR,
  series    = lncs,
  publisher = {Springer},
  isbn	    = {},
  pages	    = {99--116},
  volume    = {6564},
  url	    = {http://dx.doi.org/10.1007/978-3-642-20551-4_7},
  pdf       = {http://costa.ls.fi.upm.es/papers/costa/AlbertGRP10.pdf},
  labels    = {conf,lncs,hats,doves,prometidos},
  year	    = {2011},
  abstract  = {Glass-box test data generation (TDG) is the process of
automatically generating test input data for a program by considering
its internal structure. This is generally accomplished by performing
symbolic execution of the program where the contents of variables are
expressions rather than concrete values. The main idea in CLP-based
TDG is to translate imperative programs into equivalent CLP ones and
then rely on the standard evaluation mechanism of CLP to symbolically
execute the imperative program. Performing symbolic execution on large
programs becomes quickly expensive due to the large number and the
size of paths that need to be explored. In this paper, we propose
\emph{compositional reasoning} in CLP-based TDG where large programs
can be handled by testing parts (such as components, modules,
libraries, methods, etc.) separately and then by composing the test
cases obtained for these parts to get the required information on the
whole program. Importantly, compositional reasoning also gives us a
practical solution to handle native code, which may be unavailable or
written in a different programming language. Namely, we can model the
behavior of a native method by means of test cases and compositional
reasoning is able to use them.}
}

%% 2010

% JHSYS

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