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      <dc:title>Estimating Software Testing Complexity</dc:title>
      <dc:creator>Ferrer-Urbano, Francisco Javier</dc:creator>
      <dc:creator>Chicano-García, José-Francisco</dc:creator>
      <dc:creator>Alba-Torres, Enrique</dc:creator>
      <dc:subject>Soporte lógico</dc:subject>
      <dc:subject>Ingeniería del software</dc:subject>
      <dc:subject>Computación evolutiva</dc:subject>
      <dc:description>Context: Complexity measures provide us some information about software artifacts. A measure of the&#xd;
difficulty of testing a piece of code could be very useful to take control about the test phase.&#xd;
Objective: The aim in this paper is the definition of a new measure of the difficulty for a computer to gen erate test cases, we call it Branch Coverage Expectation (BCE). We also analyze the most common com plexity measures and the most important features of a program. With this analysis we are trying to&#xd;
discover whether there exists a relationship between them and the code coverage of an automatically&#xd;
generated test suite.&#xd;
Method: The definition of this measure is based on a Markov model of the program. This model is used&#xd;
not only to compute the BCE, but also to provide an estimation of the number of test cases needed to&#xd;
reach a given coverage level in the program. In order to check our proposal, we perform a theoretical val idation and we carry out an empirical validation study using 2600 test programs.&#xd;
Results: The results show that the previously existing measures are not so useful to estimate the difficulty&#xd;
of testing a program, because they are not highly correlated with the code coverage. Our proposed mea sure is much more correlated with the code coverage than the existing complexity measures.&#xd;
Conclusion: The high correlation of our measure with the code coverage suggests that the BCE measure is&#xd;
a very promising way of measuring the difficulty to automatically test a program. Our proposed measure&#xd;
is useful for predicting the behavior of an automatic test case generator.</dc:description>
      <dc:date>2023-12-13T10:50:18Z</dc:date>
      <dc:date>2023-12-13T10:50:18Z</dc:date>
      <dc:date>2023</dc:date>
      <dc:date>2013-07-29</dc:date>
      <dc:type>journal article</dc:type>
      <dc:identifier>Ferrer, J., Chicano, F., &amp; Alba, E. (2013). Estimating software testing complexity. Information and Software Technology, 55(12), 2125–2139.</dc:identifier>
      <dc:identifier>https://hdl.handle.net/10630/28274</dc:identifier>
      <dc:identifier>10.1016/j.infsof.2013.07.007</dc:identifier>
      <dc:language>eng</dc:language>
      <dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
      <dc:rights>open access</dc:rights>
      <dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</dc:rights>
      <dc:publisher>Science Direct</dc:publisher>
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