IMPROVING MUTANT SELECTION FOR GUI USING SIMILARITY RELATION AND CONDITIONAL ENTROPY

Authors

  • Nurfadhilah Binti Sapingi
  • Noraini Binti Ibrahim
  • Mustafa Mat Deris

Abstract

Mutation technique is known as one of the most powerful techniques to detect fault
capabilities. Mutation generates the fault version called mutants. Mutation makes small
changes in the code. The output is analysed if it is different from the original program. Then,
the mutant can be killed, or otherwise the mutant said as live. This mutation technique will be
used on Graphical User Interface (GUI), which plays an important role in testing the
interaction between user and software. Using this mutation technique, it may detect more
faults on Graphical User Interface (GUI) during testing to produce a good test suite. The
problem with the mutation technique is that it is expensive, thus new refinement technique,
such as mutant selection, is proposed. The lack of mutant selection decreases the
effectiveness and efficiency of testing due to a random selection of a small subset of mutants
and reducing some operator mutants from the test set for execution. Regardless of this,
reducing the number of mutants for execution is still important. Thus, this paper proposed
new refinement techniques for improving mutant selection in terms of effectiveness and
efficiency of testing known as Similarity Relation and Conditional Entropy. Similarity
Relation classifies the same mutants in the same class to avoid redundancy, while Conditional
Entropy selects mutant operator based on the classification of mutants. The results show that
Similarity Relation can reduce 85% of mutants, while Conditional Entropy reduces 80% of
mutant operator with 100% defect fault capabilities. Similarity Relation and Conditional
Entropy can improve mutant selection to select the optimum mutants and mutants’ operator
for testing without less effectiveness and efficiency.

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Published

2020-12-26

How to Cite

Nurfadhilah Binti Sapingi, Noraini Binti Ibrahim, & Mustafa Mat Deris. (2020). IMPROVING MUTANT SELECTION FOR GUI USING SIMILARITY RELATION AND CONDITIONAL ENTROPY. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(10), 394-410. Retrieved from https://archives.palarch.nl/index.php/jae/article/view/4351