  The analysis of execution paths (also known as software traces) collected
from a given software product can help in a number of areas including software
testing, software maintenance and program comprehension. The lack of a scalable
matching algorithm operating on detailed execution paths motivates the search
for an alternative solution.
  This paper proposes the use of word entropies for the classification of
software traces. Using a well-studied defective software as an example, we
investigate the application of both Shannon and extended entropies
(Landsberg-Vedral, R\'{e}nyi and Tsallis) to the classification of traces
related to various software defects. Our study shows that using entropy
measures for comparisons gives an efficient and scalable method for comparing
traces. The three extended entropies, with parameters chosen to emphasize rare
events, all perform similarly and are superior to the Shannon entropy.
