  The increasing number of aspect mining techniques proposed in literature
calls for a methodological way of comparing and combining them in order to
assess, and improve on, their quality. This paper addresses this situation by
proposing a common framework based on crosscutting concern sorts which allows
for consistent assessment, comparison and combination of aspect mining
techniques. The framework identifies a set of requirements that ensure
homogeneity in formulating the mining goals, presenting the results and
assessing their quality.
  We demonstrate feasibility of the approach by retrofitting an existing aspect
mining technique to the framework, and by using it to design and implement two
new mining techniques. We apply the three techniques to a known aspect mining
benchmark and show how they can be consistently assessed and combined to
increase the quality of the results. The techniques and combinations are
implemented in FINT, our publicly available free aspect mining tool.
