abstract = "The automatic detection of refactoring recommendations
has been tackled in prior optimization studies
involving bad code smells, semantic coherence and
importance of classes; however, such studies informally
addressed formalisms to standardize and replicate
refactoring models. We propose to assess the
refactoring detection by means of performance
convergence and time complexity. Since the reported
approaches are difficult to reproduce, we employ an
Artificial Refactoring Generation (ARGen) as a formal
and naive computational solution for the Refactoring
Detection Problem. ARGen is able to detect massive
refactoring sets in feasible areas of the search space.
We used a refactoring formalization to adapt search
techniques (Hill Climbing, Simulated Annealing and
Hybrid Adaptive Evolutionary Algorithm) that assess the
performance and complexity on three open software
systems. Combinatorial techniques are limited in
solving the Refactoring Detection Problem due to the
relevance of developers' criteria (human factor) when
designing reconstructions. Without performance
convergence and time complexity analysis, a software
empirical analysis that uses search techniques is
incomplete.",
notes = "HaEa p1610 'Commons Codec v.1.10 with 123 number of
classes (CCODEC), Acra v.4.6.0 with 59 classes (ACRA)
and JFreeChart v.1.0.9. with 558
classes.'