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Object-oriented cohesion as a surrogate of software comprehension: An empirical study
The concept of software cohesion in both the procedural and object-oriented paradigm is well known and documented. What is not so well known or documented is the perception of what empirically constitutes a cohesive 'unit' by software engineers. In this paper, we describe an empirical investigation using object-oriented (OO) classes as a basis. Twenty-four subjects (drawn from IT experienced and IT inexperienced groups) were asked to rate ten classes sampled from two industrial systems in terms of their overall cohesiveness; a class environment was used to carry out the study. Four key results were observed. Firstly, class size (when expressed in terms of number of methods) did not tend to influence the perception of cohesion by any subjects. Secondly, well-commented classes were rated most highly amongst both IT experienced and inexperienced subjects. Thirdly, the empirical study suggests that cohesion comprises a combination of various class factors including low coupling, small numbers of attributes and well-commented methods, rather than any single, individual class feature per se. Finally, the research supports the view that cohesion is a subjective concept reflecting a cognitive combination of class features; as such it is a surrogate for class comprehension
Symbolic Sequences and Tsallis Entropy
We address this work to investigate symbolic sequences with long-range
correlations by using computational simulation. We analyze sequences with two,
three and four symbols that could be repeated times, with the probability
distribution . For these sequences, we verified that
the usual entropy increases more slowly when the symbols are correlated and the
Tsallis entropy exhibits, for a suitable choice of , a linear behavior. We
also study the chain as a random walk-like process and observe a nonusual
diffusive behavior depending on the values of the parameter .Comment: Published in the Brazilian Journal of Physic
Potts model on complex networks
We consider the general p-state Potts model on random networks with a given
degree distribution (random Bethe lattices). We find the effect of the
suppression of a first order phase transition in this model when the degree
distribution of the network is fat-tailed, that is, in more precise terms, when
the second moment of the distribution diverges. In this situation the
transition is continuous and of infinite order, and size effect is anomalously
strong. In particular, in the case of , we arrive at the exact solution,
which coincides with the known solution of the percolation problem on these
networks.Comment: 6 pages, 1 figur
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