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Micro patterns are similar to design patterns, except that micro patterns stand at a lower, closer to the implementation, level of abstraction. Micro patterns are also unique in that they are mechanically recognizable, since each such pattern can be expressed as a formal condition on the structure of a class. This paper presents a catalog of 27 micro-patterns defined on JAVA classes and interfaces. The catalog captures a wide spectrum of common programming practices, including a particular and (intentionally restricted) use of inheritance, immutability, data management and wrapping, restricted creation, and emulation of procedural-, modular-, and even functional-programming paradigms with object oriented constructs. Together, the patterns present a set of prototypes after which a large portion of all JAVA classes and interfaces are modeled. We provide empirical indication that this portion is as high as 75% .A statistical analysis of occurrences of micro patterns in a large software corpus, spanning some 70,000 JAVA classes drawn from a rich set of application domains, shows, with high confidence level that the use of these patterns is not random. These results indicate consciousness and discernible design decisions, which are sustained in the software evolution. With high confidence level, we can also show that the use of these patterns is tied to the specification, or the purpose, that the software realizes.The traceability, abundance and the statistical significance of micro pattern occurrence raise the hope of using the classification of software into these patterns for a more founded appreciation of its design and code quality.

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Abstract-We propose an efficient and deterministic algorithm for computing the one-dimensional dilation and erosion (max and min) sliding window filters. For a p-element sliding window, our algorithm computes the 1D filter using 1:5 þ oð1Þ comparisons per sample point. Our algorithm constitutes a deterministic improvement over the best previously known such algorithm, independently developed by van Herk [25] and by Gil and Werman [12] (the HGW algorithm). Also, the results presented in this paper constitute an improvement over the Gevorkian et al. [9] (GAA) variant of the HGW algorithm. The improvement over the GAA variant is also in the computation model. The GAA algorithm makes the assumption that the input is independently and identically distributed (the i.i.d. assumption), whereas our main result is deterministic. We also deal with the problem of computing the dilation and erosion filters simultaneously, as required, e.g., for computing the unbiased morphological edge. In the case of i.i.d. inputs, we show that this simultaneous computation can be done more efficiently then separately computing each. We then turn to the opening filter, defined as the application of the min filter to the max filter and give an efficient algorithm for its computation. Specifically, this algorithm is only slightly slower than the computation of just the max filter. The improved algorithms are readily generalized to two dimensions (for a rectangular window), as well as to any higher finite dimension (for a hyperbox window), with the number of comparisons per window remaining constant. For the sake of concreteness, we also make a few comments on implementation considerations in a contemporary programming language.Index Terms-Mathematical morphology, running maximum filter, min-max filter, computational efficiency.