A software product line is a family of products that share common features to meet the needs of a market area. Systematic processes have been developed to dramatically reduce the cost of a product line. Such product-line engineering processes have proven practical and effective in industrial use, but are not widely understood. The Family-Oriented Abstraction, Specification and Translation (FAST) process has been used successfully at Lucent Technologies in over 25 domains, providing productivity improvements of as much as four to one. In this paper, we show how to use FAST to document precisely the key abstractions in a domain, exploit design patterns in a generic product-line architecture, generate documentation and Java code, and automate testing to reduce costs. The paper is based on a detailed case study covering all aspects from domain analysis through testing.

With the advent of object‐oriented languages and the portability of Java, the development and use of class libraries has become widespread. Effective class reuse depends on class reliability which in turn depends on thorough testing. This paper describes a class testing approach based on modeling each test case with a tuple and then generating large numbers of tuples to thoroughly cover an input space with many interesting combinations of values. The testing approach is supported by the Roast framework for the testing of Java classes. Roast provides automated tuple generation based on boundary values, unit operations that support driver standardization, and test case templates used for code generation. Roast produces thorough, compact test drivers with low development and maintenance cost. The framework and tool support are illustrated on a number of non‐trivial classes, including a graphical user interface policy manager. Quantitative results are presented to substantiate the practicality and effectiveness of the approach. Copyright © 2002 John Wiley & Sons, Ltd.

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Tree crown recognition with high spatial resolution remotely sensed imagery provides useful information relating the number and distribution of trees in a forest. A common technique used to identify tree locations uses a local maximum (LM) filter with a static-sized (user-specified) moving window. LM techniques operate on the assumption that high local radiance values represent the centroid of a tree crown. The static nature of this technique is inconsistent with both natural canopy structure and digital images. A variable window size (VWS) LM technique operates under the assumption that there are multiple tree shapes and sizes within an image and that the LM filter should be adjusted to an appropriate size, based on the spatial structure found within the imagery.To compare the utility of the VWS LM technique versus that of static LM techniques, tree location accuracy was evaluated for static 3x3, 5x5, 7x7 filters, VWS, and VMS plus a false. positive filter based on the Getis statistic. The study site incorporates two stands of Douglas fir (Pseudostuga menziesii); a 40 year old planted site and a >I50 year naturally regenerating site. The imagery used was MEIS-11 with 1 m ground resolution acquired in 1993 as part of the SEIDAM project [I].The plantation site has a uniform distribution of tree size and spacing, while the naturally regenerating stand is composed of irregularly sized and spaced trees. The spatially sensitive VWS technique out-perfoms the static technique when both plantation and naturally regenerating stands are examined. False-positive filters are introduced to screen for local radiance maxima which may not be representative of tree centroids.