The computer-based bootstrap method as a tool to select a relevant surface roughness parameter
The aim of this paper is to present how to make the most of the recent and powerful statistical computer-based bootstrap method (CBBM) in roughness studies. This work shows that this statistical method can help to determine quantitatively, and without preconception, the most relevant roughness parameter that characterises the surface morphology of a manufactured product as far as a correlation with a particular function, property or application is concerned. The efficiency of this statistical method is illustrated in this paper describing the relationships between the brightness level and the surface roughness of cold-rolled low carbon steel strips; the relevance of 100 or so roughness parameters was studied via a computer software we have been upgrading for a few years.
Determining whether a new software development technique is useful and usable is a challenging taste. Various flavors of empirical study may be used to help with this task, including surveys, case studies, and experiments. Little guidance is available within the software engineering community to help choose among these alternatives when assessing a new and evolving software development technique within some cost bounds. We faced this challenge when assessing a new programming technique called aspect-oriented programming. To assess the technique, we chose to apply both a case study approach and a series of four experiments because we wanted to understand and characterize the kinds of information that each approach might provide. We describe and critique the evaluation methods we employed, and discuss the lessons we have learned. These lessons are applicable to other researchers attempting to assess new programming techniques that are in an early stage of development.