Abstract— Sampling is a general concept that has many applications in various domains. The idea of representing sampling as a pattern is to guarantee a reusable core. The stable sampling analysis pattern is introduced and defined as a solution for providing the core knowledge of the sampling problem itself. In order to achieve this goal, the Sampling pattern is built based on the software stability concepts approach introduced in [1]. The Software Stability Concepts provide the Sampling pattern a stable and reusable core [1]. This core is represented in terms of Enduring Business Themes and Business Objects artifacts [1]. Due to their reusable and stable nature, they grant the ability of this pattern to be used in other applications which share the same knowledge. This paper provides detailed documentation of the proposed -stable sampling analysis pattern. Index Terms—Software stability, Software patterns. I. INTRODUCTION When referring to the term sampling, we are entering into a multi-application area of study. This term as an action is applied in almost every activity in our daily lives. Sampling utilization ranges from small and simple activities, such as sampling a small portion of a cake at the supermarket to taste the cake, to the most complex ones, such as the action of determining the percentage of contamination occurrence in hard-drives manufactured in the month of July 2003 at the Seagate Company. Due to the impossibility of studying large volumes of the population, researchers rely constantly on sampling to single out small portions of a particular population to perform an experiment or evaluation study. For instance, in performing an investigation of a customer's satisfaction against a store's service. Since it would be impossible to ask every customer for their opinion, the store would have to make used of sampling to randomly choose customers for the investigation. Similarly, the research of the use of certain Educational Software might involve the sampling of picking 20 students out of a total of 500 students. All given examples provide strong evidence of the utilization of sampling across different domains; explicitly exhibiting the true reusable nature of the Sampling Analysis Pattern across many domains. Sampling as a concept is a very general term that can span multiple applications. It is defined as a technique used to capture continuous phenomena from a universe, providing an idea or estimation of that particular universe [4]. There are different kinds of sampling techniques that are employed these days. For Simplicity purposes, only a few of them will be mentioned in this paper, such as Random Samp ling, Cluster Sampling, Stratified Sampling, and Quota Sampling. Random sampling is a sampling technique where a group of subjects are selected for a study to represent a larger group of the population. Each subject is randomly chosen. Each of these members or subjects that are part of a particular population has an equal chance of being included in the sample. Every possible sample of a given size has the same chance of selection [5]. Cluster Sampling is a sampling technique where the entire population is partitioned into groups. Then, a random sample of these clusters is selected. All observations in the selected clusters are included in the sample. Cluster Sampling is usually used when the researcher cannot get a complete list of the members of a particular population they wish to study, but can get a complete list of clusters of the population [5]. Stratified Sampling is a technique that takes samples from each stratum or sub-group of a population [5]. This is driven by the occurrence of factors that partition a population into sub-populations or sub-strata. Quota Sampling is a technique that usually applies in market research and opinion polling [5]. A person in charge of sampling is given a quota of subjects of a specified type to attempt to record a certain phenomena and perform a specific action (e.g. interviewing). These Sampling techniques are employed usually to cope with a particular problem domain. Concretely speaking, they are structured in such a manner that is solely focused on a solution for a specific problem. In the case that they are to be implemented in a distinct domain, in which elements are different in characteristics and behavior, it is possible to obtain an inaccurate result; and hence, a failure of the sampling action. Therefore, being able to address all the different varieties of problem domain solutions, where the Sampling is THE SAMPLING ANALYSIS PATTERN H.A. Sánchez, Binbin Lai, and M.E. Fayad Computer Engineering Dept., College of Engineering, San Jose State University One Washington Square, San Jose, CA 95192-0180 huascar_sanchez@yahoo.com , binbinlai@yahoo.com , and m.fayad@sjsu.edu