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and nonfunctional requirements should be tested. In some situations, several quality attributes such as con guration, performance, security, stress, reliability, recovery, and usability have to be tested. However, since not all types of systems undergo such thorough testing, the following sections address the three categories of the most frequently conducted system testing: functional, performance and load stress, and security. Functional Testing The goal of system functional testing is to verify the application functional requirements at the highest level of abstraction. System functional tests often correspond directly to the application use cases, and with ADP they are designed as early as use cases are created. Performance and Load Stress Testing The objective of the performance and loadn stress testing is to verify whether the system continues to function correctly under nominal and extreme loads, and whether the response time under those loads is satisfactory. The load is the amount of work performed by a system, and in testing it is expressed as a series of inputs to execute a group of transactions. Performance testing focuses on ne tuning system response time and throughput under its nominal loads. Load stress testing, on the other hand, concentrates on creating extreme conditions that would break the system due to the resource exhaustion. Security Testing Security testing involves designing and executing tests to expose possible application vulnerabilities to intentional and unintentional system security violations. The focus is on those segments of code that are responsible for protecting access to the system and where sensitive data is stored.
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other resources in the company. Because they spend so little time online, their interface to the Net should be as simple and quick to use as possible. Online surveys and email might be the most appropriate way to gain their participation. Profile D is an ongoing, focused population that interacts for enjoyment. The knowledge generated in their conversations may or may not be of direct value to the company, but their relationships do offer the potential of releasing valuable knowledge and experience indirectly available to the company. Communities of practice are cohesive as long as their members work for the organization and have time to participate, but their motivations are powerful for making the best use of whatever tools are available for group communication. As we ll describe in s 7 and 8, communities of practice differ greatly in their needs for technology. Their motivating focus makes them more adaptive than communities that are formed more artificially to accomplish work-related tasks. Initial group tendencies, as we ve pointed out, are not deterministic; they are likely to change and fluctuate. But as a group takes its first steps into the online meeting space, it s worthwhile to address these natural tendencies for collaboration with the intention of making its early experience as nondisruptive as possible. Forcing cultural change by trying to fit square-peg cultures into round-hole interfaces has a low probability of success. But once accustomed to working through an online interface, the square pegs can change their shape and adapt to interfaces that they find more helpful to their reaching goals.
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SYMBIAN PLATFORM FUNDAMENTALS
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Although different individuals vary in terms of their specific characteristics and thus develop different disorders, there exists some logic connecting the personality disorder
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Legitimate peripheral participation: competences that novices start to develop through these activities
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Exhibit 4.5 Organizational Diagnostic Assessment
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exp(h j b) eh e j b = 1 |aRT | exp(j ) (1 |aRT | cos ) j |aRT | sin
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IV. T H E S O C I A L D I M E N S I O N
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Cascade noise gure from Fig. 3.3.
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groups except for children, pregnant women, institutionalized individuals, and the categorically needy in Medicaid HMOs (Scholesberg and Jerath, 1999). It is possible that the potential higher costs of pharmacogenomic drugs will increase cost sharing for all drugs paid for by Medicaid patients, therefore further decreasing drug utilization and access. As noted above, this would increase illness, health care visits, and admissions to hospital and nursing homes. Therefore, Medicaid patients who are already in a system that requires prior authorization, prescription caps, and other limits may experience an increase in differential access to pharmaceuticals compared with the general population.
where h(N) gives the reduction in dN/dt at a particular value of N due to harvesting. The population change and harvesting functions of the generalized form of equation 6.17 can be combined on one graph as they are both functions of N (Figs 6.14 and 6.15). We will now consider several harvesting possibilities with a logistic growth curve. If h(N) is constant and therefore independent of N, harvesting is represented by a horizontal line on the graph. We know that sustainable harvesting can only occur when dN/dt > 0; when f(N) > h(N) in equation 6.17. The area on the graph when this condition is met is shaded in Fig. 6.14 and lies between N = 20 and N = 80. What happens to the population at different population sizes For example, consider a population of size 70. Here f(N) > h(N) and therefore population size increases (dN/dt > 0, equation 6.17).
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This pattern is also known as the State Design pattern. Our intent here is to show how a well-known design pattern can be used to improve the quality of business modeling not to reinvent the pattern wheel, so to speak. Intent The Process Instance State pattern is a Process Support pattern that shows how the state of a process instance can be used to create both well-designed processes and support systems, such as for computer systems. Motivation As with the Process-Process Instance Pattern, this pattern focuses on process instances. A process instance goes through a number of activities and/or subprocesses during its execution. If an activity or a subprocess is executed more than once, it is unclear whether the process instances remain in the same state after the first and second executions. A counter that tracks something for example, number of items produced is an example: Every time the counter increases it number, it enters a new state. The counter is defined in the process, and each counter that actually counts is in a process instance that is affected by the activity increase. This means that a process instance can go through several states during its lifetime. In addition, a process instance or activity can be suspended, or it can be abandoned. This means that a process instance within a product development process, for example, can be in the state product planned, product designed, product in product set, or product on market ; or the instance can be suspended for some time. Furthermore, a process instance can be proposed and accomplished. The risk of not modeling the states of a process is that the process won t work in practice. Consider a sales process in a telemarketing company that specifies that an employee should call a certain customer to try to sell him or her a product. That process might not work if it hasn t specified how to determine whether someone else at the telemarketing company has already called the same customer; the result would be a customer who is annoyed and so definitely won t buy anything. Simply put, the states of the sales process must be defined. If a customer has already been called by someone at the telemarketing company that customer should not be called again the same day, and preferably not for a number of days. As illustrated, the states of the process are often connected to business rules. It is a business rule that no two employees of the same company should call the same customer on the same day. Applicability The Process Instance State pattern is used to model process and activity states in order to understand them before building support systems, such as information systems. Typical problem situations where this pattern can be applied are supervising applications, project management tools, and simulators.
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