requirements model testing
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Requirements ModelTesting
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Requirements Gathering
We can understand the process ofrequirements gathering based on differentusers-
Top Level Stakeholders Middle Level Managers Users or Operators Programmers / IT people
Implementers or Maintainers
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Top level Managers Middle Level Managers
Users/Customers IT PeopleProgrammers
ImplementorsMaintainence
Requirements
Stakeholders
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Hierarchical Concerns
Top Level- Mission , Values Middle Level- Business Activity Users- Interactivity
Programmers- Organization ProblemSolutions
Implementers & Maintainers- Relevance,Profitability , Growth
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Testing parameters based on
hierarchy Following parameters can be used to test the
requirements model- M(Mission), V(Vision)
B(Business Activity) I(User Interactivity) S(Problem Solutions) R(Relevance), P(Profitibality),G(Growth)
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Requirement Model Testing
Test Cases can be derived from any modelbased on the above parameters.
For eaxmple waterfall model can be tested by
starting with topmost requirementsparemeters.
Evolutionary model can be tested by testingeach of these parameters at each stage
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Use Cases
Use Cases use the following mehod for testingrequirements-
Identify users or actors operating the system.
Identify the scenarios with which the actors mayinteract with the system. These are called the usecases.
For Example a scenario for a banking system canbe-
-> The customer enters the ATM card and enters thepassword. After entering the password the customersenters the amount and presses yes.
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Test Cases
Test cases are the conditions which test thesoftware in two ways-
Black Box test cases try to test the functionality of
the software. For example If the customer enters the password wrong.How willATM respond?
White Box test cases try to test the internal structureof the software.
If the entered_amount < 10 && entered_amount>10000 ?
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Use Cases for testing requirementmodels
Based on the specified method. All thestakeholders of the requirements phase canact as actors.
Top Managers Middle Managers Users Programmers
Maintainers and Implementors
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Testing the requirements models
Each of these actors can test the modelsusing the use cases based on the followingparameters.
For example top managers can test themodels for mission and vision statement.
Middle managers can test the model forbusiness activity considerations.
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Test Cases
Test cases can be designed for the modelsby interpreting these parameters for black box andwhite box testing.
For Example a test case can be based on the missionstatement-If loan_amount> 10000 and loan_amount
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Evolutionary Model
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Use cases for evolutionary model
The evolutionary model can be tested if we test allthe components with the following scenarios-
Do the components of the requirements model fulfillthe mission, vision?
Do the components of the requirements model fulfillthe business acivitiy considerations?
Do the components of the requirements modelsatisfy the user interface?
Do the components provide solutions to all theproblems? DO the components show relevance, profitability
and growth?
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Test cases for evolutionary model
White Box test cases- Do the requirement model satisfy all the
parameters internally?
Black Box test cases- Do the requirement model saisfy all the
parameters functionaly?
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Mathematics of Testing
Let us choose the variables representing each ofthese parameters-
C1(Mission), C2(Vision)
M1(Business Activities) U1(Interactivity) S1(Solutions) I1(Relevance),I2(Profitability),I3(Growth) Deriving the relationship between these variables
can help to access accurately the success ofgathered requirements.
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The above paper uses models or graphs for representing the structure ofthe database.
The main aim of this work is to show the connection between querylanguages applied to semistructured data and modal logics, and tocharacterize families of queries that admit efficient solutions to the data
retrieval problem. The novel ideas are to associate a modallogic formula to a graphical
query (or constraint), and to interpret database instance graphs asKripke Transition Systems (KTS).
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This approach uses creating planning models for
describing agro-products. Further this model uses the decision variables for
the purpose of classification. These decision variables serve as parameters for
creating and verifying the planning models. All the decision variables are based on the work ofvarious people in the field and are used in theplanning models.
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This paper is concerned with the formulation and the solution of aprobabilistic model for determining the optimal location of facilities incongested emergency systems.
The inherent uncertainty which characterizes the decision process ishandled by a new stochastic programming paradigm which embeds theprobabilistic constraints within the traditional two-stage framework.
The resulting model drops simplifying assumptions on serversindependence allowing at the same time to handle the spatial
dependence of demand calls. An exact solution method and different tailored heuristics are presented
to efficiently solve the problem. Computational experience is reportedwith application to various networks.
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The model uses the heuristic approach for developing a branch andbound model for solving the problem.
These models are tested by estimating the time required to obtain theresults by creating tests based on number of scenarios.
These scenarios are then tested separately by estimating their running
times.
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Network protection against natural and human-caused hazards has
become a topical research theme in engineering and social sciences.
This paper focuses on the problem of allocating limited retrofit resourcesover multiple highway bridges to improve the resilience and robustnessof the entire transportation system in question.
The main modeling challenges in network retrofit problems are tocapture the interdependencies among individual transportation facilitiesand to cope with the extremely high uncertainty in the decisionenvironment.
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The model is tested by taking real scenarios and testing the
robustness of the network.
The network should handle all the cases without any malfunctioningand be able to provide secure results.
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Requirements Testing Simulation
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