System-Level Requirements Example
Consider the Paint the Room program, in which you developed what are often called system-level requirements-the basis for all subsequent analysis and design steps. The following steps will take these system-level requirements and refine them into a detailed blueprint for the program.
Up to this point, you have identified the processes the program must perform, but you have not given any consideration to exactly how the processes work together to solve the problem. At this point, you must generate a description of the processing using pseudocode, a natural language description of the processing the application must perform.
The natural place to start is the system-level requirements you identified in the Input-Process-Output (IPO) chart. Determine how the processes work together: Once you have determined the top-level logic, you can then design each of the individual processes. It is this step-wise refinement process that allows you to conceptualize a vague problem into increasing levels of details in order to actually generate a working program. This point is important because the step-wise refinement pattern is used throughout the entire program development-each new piece of information is based on, and is a refinement of, the information uncovered in the previous step.
For this week's CheckPoint, you will refine the IPO table into a complete design, as demonstrated on pp. 33 and 36 of Extended Prelude to Programming: Concepts and Design (2nd ed.). Refer also to the Input and Output Process Example in Appendix B to see how more detailed analysis and design relates to the previously constructed IPO chart.
The following information demonstrates all the items you need to develop for your programming assignments and for the final project.
Analysis
Process:
1. Get user input
2. Find room area
3. Divide room area
4. Multiply gallons
5. Prompt for ounces
6. Display total
price (real: 0-100)
squareFeetPerGal (real: 0-1000)
width (real: 0-100)
length (real: 0-100)
height (real: 0-100)
Input:
Output:
total_cost (real: > 0)
Design
Main Module
Declare price as real
Declare squareFeetPerGal as real
Declare width as real
Declare length as real
Declare height as real
Declare totalArea as real
Declare gallonsNeeded as real
Declare outputPrompt as string
Declare totalCost as real
Get user input
Find room area
Divide room area
Multiply gallons
Display total
End Main Module
Input Data Module
Write, "What is the price per gallon of paint?"
Input price
Write, "How many square feet does each gallon cover?"
Input square_feet_per_gal
Write, "What is the height of the walls?"
Input height
Write, "What is the width of the walls?"
Input width
Write, "What is the length of the walls?"
Input length
End Input Data Module
Find Room Area Module
Declare sideArea as real
Declare frontBackArea as real
Declare ceilingArea as real
sideArea = 2*(length * height)
frontBackArea = 2*(width * height)
ceilingArea = width * length
totalArea = sideArea + frontBackArea + ceilingArea
End Find Room Area Module
2/3 = 2 x 1/3
II. SIMPLEX ALGORITHM A. Primal Simplex Algorithm If the unconstrained solution space is defined in n dimensions (each dimension assumed to be infinite), each inequality constraint in the linear programming formulation divides the solution space into two halves. The convex shape defined in n-dimensional space after m bisections represents the feasible area for the problem, and all points which lie inside this space are feasible solutions to the problem. Figure 1 shows the feasible region for a problem defined in two variables, n = 2, and three constraints, m = 3. Note that in linear programming, there is an implicit non-negativity constraints for the variables. The linearity of the objective function implies that the the optimal solution cannot lie within the interior of the feasible region and must lie at the intersection of at least n constraint boundaries. These intersections are known as corner- point feasible (CPF) solutions. In any linear programming problem with n decision variables, two CPF solutions are said to be adjacent if they share n − 1 common constraint boundaries. When interpreted geometrically, the Simplex algorithm moves from one corner-point feasible solution to a better corner-point-feasible solution along one of the constraint boundaries. There are only a finite number of CPF solutions, although this number is potentially exponential in n, however it is not necessary to visit all of them to determine the optimal solution to the problem. The convex nature of linear programming means that there are no local maxima present in the problem which are not also global maxima. Hence if at some CPF solution, no improvement is made by a move to another adjacent CPF then the algorithm terminates and we can be confident that the optimal solution has been found.
2 x 3 x 72 = 294 is the index form. Index notation refers to higher mathematics and computer programming.
1/5 = 0.2 or 20%
2 x 2 x 3 x a
2(2)2
When you select your checkpoint, simply just click where you want to add it.
Border Wars - 2010 Checkpoint Texas 2-2 was released on: USA: 1 September 2010
1)Multistage graph 2)Travelling salesman problem
Respawn at every checkpoint.
See "Programming the Transmitters" on page 10 in chapter 2 "Keys and Doors" of your "OWNER'S MANUAL".....
See "Programming the Transmitters" on page 10 of chapter 2 "Keys and Doors" in the "OWNER'S MANUAL".....
run past a checkpoint with fifty rings, then, a ring will appear above the checkpoint, jump into the ring, and boom, your in the special stage
have 2 controllers on at the same time near a checkpoint or at your pod
yes. example: every musher in the iditarod race can spend 24 hours only once at a checkpoint. so it would be a good idea when they are tired to stop at a checkpoint for 24 hours since they only get to do it once. they all have limited time at checkpoint. if u r confused, go 2 the iditarod website.
you have to reach the blue checkpoint and click the save button choice
The game is divided in 9 chapters: Prologue Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Final Chapter Fragment