CS599: PPMP + PSP + Project
Personal Project Management Process
Project Personal Software Process
Project using PPMP/PSP
PPMP Extra Credit Exercise 7S
© 1999 A. Winsor Brown BES/MSEE
599s99PPMP-SS7Sv1.doc–1
v1.0 - 08/09/99
CS599: PPMP + PSP + Project – PPMP Extra Credit Exercise 7S
Assignment & Exercise Details
Process Exercises
Assignment Kit #7
Exercise Evaluation and Grading Process
• Specific Items to be Checked per form or Process —
Defect Recording Log
• PSSP1.1 Exercise Reports – Exit Criteria
© 1999 A. Winsor Brown BES/MSEE
599s99PPMP-SS7Sv1.doc–2
v1.0 - 08/09/99
CS599: PPMP + PSP + Project – PPMP Extra Credit Exercise 7S
Assignment & Exercise Details
Assignment Kit #7S
Assignment Kit 7 Contents
PSSP1.1 Process Scripts
C41 PSSP1.1 Script
C42 PSSP1.1 Planning Script
C43 PSSP1.1 Development Script
C44 PSSP1.1 Postmortem Script
C36 PROBE Estimating Script
Forms, Templates, and Standards
C45 PSSP1.1 Project Plan
Summary
C47 Task Planning Template
C49 Schedule Planning Template
C37 Test Report Template
C39 Size Estimating Template
C27 Process Improvement Proposal
C29 Coding Standard
C16 Time Recording Log
C18 Defect Recording Log
C20 Defect Type Standard
© 1999 A. Winsor Brown BES/MSEE
Instructions Order to submit assignment
PSSP1.1 Project Plan Summary
n/a
Test Report
n/a
PIP form, including lessons learned
n/a
Size Estimating Template
n/a
Task Planning Template
n/a
Schedule Planning Template
Time Recording Log
C46
Defect Recording Log
C48
C50
C38
C40
C28
n/a
C17
C19
n/a
Source program listing
Other requested materials
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CS599: PPMP + PSP + Project – PPMP Extra Credit Exercise 7S
7S. Relative Error Linear Regression
Enhance the linear regression prediction interval
spreadsheet 3S, which includes selective masking of
outliers, to use relative error instead of absolute error.
1. Spreadsheet 7S Prerequisites and References:
DSE Appendix A8 and spreadsheet 3S
2. Spreadsheet 7S Requirements:
Write a spreadsheet to calculate an LOC estimate
and the 90 percent and 70 percent prediction
intervals for this estimate using relative error, not
absolute error. .… Also display the relative errror +/90% and +/-70% curves around the regression line; …
© 1999 A. Winsor Brown BES/MSEE
599s99PPMP-SS7Sv1.doc–4
v1.0 - 08/09/99
CS599: PPMP + PSP + Project – PPMP Extra Credit Exercise 7S
7S. Relative Error Linear Regression (cont.)
2. Spreadsheet 7S Requirements in detail
Write a spreadsheet to calculate an LOC estimate and
the 90 percent and 70 percent prediction intervals for
this estimate using relative error, not absolute error.
The formulae for calculating the linear regression and
prediction interval using absolute error are given in
Appendix A7 & A8. For relative error, just re-express
the dependent variable to produce a new set of
dependent variable values, percent error, which you
then plug into the formula. Use TREND or LINEST to
calculate the regression values. Use the TINV built-in
function to calculate the value of the t distribution.
You may enhance spreadsheet 3S to develop this
spreadsheet.
© 1999 A. Winsor Brown BES/MSEE
599s99PPMP-SS7Sv1.doc–5
v1.0 - 08/09/99
CS599: PPMP + PSP + Project – PPMP Extra Credit Exercise 7S
7S. Relative Error Linear Regression (cont.)
2. Spreadsheet 7S Requirements in detail (cont.)
Display the relative errror +/-90% and +/-70% curves
around the 7S regression line; also include a light
vertical line at the "estimated" object LOC and light
horizontal lines at the project value and both the sets
of UPI and LPI values.
© 1999 A. Winsor Brown BES/MSEE
599s99PPMP-SS7Sv1.doc–6
v1.0 - 08/09/99
CS599: PPMP + PSP + Project – PPMP Extra Credit Exercise 7S
7S. Relative Error Linear Regression (cont.)
3.
Spreadsheet 7S Testing
Thoroughly test the spreadsheet. As one test, use the data for
estimated object LOC and actual new and change LOC in table D8
(p 756). Also assume an estimated object LOC value of 386.
Under these conditions, the estimated LOC values and the
parameter values obtained should be
Linear Regression Prediction Interval
ß0(-70%) = ~0; ß1(-70%) = ~0.8277;
ß0 = 0
ß0(+70%) = 0; ß1(+70%) = ~0.4594
ß1 = 0.5908664 ß0(-90%) = 0; ß1(-90%) = ~1.129;
ß0(-90%) = ~0; ß1(-90%) = ~0.4000
t(70%) = 1.108
σ = 187.21923
Projection = 653 t(90%) = 1.860
Range(386,70%) = ~186.946; UPI = ~840.224, LPI = ~466.331
Range(386,90%) = ~311.520; UPI= ~964.799, LPI = ~341.757
This example is worked out in TBD.
© 1999 A. Winsor Brown BES/MSEE
599s99PPMP-SS7Sv1.doc–7
v1.0 - 08/09/99
CS599: PPMP + PSP + Project – PPMP Extra Credit Exercise 7S
Assignment 7 Details
PSSP1.1 Exercise Reports – Exit Criteria
0. The following five items (per paragraphs one and three of C4.5)
• Complete process data
• Accurate and self-consistent data
• Process Report in proper order and format
• Historical data used planning
• Neat and legible; need not be typed
• NO cover sheets, binders, or written reports other than those requested
1. PSSP1.1 Project Plan Summary (per Tables C45 & C46, pg., 690-692).
2. Test Report (per Tables C37..C38, pages 681..682).
3. PIP forms, including lessons learned (per Tables C27&C28, pgs. 668-9)
4. Size Estimate (per Tables C39..C40, pgs. 683..685).
5. Task Plan (per Tables C47&C48, pages 693..694).
6. Schedule (per Tables C49&C50, pages 695..696).
7. Time Recording Log (per Table C17, page 658).
8. Defect Recording Log (per Tables C19 and C20, pages 660-661).
9. Source Program Listing (per personal coding standard - Exercise R3)
10. Other Requested Material
• Test Results - Program 7S (per Table D11, pg. 758)
• Stu1.XLS with data to date, if at all possible
(on diskette, IBM format; all data per instructions through exercise 7S)
© 1999 A. Winsor Brown BES/MSEE
599s99PPMP-SS7Sv1.doc–8
v1.0 - 08/09/99