Appendix A: Data Tables Used for Use Cases

TABLE A.1

Complexity Level Evaluation for CP1

0-4 NEM

5-8 NEM

9-12 NEM

>13 NEM

0-1 NSR

Low

Low

Average

High

2-3 NSR

Low

Average

High

High

4-5 NSR

Average

High

High

Very High

>5 NSR

High

High

Very High

Very High

TABLE A.2

Complexity Level Evaluation for CP2

0-2 NSR

0-5 NOA

6-9 NOA

10-14 NOA

>15 NOA

0-4 NEM

Low

Low

Average

High

5-8 NEM

Low

Average

High

High

9-12 NEM

Average

High

High

Very High

>13 NEM

High

High

Very High

Very High

(а)

3-4 NSR

0-4 NOA

5-8 NOA

9-13 NOA

>14 NOA

0-3 NEM

Low

Low

Average

High

4-7 NEM

Low

Average

High

High

8-11 NEM

Average

High

High

Very High

>12 NEM

High

High

Very High

Very High

(b)

>5 NSR

0-3 NOA

4-7 NOA

8-12 NOA

>13 NOA

0-2 NEM

Low

Low

Average

High

3-6 NEM

Low

Average

High

High

7-10 NEM

Average

High

High

Very High

>11 NEM

High

High

Very High

Very High

(c)

System

Component Type

Description

Complexity

Low

Average

High

Very High

PDT

Problem Domain Type

3

6

10

15

HIT

Human Interface Type

4

7

12

19

DMT

Data Management Type

5

8

13

20

TMT

Task Management Type

4

6

9

13

TABLE A.3

Degree of Influences of Twenty Four General System Characteristics

ID

System characteristics Dl

ID

System Characteristics

Dl

Cl

Data Communication .....

C13

Multiple Sites

C2

Distributed Functions .....

C14

Facilitation of Change

C3

Performance .....

C15

User Adaptively

C4

Heavily Used configuration .....

C16

Rapid Prototyping

C5

Transaction Rate .....

C17

Multiuser Interactivity

C6

Online Data Entry .....

C18

Multiple Interface

C7

End User Efficiency .....

C19

Management Efficiency

C8

Online Update .....

C20

Developer’s Professional Competence

C9

Complex Processing .....

C2I

Security

CIO

Reusability .....

C22

Reliability

Cll

Installation Ease .....

C23

Maintainability

C12

Operational Ease .....

C24

Portability

TDI

Total Degree of Influence (TDI)

Appendix B: Review Questions

  • 1. Among all the proposed metrics list any five metrics which, in your opinion, predict component development effort most precisely.
  • 2. Which of these proposed metrics significantly adds to the predictive ability ofCOCOMO?
  • 3. Can the proposed metrics be used as a basis for further remuneration options for COCOMO?
  • 4. Describe the top model/technique for software cost estimation.
  • 5. Case study a safety critical system and find out the differences in estimating software costs compared to normal systems.
  • 6. How do we evaluate the effectiveness of software effort estimation models?
  • 7. Explain in detail the importance of “Project Methodology” as a parameter for software cost estimation.
  • 8. Define scalability as far as the software cost estimation is concerned.
  • 9. What is the importance of the use case points method in software effort estimation?
  • 10. What are the most used approaches for evaluation of software effort estimation models?
  • 11. Is it possible to do a software cost estimation before requirement collection?
  • 12. What method can be used to measure the accuracy of prediction for an effort estimation technique in software development?
  • 13. Explain the research gaps in the deep learning, machine learning ... soft computing techniques in the analysis of estimating efforts.
  • 14. List the available simulation tools to work on soft computing techniques.
  • 15. What is the state of the knowledge surrounding COTS technology implementation?
  • 16. Describe the forerunners of both obstacles and designers of COTS technology implementation.
  • 17. What are the issues for successful adoption and performance of COTS technology?
 
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