- Over 40-60% of IT development projects are outsourced, and this percentage is increasing each year. - 50-70% of companies experience projects that exceed time and budget estimates by over 80% and deliver less than 70% of required functionality when outsourcing projects. - A new model was developed that incorporates efficiency and risk factors to more accurately estimate test effort for outsourced software projects compared to traditional use case point methods. The model was demonstrated on a sample project.

1. Over 40 -60% of the IT development are done through
outsourcing
And day by day this is increasing
1
Offshore Software Development

2. Software Outsourcing
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Country Name Revenue in 2012 Next Target By
Vietnam 2.3 B 6 B 2016
Philippines 1.2 B 2 B 2015
Bangladesh 15 M 2 B 2017

3. 50 -70% of companies are more likely to have a marginal
project or outright failure:
 Taking over 180% of target time to deliver;
 Consuming in excess of 160% of estimated budget;
 Delivering under 70% of the target required functionality.
3
Offshore Software Development

4. Efficiency Factor and Risk Factor Based User
Case Point Test Effort Estimation Model
Compatible with Agile Software Development
Masud Parvez
Senior IT Project Manager, IT Services
RMIT International University
Email: niloy.cit1@gmail.com

5. Index
1.Estimation in software outsourcing
2.Our Investigation
3.Use Case Test Effort estimation
4.Our Developed New Layer
5.Project Demonstration
6.Performance Analysis
7.Future Research Opportunities
8.Question / Answer
5

6.  Software project bidding
 Planning for whole project
 Next cycle estimation
6
Estimation in Software outsourcing

7. •FIA (finger in the air) or best guess
•Use case point estimation method
•Functional point estimation method
•Ad-hoc method, WBS
•Function points / Test point Analysis
•Percentage of development effort method
•Percentage distribution
•Etc.
7
Test Effort Estimation Models

8. •Well Used in Agile Outsourcing industry
Investigation and Challenges :
•Different teams
•Different skill set of the members
•Conversion Factor
•New / Young test studio
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Our Motivation and Investigation

9. •Determine and compute the unadjusted use case point
(UUCP).
•Determine and compute the technical complexity factor
(TCF).
•Determine and compute the Environmental complexity
factor (ECF).
•Calculate final (primary) Use case point (UCP).
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Use Case Test Effort Estimation

10. •Efficiency Factor (EF)
•Risk Factor (RF)
10
Two Keys in our New Layer

11. 1. Test Cycle length and cycle weight
2. Test Team member definition
3. Compute efficiency factor
4. Determine Risk factor for each tester
5. Compute average efficiency factor
6. Effort adjustment
11
Steps to complete the new layer

12. Test Cycle length and cycle weight
12
Class Duration of Test Cycle Cycle weight
Class 1 1-2 working days 0.2
Class 2 3-4 working days 0.4
Class 3 5-7 working days 0.6
Class 4 8-10 working days 0.8
Class 5 11- 12 working days 1.0

13. Test Team member definition
13
Tester Level Skill Set Level weight
Level 1 Fresher / Young 1
Level 2 Average skill level 2
Level 3 Standard skill 3
Level 4 Expert 4

14. Compute efficiency factor (EF)
Formula to compute the efficiency factor for tester n :
EFn = LWn * YEn * [{ ( Bug number of Cycle Z /Cycle weight) /100 +….
…….+ (Bug number of Z-tc/Cycle weight) /100 }/tc]
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Tester No.
Level weight
(LW)
Year of exp.
(YE)
Bug in each cycle
Cycle z Cycle z-1 …. Cycle z-tc
BN CW BN CW BN CW
Tester 1 LW1 YE1
…..
Tester n LWn YEn

15. Define Risk factor for each tester (RF)
15
EF score
range
Comment Action Risk %
0.1 – 0.39 Very low
Remove from the team and
engage in training ( if not
fresher)
Replace
0.4 – 0.59 Low
Add risk by considering KPIs
20 % - 45 %
0.6– 0.89 Fair
Add risk
10 % - 20%
0.90–0.99 Good
Add no risk
0%
1+ Excellent Round it to 0.95 0%

16. Compute Average Efficiency Factor (AEF)
The formulate to complete Future efficiency factor :
•For Tester 1 , FEF1= (EF1 + RV1)/10
•………….
•For Tester n, FEFn= (EFn + RFn)/10
•
Business formula to compute the average efficiency factor (AEF) :
•AEF= (FEF1+ FEF2+…………+FEFn) / n
16

17. Test effort adjustment and Final Effort (FE)Test effort adjustment and Final Effort (FE)
The formulate to complete Final Effort (FE) :
•Final effort (FE) = Primary Total Effort (FE) / Average Efficiency Factor
(AEF)
17

18. Project DemonstrationProject Demonstration
Project Profile :
18
Project type Size Development methodology Team member
Web application Medium + Agile Scrum 14

19. Project DemonstrationProject Demonstration
Project Profile :
•Unadjusted use case point (UUCP) = 44
•environmental complexity factor ( ECF) = 2.0335
•final use case point (UCP) = 76.32
•Conversion Factor (CF) = 10
• Primary total effort (PE) = 763.2Primary total effort (PE) = 763.2
19

20. Project Demonstration – Compute (EF)
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Tester No.
Level weight
(LW)
Year of exp.
(YE)
Bug in each cycle
Cycle z Cycle z-1 Cycle z-tc
BN CW BN CW BN CW
Tester 1 3 3 30 0.5 50 0.6 30 0.4
Tester 2 3 2 40 0.6 55 0.6 45 0.5
Tester 3 2 3 30 0.5 45 0.5 25 0.3
Testers Profile :

21. Project Demonstration – Compute (EF)
Formula to compute the efficiency factor for testers :
EFn = LWn * YEn * [{ ( Bug number of Cycle Z /Cycle weight) /
100 +….…….+ (Bug number of Z-tc/Cycle weight) /100 }/tc]
EF1 = 6.54
EF2 = 5.34
EF3 = 4.62
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22. Project Demonstration – Compute (RF)
Define the risks on testers:
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Testers
Efficiency factor
(EF)
Risk factor (RF) Risk value (RV)
Tester 1
6.54 25% 1.63
Tester 2
5.34 35% 1.86
Tester 3
4.62 45% 2.07

23. Project Demonstration – Compute (FRF)
The formulate to complete Future Efficiency Factor (FEF) :
•For Tester n, FEFn= (EFn + RFn)/10
•For Tester 1 , FEF1 = 0.81
•For Tester 2 , FEF2 = 0.72
•For Tester 3 , FEF3 = 0.66
Formula to compute the Average Efficiency Factor (AEF) :
•AEF= (FEF1+ FEF2+…………+FEFn) / n
•AEF = 0.73
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24. Test effort adjustment and Final Effort (FE)Test effort adjustment and Final Effort (FE)
The formulate to complete Final Effort (FE) :
•Final effort (FE) = primary total effort (FE) / average
efficiency factor (AEF)
•FE= 1045 Hours.
24

25. Performance Analysis
25
Type Size
Expert
(Hour)
UC (Hour)
Our
model
(Hour)
Real effort
(Hour)
Deviation
with expert
Deviation
with UC
Deviation
with our
model
Web Large 750 870 980 1090 -32% -21% -11%
Mobile Medium 484 420 530 520 -30 % -20% +2%
Web Medium 910 780 1015 985 -8% -21% +3%
Game Small 320 230 270 254 +21% -10% +6%

26. Future Research OpportunitiesFuture Research Opportunities
•Conversion factor (CF)
26

27. Questions !
27
Thank you
My URL : masudparvez.weebly.com
Slide URL : masudparvez.weebly.com/blog