This document introduces a new method for estimating story points called User Story Point (USP) estimation. It is presented as an empirical, algorithmic-based method that aims to address inconsistencies with current estimation practices. The USP method involves two main steps: 1) assessing productivity factors for a team and story, and 2) determining a story's complexity score. These scores are then used in a calculation to assign a USP value to a user story. Advantages of the USP method include being objective, consistent, analytical and adaptable to changing team environments and technologies.

1. USER STORY
POINT
ESTIMATION

2. Hello!
I am Fred Heath
Developer, Problem solver, Ruby / Nim evangelist, Agile practitioner.
You can find me at:
FredAtBootstrap
www.bootstrap.me.uk

3. SCRUM
ESTIMATION
● Planning Poker
● T-Shirt Size
● Fruit Eating
● …and others
Story
Points

6. “There is currently no standard unit of
measurement in software development

7. What’s this all about
● Current estimation methods not fit for purpose
● A new algorithmic-based, empirical way introduced:
USP estimation
USP = User Story Point

9. 4 -
easy

10. 4 -
easyNot to
me - 16

11. #!@%

12. but, look
here……
…………..
easy, see?

13. I suppose
you’re
right,
4 then!

15. Ben implements the story
Ben struggles
Jerry helps
Ben struggles some more
PO getting stressed
Scrum begins...

16. What went wrong
Environmental
factors ignored
Ben unfamiliar
with project,
programming
language and
application
domain.
Technical factors
ignored
Asynchronous
processing is
inherently
complex, setup is
not trivial.
Human interaction
factors ignored
Jerry charismatic
and influential
leader. Peer
pressure always
wins.

17. Key problems with current estimation
methods
● Inconsistent
● Subjective
● Unreliable

18. Velocity Assumptions
● Stable previous velocity
● Team doesn’t change
● Sprint duration doesn’t change
● Technology / platform doesn’t change
● Individual productivity doesn’t change

19. USER STORY POINT ESTIMATION
An empirical, algorithmic-based estimation method

20. Use Case Point
Estimation
for Object-Oriented, Use-Case based development

21. Use Case Points
Environmental Factors
(EF)
Project familiarity,
application experience,
motivation, language
complexity, etc.
Technical Factors (TF)
Complex algorithms,
concurrency,
cross-platform support,
etc.
Use Case complexity
(UUCP)
Simple, average or
complex
Use Case Points = UUCP * TCF * ECF

22. UCP References
1. Karner, G, 1993, “Metrics for Objectory”. Diploma thesis, University of
Linköping, Sweden. No. LiTHIDA-Ex-9344:21. December 1993.
2. Smith, John, 1999, “The Estimation of Effort Based on Use-Cases.
Rational Software.Cupertino, CA.TP-171. October 1999.
3. Anda, Bente, D. Dreiem, Dag Sjøberg and Magne Jørgensen, 2001,
“Estimating Software Development Effort Based on Use-Cases -
Experiences from Industry”, The Unified Modeling Language.Modeling
Languages, Concepts, and Tools, 4th International Conference,
Toronto, Canada, October 1-5, 2001, LNCS 2185
4. Roy Clem, Project Estimation with Use Case Points,
http://www.codeproject.com/KB/architecture/usecasep.aspx

23. UCP
USP
Object-Orientated
UML
Waterfall
Multi-paradigm
Agile
Use Cases
User Stories

24. USP - STEP 1
PRODUCTIVITY
FACTORS

25. PRODUCTIVITY FACTORS

26. Total Impact
= Σ(F1..8 weight * F1..8 score)

27. P FACTOR
PF = 1.4 + (-0.03*Total Impact)

28. USP - STEP 2
USER STORY
COMPLEXITY

29. COMPLEXITY FACTORS (1/2)
Weight Name
2
Distributed or Parallel
processing
2
Concurrency or
Synchronisation
2 High Performance
1 Reusability & Modularity
1
Complex internal
Processing

30. COMPLEXITY FACTORS (2/2)
Weight Name
0.5 Ease of Use
1.5 Cross-Platform Support
0.5
Complex Setup or
installation
1
Extensibility &
Compliance
1 Security

31. USER STORY COMPLEXITY
Complexity Score (USCS) =
5 + Σ{Weights}

32. USER STORY POINTS
USP = USCS * PF

33. Example: Shopping
app
Estimate during planning meeting. Whole team participates.

34. PRODUCTIVITY FACTORS
Name Weight Score Impact
Project Familiarity 1.5 4 6
Application Experience 0.5 4 2
Programming paradigm
experience
1 2 2
Team Cohesion 1 4 4

35. PRODUCTIVITY FACTORS
Name Weight Score Impact
User Story Robustness 1.5 5 7.5
New / part-time
team-members
-1 3 -3
Programming language
complexity
-1 3 -3
Motivation 1 5 5
TOTAL IMPACT = 20.5

36. P FACTOR
PF = 1.4 + (-0.03*20.5)
= 0.785

37. User Story
Epic: “As a user, I want to backup my hard
drive.”
Story: “As a power user, I can specify files or
folders to backup based on file size, date
created and date modified.”
USCP = 7

38. USER STORY POINTS
USP = 7 * 0.785
= 5.495
=~ 5.5

39. USP ADVANTAGES
● Objective
● Consistent
● Analytical
● Adaptable to change

40. WORKSHOP
● Detailed case scenarios
● Velocity
● Ideal Sprint
● Forecasting

41. “The greatest gift is the power to estimate
things at their true worth
Francois de la Rochefoucauld

42. Discussion, suggestions, feedback
https://muut.com/usp-estimation

43. THANKS!
Any questions?
You can find me at:
@FredAtBootstrap
fred@bootstrap.me.uk