Backlog proritization

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We can think of backlog prioritization as a knapsack problem.

Technology Business

Backlog Prioritization,
Knapsack Problem, and
Theme Feature Maps
Using computation to aid in prioritization

Tarun Sukhani
March 6, 2012

Backlog Prioritization (BP)
Problem
 Backlog prioritization is one of the most
important duties of the PO because it
determines in what order features will be
released.
 Despite this, the methods to prioritize stories
or features are mostly intuitive.
 Mike Cohn recommends a “Bubble Sort”
where items are compared to each other and
swapped until the highest value items are on
top.
 However, this approach does not necessarily
yield an optimal packing for each sprint. It
also may underestimate certain types of risk.

Enter The Knapsack Problem
(KP) C = 25 kg 9 kg

4 kg

RM1400
RM1200

20 kg
10 kg
3 kg

RM800

RM1500
RM2000

Reformulate BP as KP
 Now the shopping cart becomes the
team with certain sprint capacity C =
<number of story points>
 Each available backlog item can be
inserted into the sprint based on its
size and value. We can try to
maximize the value.

Two Approaches to BP
Minimize Risk Maximize Value

This is the Markowitz Model: This is the Triage Model:

We want to obtain a decent level of
We want to obtain a decent level of
return (value) for a low level of
risk for a high level of return (value)
risk

This problem is solvable for a single
team
Dynamic Genetic Algorithm
Programming
Stochastic selection and
reproduction
Divide-and-conquer

Sacrifice time for space – O(n)
Sacrifice space for time – O(nC)

For multiple teams? GOOD LUCK!

 The problem is NP-hard for multiple
teams, and if the risks vary across
team-item assignments, which they
probably will, then it becomes even
harder! (GAP)
 There are pseudo-polynomial time
approximation schemes for solving
them nonetheless. However, that’s
beyond the scope of this discussion.

Remember the DSL?
PROJECT 4 SomeProject
TEAM 45 0 11.25 SomeTeam
THEME User Management
USRREG 28 3000
USRPRO 13 1200 USRREG
PEPALO 20 1200 USRPRO
ROLES 13 1200 PEPALO
THEME Data Management
COMINS 10 2000
DREQAL 15 2000
PATCON 15 3000
PATSER 28 3000
SUPCLI 25 1200 DREQAL
EDICPC 25 800 PATCON COMINS
EDPACS 45 1200 PATSER SUPCLI
THEME Appointment Management
PATVIS 26 3000 COMINS USRPRO
PATEXA 23 3000 PATCON PATVIS PATSER
PATTRE 25 3000 PATEXA DREQAL
PATSCH 10 800 PATVIS
THEME Order Management
ORDINV 26 2000 EDPACS
THEME Account Management
ACCTNG 40 1200 PATTRE ORDINV
THEME Feedback Management
REPLOG 40 1200 PATSCH ACCTNG ROLES
THEME Quality Management
NOFNRQ 8 2000 REPLOG EDICPC

Remember Project Trees?

Theme Feature Maps

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Backlog proritization

1. Backlog Prioritization, Knapsack Problem, and Theme Feature Maps Using computation to aid in prioritization Tarun Sukhani March 6, 2012

2. Backlog Prioritization (BP) Problem  Backlog prioritization is one of the most important duties of the PO because it determines in what order features will be released.  Despite this, the methods to prioritize stories or features are mostly intuitive.  Mike Cohn recommends a “Bubble Sort” where items are compared to each other and swapped until the highest value items are on top.  However, this approach does not necessarily yield an optimal packing for each sprint. It also may underestimate certain types of risk.

3. Enter The Knapsack Problem (KP) C = 25 kg 9 kg 4 kg RM1400 RM1200 20 kg 10 kg 3 kg RM800 RM1500 RM2000

4. Reformulate BP as KP  Now the shopping cart becomes the team with certain sprint capacity C = <number of story points>  Each available backlog item can be inserted into the sprint based on its size and value. We can try to maximize the value.

5. Two Approaches to BP Minimize Risk Maximize Value This is the Markowitz Model: This is the Triage Model: We want to obtain a decent level of We want to obtain a decent level of return (value) for a low level of risk for a high level of return (value) risk

6. This problem is solvable for a single team Dynamic Genetic Algorithm Programming Stochastic selection and reproduction Divide-and-conquer Sacrifice time for space – O(n) Sacrifice space for time – O(nC)

7. For multiple teams? GOOD LUCK!  The problem is NP-hard for multiple teams, and if the risks vary across team-item assignments, which they probably will, then it becomes even harder! (GAP)  There are pseudo-polynomial time approximation schemes for solving them nonetheless. However, that’s beyond the scope of this discussion.

8. Remember the DSL? PROJECT 4 SomeProject TEAM 45 0 11.25 SomeTeam THEME User Management USRREG 28 3000 USRPRO 13 1200 USRREG PEPALO 20 1200 USRPRO ROLES 13 1200 PEPALO THEME Data Management COMINS 10 2000 DREQAL 15 2000 PATCON 15 3000 PATSER 28 3000 SUPCLI 25 1200 DREQAL EDICPC 25 800 PATCON COMINS EDPACS 45 1200 PATSER SUPCLI THEME Appointment Management PATVIS 26 3000 COMINS USRPRO PATEXA 23 3000 PATCON PATVIS PATSER PATTRE 25 3000 PATEXA DREQAL PATSCH 10 800 PATVIS THEME Order Management ORDINV 26 2000 EDPACS THEME Account Management ACCTNG 40 1200 PATTRE ORDINV THEME Feedback Management REPLOG 40 1200 PATSCH ACCTNG ROLES THEME Quality Management NOFNRQ 8 2000 REPLOG EDICPC

9. Remember Project Trees?

10. Theme Feature Maps