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Improving your Agile Process
First...who are we?
We reduce home energy use
We reduce home energy use
by showing people how much they use
We reduce home energy use
by showing people how much they use
and then they actually use less!
Agile means:
Agile means:
short iterations
Agile means:
       short iterations
a metric sh*t-ton of practices
  chosen from a salad bar
TDD, Pair Programming,
 Scrum, Kanban Cards, BDD,
 Continuous Integration, MDD,
  Sprints, Stand-ups, Continuous
Deployment, The Planning Game, User
  Stories, Planning Poker, Sustainable Pace,
           Collective Code Ownership...
Adopting agile?
Adopting agile?
Which practices?
Already agile-ish?
Already agile-ish?
Which practices will help us and
             why?
Pick what feels good?
Pick what feels good?
Study your process and target
       problem areas.
Scientific Method!
1. 	 Gather Data
	 2. 	 Form Hypothesis
	 3. 	 Perform Experiment
	 4. 	 Analyze Results
1. Gather Data
Quantify:
good things (to increase)
Quantify:
good things (to increase)
bad things (to decrease)
The Big Bad: Bugs
The Big Bad: Bugs
(you are using a bug tracker, right?)
“We wrote less bugs than the
  previous iteration. We
      rule!” (Right?)
V2.2   V2.3


# of Bugs   25     23
Wait...What?
            V2.2      V2.3


# of Bugs   25        23      Flu took out
                             half the team!

Team Size    8         5
Raw measurements must be put
        in context
What is the “size” of an
       iteration?
LOC?
LOC?
Doesn’t fit with agile
 Hard to measure
Hours/Days?
Hours/Days?
Fixed iterations
Hard to measure
We use “Story Points”
We use “Story Points”
1, 2, 4, 8 per user story
“Story Points” could be anything that:
“Story Points” could be anything that:
changes w/ amount of work
“Story Points” could be anything that:
changes w/ amount of work
  determined consistently
“Story Points” could be anything that:
changes w/ amount of work
  determined consistently
      easy to capture
Bugs ÷ Size ==
Defect Density
V2.2   V2.3

   Bugs        25     23

Story Points   14     10

  Density      1.79   2.3
Simple
Simple
Paints Broad Strokes:
  Increase == Bad
Simple
 Paints Broad Strokes:
   Increase == Bad
Almost enough to draw
      conclusions
With a   smallamount of additional
             meta-data...
With a    amount of additional
         small

         meta-data...
  ...you can gain incredible
           insights
•Severity
•Priority
•‣
 Where Introduced:
  bad requirements
 ‣bad programming
 ‣configuration/deployment
Defect Density: unclear requirements
          Defect Density: Programming Errors
2.2


1.9


1.6


1.3


1.0
   V2.3                    V2.4                  V2.5
Defect Density - all types


2.500


2.125


1.750


1.375


1.000
     V2.3              V2.4              V2.5
Defect Density - all types
            Defect Density - Blockers

2.500


2.125


1.750


1.375


1.000
     V2.3              V2.4              V2.5
2. Form Hypothesis
Metrics give us insight
to focus on problem areas
Form a hypothesis about
problem areas and potential
         solutions
Agile practices are a goldmine
Agile practices are a goldmine
...if used sensibly in the context
          of your process
Example:
“Increasing test coverage will
 reduce our defect density”
Example:
“Pair Programming will reduce
  ‘bad programmer’ bugs”
Example:
    “BDD will help clarify
requirements so we implement
       the right thing”
3. Perform Experiment
On the next iteration, test your
         hypothesis
Start slowly;
implement one change, chosen
     for maximum impact
4. Analyze Results
The next iteration’s metrics
should prove/disprove your
        hypothesis
Repeat until profit!
This improvement method isn’t
           perfect
This improvement method isn’t
              perfect
     but it’s a GREAT start
How has this helped OPOWER?
Iteration 1
Half of the user stories not
     being tracked :(
Iteration 2
Parts of the team using different
    scale for story points :(
Iteration 3
Data looked good, baseline
       established.
Iteration 4
Lots of deploy/config errors
Other numbers same/better
Iteration 5
 Automated deployment »
   deploy errors down.
But: Defect Density was up
Iteration 6
     Lowered velocity
Set up test coverage tracking
 (final results not in yet!)
Scientific Method
Measure
Hypothesize
Experiment
 Analyze
dave@opower.com

http://www.opower.com

   @davetron5000

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