code.talks 2015 hosted by ABOUT YOU

1. Data-Driven
Software Development -
The quest for the intelligent machine that manages software projects
freiheit.com technologies - Hamburg, September 29, 2015
code.talks 2015

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Thanks for coming!
WHO AM I?
Me: Stefan Richter
● Dipl.-Ing. + Dipl.-Inf.
● First program: I983
● First language: UCSD Pascal
● First 15 years: Lots of C and
4GL languages
● Last 15+ years: Java (from 1.02),
Python, Common Lisp, Clojure,
JavaScript
● Current favorite: Go
My company: freiheit.com
● Founder: 1999
● Building large-scale Internet
systems since 15+ years
● 90 % developers (degree in
computer science, mathematics,
physics)
● Self-organizing, self-optimizing,
hierarchy-free organization
● Daimler, Tchibo, E&V, METRO,
SPIEGEL, Libri, Swiss Life,
1&1/WEB.DE etc.

3. © freiheit.com GOTO Conference Copenhagen, 2011 3
!

4. DATA-DRIVEN SOFTWARE DEVELOPMENT
THE QUEST FOR
THE INTELLIGENT
MACHINE THAT
MANAGES SOFTWARE
PROJECTS

5. Since 2003, we have been experimenting with systems
that support developers in their decision making.
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HISTORY: MAKE PROJECT MANAGEMENT MORE INTELLIGENT

6. TODAY I WANT TO SHOW YOU
HOW WE HAVE BUILT AN
INTELLIGENT AGENT TO
HELP US RUN OUR
SOFTWARE PROJECTS

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9. BUT WE ARE AGILE!
WE DON’T NEED
PROJECT
MANAGEMENT!

10. WHY IS BUILDING
SOFTWARE IN LARGER
TEAMS STILL DIFFICULT?

11. Too many steps, artefacts, roles.
Lot’s of communications needed.
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WATERFALL PROCESS

12. I think you have seen the short-cut, too ...
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AGILE PROCESSES
Product Owner
Value? Code!
SoftwareStories
Developer
Conclusion: It is not
enough to implement
the Scrum method.
You need to change
the whole way how an
organization works.

13. If structures are getting larger and complicated, things
become less easy ...
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LARGER ORGANIZATION, LARGER TEAMS
Product
Owner
Developer
Developer
Larger OrganizationSmall Team Larger Team
n x (n-1)
5 x 4 = 20
Communicaiton
Communicaiton
Manager
Manager

14. WE NEED TO GET
THE RIGHT INFORMATION
TO THE RIGHT PEOPLE
AT THE RIGHT TIME

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Every project looks like this: You have to deliver the
highest value to the lowest costs.
TIME VS. VALUE
Capacity
(highest value to
the lowest costs)
Time = Money
Developers
Start Today End
Past
Growth
Future
Shrinks

16. Given the same quality, faster is always better.
Power = Force x Distance
Time
NOT
Force x Distance x Time
No matter if you measure
in points or ideal days!
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FASTER IS BETTER: ABOUT MEASURING TIME AND PROGRESS

17. PROJECT MANAGEMENT
ARE WE ON TRACK?
IS THERE ANYTHING
SLOWING US DOWN?

18. “ADVENTURE IS JUST
BAD PLANNING.”
- ROALD AMUNDSEN
(First to reach the south pole on 14 December 1911)

19. A PLAN IS AN
ASSUMPTION ...

20. … AND PLANNING IS TO
CHECK YOUR ASSUMP-
TIONS AND ADAPT THEM
EVERY DAY BASED ON
YOUR PROGRESS.

21. ARE WE ON TRACK?
- What is the initial plan?
- Expected delivery? Feature set, delivery date, customer value?
- Re-implementing existing systems or how to sort 5000+ stories into a plan.
- Capacity checks, forecasts: We have a dedicated team for that.
- Forecasts are difficult no matter if you have 5 or 500 people working.
- Is our plan still correct? (80/20 rule of uncertainty)
- Having project managers running a software project. We don’t like that.
- Doing meetings to keep everybody on the same page?
- Having more meetings whenever there is a problem?
- Everybody is buying Jira, but does this help? (“A fool with a tool is still a fool.”)

22. ARE WE WORKING ON
THE RIGHT THINGS?
- MoSCoW prioritization method (MUST, SHOULD, COULD, WON’T)
- implementation order
- transparency without meetings (reporting, using the “Facebook effect”)
- Blackboards versus agile project systems
- Who is working on what? What is done, not done?
- Is everybody working on the planned stories? (Anecdotal evidence)
- The feeling of being part of a larger team

23. ARE THERE ANY
PROBLEMS?
- Are we spending an unusual amount of time somewhere?
- Are new developers getting up to speed?
- External factors? Systematic under/over estimations?
- Who needs support? Who is stuck somewhere?
- Is “done” really done? Getting feedback from users/stakeholders.
- Where are changes needed? Follow-up stories?
- What will that do to our plan?

24. WHAT IF AN INTELLIGENT
AGENT COULD DO THIS
FOR YOU?

25. Let’s talk about agent-based systems ...
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26. … or maybe like this.
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27. INTELLIGENT AGENT - DEFINITION:
In artificial intelligence, an intelligent agent
(IA) is an autonomous entity which observes
through sensors and acts upon an
environment using actuators and directs its
activity towards achieving goals (i.e. it is
"rational", as defined in economics).*
*Source WIKIPEDIA

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Simple reflex, model-based, goal-based, utility-
based and general learning agents.
INTELLIGENT AGENT

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Siri, Google Now, and Facebook bringing
general purpose intelligent agents in place.

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WE ARE COMMUNICATING
MORE AND MORE VIA INSTANT
MESSAGING SYSTEMS LIKE
WHATSAPP, SLACK, WECHAT,
FACEBOOK etc.

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33. SO HERE IS
WHAT WE HAVE BUILT ...

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36. USING GAME AI
TECHNIQUES

37. ● Target date
● Estimation
● Effort
● What is STARTED?
● What is DELIVERED
● What is ACCEPTED?
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Before we can learn from behavior, we have to get
the basics right.
Agent
State
Goals
Utility
What is the world right now?
What will it be like
if I do action x?
How happy I will be in such a
state?
World
Percepts EnvironmentSensors
Actuators
⎬ Stories
⎬ Progress
Action

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Goal-oriented action planning is a technique
used in building NPCs for Game AI.
STATE-MACHINES VS. GOAP
Source: http://gamedevelopment.tutsplus.com/tutorials/goal-oriented-action-planning-for-a-smarter-ai--cms-20793
State machine Loosely coupled goals/action

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To reach a goal, we can select the best next
action that reduces high insistence values.
GOAL-ORIENTED ACTION PLANNING, INSISTENCE
Goal:Eat=4, Bathroom=3
Action:Drink-Soda (Eat-2, Bathroom+3)
Action:Visit-Bathroom (Bathroom-4)
Source: Artificial Intelligence for Games

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If we want to consider side-effects of an action
we have to calculate the discontentment value.
GOAL-ORIENTED ACTION PLANNING, DISCONTENTMENT
Goal:Eat=4, Bathroom=3
Action:Drink-Soda (Eat-2, Bathroom+2)
- afterwards: Eat=2, Bathroom=5:
Discontentment=29
Action:Visit-Bathroom (Bathroom-4)
- afterwards: Eat=4, Bathroom=0,
Discontentment=16
Source: Artificial Intelligence for Games

41. HYPOTHESIS
WE CAN MODEL OUR
PROJECT WORLD USING
THE SAME METHODS.

42. All aspects of a project are monitored by specialized
software bots. These bots are working in swarms.
Slack
Sensors
Actuators
Space Planner/Bots
Slack
API
Agile Planning Tool GOAP AI
Agent Framework
Swarm
Swarm
Realtime
Databases,
other
Sources
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Super Swarm
GOETHE ARCHITECTURE

43. Sensors
● Stories (“Plan”)
● Progress (“Done”)
● Commits (Code)
● Bugs
● Calendar
● Android/Apple Watch
Actuators
● Messengers
(Slack etc.)
● IRC, XMPP
● Apple/Android Watch
● Text-2-Speech
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Bots
● Personal Bot
● Sprint/Milestone
● Project
● Anomaly Detection
● Pattern Recognition
● Neural Networks
● Planning
● Code Quality

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The actions are using Go text/templates and a
homegrown DSL to access the data.
DSL EXAMPLES
All Stories from the current milestone
All Stories in the past that are not done

45. DEMO VIDEOS

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48. SIGN-UP HERE FOR OUR
CLOSED BETA:
http://goethe.instapage.com/

49. THANK YOU
Twitter: @smartrevolution
freiheit.com technologies
Budapester Str. 45
20359 Hamburg, Germany
kontakt@freiheit.com
T +49 40 890584 0
F +49 40 890584 20

50. Hackers wanted.
Join us:
jobs@freiheit.com