The document discusses 5 key challenges in developing a computer vision startup: quality, time to market, changing requirements, user experience, and efficient teamwork. It recommends using an iterative development process like Scrum to balance these challenges by having short iterations, prioritizing requirements, estimating work, and protecting development teams from interruptions during sprints. Scrum uses backlogs, sprints, planning poker for estimating, and burndown charts to help manage the project in a flexible way that can adapt to changing needs.

1. 8. Software Engineering

2. Learnings from founding a Computer Vision Startup
Research vs Developing a Product

3. Learnings from founding a Computer Vision Startup
Challenge 1: Quality
Stability
Modularity
Robustness
Flexibility
Flickr: sbisson

4. Learnings from founding a Computer Vision Startup
Challenge 2: Time to market
Effort Estimation
Effort vs Reward
Priorization
Flickr: Kristian M
Often in direct conflict with quality

5. Learnings from founding a Computer Vision Startup
Challenge 3: Changing requirements
A new competitor
Customer feedback
Flickr: 416style A new technology

6. Learnings from founding a Computer Vision Startup
Flickr: geoffeg
Challenge 4: User Experience

7. Learnings from founding a Computer Vision Startup
Flickr: royskeane
Challenge 5: Efficient Teamwork

8. Learnings from founding a Computer Vision Startup
Summary Challenges
Time To
Market
Quality
Efficient
Changing Teamwork
Requirements
User
Experience

9. Learnings from founding a Computer Vision Startup
Running the project
Time To Market
Quality Needed: a process that is
Changing
Efficient
Teamwork
- Flexible (Changing Requirements)
Requirements
- Quickly leads to results (Time to Market)
User Experience
- Little overhead (Efficient teamwork)
- Shows problems early on
(Time to Market and Quality)

10. Learnings from founding a Computer Vision Startup
Traditional way of running software projects
What if requirements
change halfway
through project?
What if unexpected
issues arise?
waterfall model
(image source: Wikipedia)

11. Learnings from founding a Computer Vision Startup
A better way to run software projects
Do short iterations with all
phases: iterative process
Iteration 1
Examples:
- Scrum
Iteration 2 - RUP (Rational Unified Process)
- ...
Iteration 3

12. Scrum et al. (Google Tech Talk)
Learnings from founding a Computer Vision Startup
Ken Schwaber
http://www.youtube.com/watch?v=IyNPeTn8fpo

13. Learnings from founding a Computer Vision Startup
Scrum

14. Learnings from founding a Computer Vision Startup
Backlogs
Requirements written as “User Stories”
Example: “iPhone app user can log-in with username and
password, in order to log history of requests”
Effort for each story is estimated (how, see later)
Prioritized list of stories in the Product Backlog. Re-prioritize
regularly.
For each sprint, just select top-n stories to Sprint Backlog

15. Learnings from founding a Computer Vision Startup
Sprints
Sprint = Iterations (2-4 weeks)
Meetings
Sprint Planning Meeting
-> Team decides how much it can do in
the Sprint (not the manager).
Set a goal.
Daily Scrum
15 minutes max (“timeboxed”).
Each team member:
What I am working on now? / what’s next? / problems?
Check progress on burndown chart. If needed drop tasks.
Sprint Review Meeting

16. Learnings from founding a Computer Vision Startup
NO INTERRUPTIONS DURING SPRINT
why??
“People need uninterrupted time to get things done”
http://gettingreal.37signals.com/ch07_Alone_Time.php
With programmers, it's especially hard. Productivity depends on being
able to juggle a lot of little details in short term memory all at once
http://www.joelonsoftware.com/articles/fog0000000068.html

17. Learnings from founding a Computer Vision Startup
Estimations
Product Backlog: Stories are estimated roughly
Unit: Story points. A measure of “size” / ”complexity”
Ex 1: 0, ½, 1, 2, 3, 5, 8, 13, 20, 40, 100
Ex 2: 0, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89
Team estimates using Planning Poker
Sprint Backlog: Each story gets assigned
detailed tasks (in hours) at Sprint Planning
Longterm planning: velocity = story points / sprint
“we need about 3 sprints to finish this module at our current velocity”

18. Learnings from founding a Computer Vision Startup
Why a process at all? Why Scrum?
You need some process, otherwise you get lost. At the
latest with more than 5 people more than 5 customers.
Scrum doesn’t get in the way. Simple. Lightweight.
“Advanced common sense” *
Perfect for startups
* David Allen, in order to describe “Getting Things Done”

19. Learnings from founding a Computer Vision Startup
http://agilemanifesto.org/
Scrum is part of Agile

20. Learnings from founding a Computer Vision Startup
More Agile Practices
(or: more common sense)
Testing / Test Driven Development
Time To Market
Quality
Code refactoring
Efficient
Changing Teamwork
Requirements
User Experience
Continuous Integration
Pair Programming

21. Learnings from founding a Computer Vision Startup
If you don’t like Agile ...
http://www.joelonsoftware.com/articles/fog0000000043.html

22. Learnings from founding a Computer Vision Startup
Consolidated must-haves
Subversion: http://subversion.tigris.org/
Source control (no-brainer)
Git: http://git-scm.com/
Google Test (http://code.google.com/p/googletest/)
Automated Testing
Often part of good frameworks (e.g. Rails)
CDash http://www.cdash.org/
Continuous Integration
Cruise Control http://cruisecontrolrb.thoughtworks.com/
Automated daily builds
(Ruby only)
Just give your product to a few random people (in person)
Hallway Usability Testing
and observe them using it (before release)

23. Learnings from founding a Computer Vision Startup
On Deployment
- We have 20 million items indexed. Need multiple machines to keep index
- What if we have many requests, so we need to replicate the whole system
- Buying and maintaining so much hardware is expensive
Amazon Web Services (“Cloud Computing”) to the rescue

24. Learnings from founding a Computer Vision Startup
On Outsourcing Work
Do not outsource core competence
Overhead from time difference, communication,
project management over distance, travel
Graphic design, administration etc. are things to
outsource first (www.elance.com)

25. What is special about Vision?
In Terms of Engineering

26. Learnings from founding a Computer Vision Startup
What’s special about Vision (Consumer)
Software is a lot harder to do than e.g. a web-community
in PHP (means you need more money also)
Lot’s of uncertainty and complexity
Still early stage of technology
Hard to find good specialists

27. How we did it

28. Learnings from founding a Computer Vision Startup
How we did it
- Two Scrum-Teams in parallel
- Use of Story points
- 3-week sprints
- 2-day break between sprints
Ec2, Trac + Agilo, C++,
Rails, Googletest,
SVN, CDash, Reviewboard
In-house: Vision, Rails, iPhone
Outsourced: Android, Symbian etc.

29. Learnings from founding a Computer Vision Startup
Challenges we still face
User stories for system
related topics
Uncertainty in Computer
Vision related stories/tasks
Testing can still be
improved

30. Learnings from founding a Computer Vision Startup
Polar Rose: How we did it
- Two teams (infrastructure and vision)
- Sys admin & dedicated Q/A tester
- 2-3 week sprints
- daily and weekly standup Skype meetings
Ec2, S3, C++, Python
Java, Jira, Confluence, WordPress
SVN, Bamboo
In-house: Vision, Infrastructure, Web, iPhone/iPad, Android
outsourced: Symbian, small projects/prototypes

31. Q&A

32. Learnings from founding a Computer Vision Startup
Resources II
Ken Schwaber Scrum http://www.youtube.com/watch?v=IyNPeTn8fpo
Agile Manifesto http://agilemanifesto.org/
http://www.joelonsoftware.com/articles/
The Joel Test
fog0000000043.html
Wikipedia: Scrum http://en.wikipedia.org/wiki/Scrum_(development)
Wikipedia: Planning Poker http://en.wikipedia.org/wiki/Planning_poker