1. Greetings Márton Kamrás TUM18 – Blue section 2013.03.21.

3. WHY ENDANGERED? People have been killing killer whales since the 12 th century. They have died from oil spills, and garbage in the ocean. Also toxins like radiation was spilled in the ocean.

4. WHERE THEY LIVE. They live in both coastal oceans. They also live in the tropical to arctic waters.

5. What Do They Eat? Like dolphins orcas use echolocation. killer whales eat: Fish, squid, bird, sea lion, and any other marine mammals. The kill whales are eating the blue whale.

6. Interesting Facts Killer whales can swim up to 30mph. Also do not eat or attack people. IT’S A PENIS

7. Our viewpoint (opinion) Our viewpoint opinion is to save the killer whale because the killer whale is harmless. And is a peaceful animal in both coastal oceans.

8. OUR BIBLYOGRAPHY Why Endangered Title: Whale.Authors: Sarah Blue, Shawn Buell, Stephan Creed, Scott McCarthy. Web:www.edu.pe.ca/southern kings/whale LOCATION: LOCATION: Web: www.pacificwhale.org/children’sforce Web: www.pacificwhale.org/children’sforcewww.pacificwhale.org/children’sforce Interesting Facts: Interesting Facts: Title: World book Publisher: Scott Eetzer company Publisher: Scott Eetzer company

9. Expert contra algorithmic estimation Márton Kamrás TUM18 Blue section

10. Generally about Estimation (within Agile context) Def.1: An attempt to predict the duration or cost of a project.

11. Generally about Estimation (within Agile context) Def.2: Estimation is a calculated appoximation of a result wich is usable even if input data may be incomplete or uncertain. (wiki)

12. Generally about Estimation (within Agile context) By definition, estimation is not accurate!!4four

13. ” The more effort we put into something, the better the result. ” …Right?

14. Example

16. BEWARE Do you like wasting time? A lot of effort for slightly better results!!

18. We cannot eliminate uncertainty. No amount of additional effort will make an estimation perfect.We cannot eliminate uncertainty. No amount of additional effort will make an estimation perfect. Vary the effort you put into estimating according to purpose of the estimate.Vary the effort you put into estimating according to purpose of the estimate. Agile teams tend to stay on the left of the accuracy/effort scale.Agile teams tend to stay on the left of the accuracy/effort scale. Embrace the idea that small efforts are rewarded with big gains.Embrace the idea that small efforts are rewarded with big gains. Frequently delivered small increments of fully working, tested, integrated code result in more reliable plans.Frequently delivered small increments of fully working, tested, integrated code result in more reliable plans.

19. The Estimation Scale Why would we need a scale? <<demo>>

20. Conclusion 1 Do you know me?

21. Conclusion 2 We are best at estimating within a single order of magnitude.

22. Example for scale Bucket sizes: 1, 2, 3, 5 and 8Bucket sizes: 1, 2, 3, 5 and 8 1 is the chosen unit1 is the chosen unit 2=2*1, 3=3*1,3=1.5*2 etc…2=2*1, 3=3*1,3=1.5*2 etc… Nonlinear sequences reflect the greater uncertainty for larger untis.Nonlinear sequences reflect the greater uncertainty for larger untis.

23. 0? 0? 10, 20, 30, 50 – still within a single order of magnitude10, 20, 30, 50 – still within a single order of magnitude You need to pre-identify.You need to pre-identify.

24. Deriving an Estimate Expert-based estimationExpert-based estimation Algorithmic estimationAlgorithmic estimation

25. Expert-based Guess what.. an expert is askedGuess what.. an expert is asked The X/t relies on his/her intuition or gut feel and provides an estimateThe X/t relies on his/her intuition or gut feel and provides an estimate

26. BECAUSE Less useful on agile projects than on traditional projects. Estimates are assigned to user stories, user-valued functionalityEstimates are assigned to user stories, user-valued functionality It is difficult to find one suitable expert who assess the effort across all disciplines.It is difficult to find one suitable expert who assess the effort across all disciplines.

27. You cannot know for sure who will do specific works – actually anyone may work on anything.You cannot know for sure who will do specific works – actually anyone may work on anything. Everyone should have input into the estimate.Everyone should have input into the estimate.

28. Algorithmically Set up an Issue Tracker – Something to contain your issues/stories/etc like a developer backlog. Give points to issues – Fibonacci or doubles works here. The point system is entirely arbitrary, but points should be relative to how hard the issue is to the other issues in the project. Estimate total number of hours to complete each issue -Based on personal experience to start.

29. Algorithmically Complete each issue – Track total amount of time it took to complete. The time when you’re actually coding, architecting, or otherwise engineering what the issue specifically asks for. Reflection – Calculate your efficiency ratio (ER). The ER is the ratio of the number of hours estimated to the actual number of hours taken. This needs to be calculated for each issue. It will lead to a developer efficiency.

30. Algorithmically Summary – At the end of the project collect your results about the efficiency of estimations. You will evolve.

31. Reference, details http://www.mountaingoatsoftware.com/sys tem/asset/file/15/aep_sample.pdfhttp://www.mountaingoatsoftware.com/sys tem/asset/file/15/aep_sample.pdf http://leadinganswers.typepad.com/leading _answers/2007/11/agile-estimatin.htmlhttp://leadinganswers.typepad.com/leading _answers/2007/11/agile-estimatin.html http://de.slideshare.net/jssunil/agile- software-estimationhttp://de.slideshare.net/jssunil/agile- software-estimation http://agilescout.com/algorithmically- estimating-developer-time/http://agilescout.com/algorithmically- estimating-developer-time/

32. Thank you - Danke schön - ¡Gracias - obrigado – شكرا - Köszönöm – Merci - Teşekkür ederim – Děkuji - Dank u – Grazie - 谢谢