Your SlideShare is downloading. ×
0
Software estimation
Software estimation
Software estimation
Software estimation
Software estimation
Software estimation
Software estimation
Software estimation
Software estimation
Software estimation
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Software estimation

466

Published on

Slides from software estimation session at C'Ville's 2010 beCamp.

Slides from software estimation session at C'Ville's 2010 beCamp.

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
466
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
24
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Coping with Software Estimation<br />Simeon H.K. Fitch<br />Mustard Seed Software, LLC<br />
  • 2. What and Why<br />We have to do it<br />No one likes it<br />We’re always wrong<br />Real money and time is at stake<br />
  • 3. References<br />A Review of Surveys on Software Effort Estimation<br />KjetilMoløkken and MagneJørgensen<br />Better sure than safe? Over-confidence in judgment based software development effort prediction intervals<br />MagneJørgensen, Karl HalvorTeigen, and KjetilMoløkken<br />
  • 4. How<br />Expert based methods<br />Expert consultation<br />Intuition and experience<br />Analogy<br />Model based (Software Cost Models)<br />COCOMO<br />Use-Case-based estimation<br />FPA-metrics or other algorithm driven methods<br />Other<br />Price-to-win<br />Capacity related<br />Top-down<br />Bottom-up<br />
  • 5. Results<br />Expert estimation most frequently used method<br />No evidence that the use of formal methods (on average) lead to more accurate estimate<br />Cost overrun more common than schedule overrun<br />Average cost overrun of 30-40%<br />
  • 6. Results<br />Accuracy (according to one study)<br />If cost overrun (34%)<br />Over budget: 61%<br />Under budget: 10%<br />If schedule overrun (22%)<br />Completed after schedule: 65%<br />Completed before schedule: 4%<br />
  • 7. Results<br />Prediction intervals (estimate min/max)<br />In one study, students provided better prediction intervals than “experts”.<br />“The software professional may feel a pressure to indicate high development skills through narrow prediction intervals”<br />
  • 8. Blame<br />Cost overruns<br />Over-optimistic estimates<br />Changes in design or implementation<br />Schedule overruns<br />Optimistic planning<br />Frequent changes in specification<br />Frequent requests for changes by users<br />Users’ lack of understanding of their own requirements<br />Other (not just bad estimation)<br />
  • 9. What do you do?<br />NASA<br />
  • 10. What do you do?<br />MSS<br />Complexity measure (intuition)<br />Per developer conversion factor (complexity to time)<br />Confidence value [0..1]<br />

×