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Predicting Release Time based on
Software Reliability Model
Hironori Washizaki, Kiyoshi Honda, Yoshiaki Fukazawa
Waseda University
Twitter: @Hiro_Washi
washizaki@waseda.jp
http://www.washi.cs.waseda.ac.jp/
Agile 2015 Research Track, August 6th, 2015
When to release?
• Predicting completion based on cumulative
flow diagram [Power, Agile’14]
2
Ken Power, "Metrics for Understanding Flow,“ Proceedings of the Agile 2014 Conference, 2014.
O blog da ASPERCOM Treinamentos, O Cumulative Flow Diagram,
http://blog.aspercom.com.br/2012/04/03/cumulative-flow-diagram/
T1 T2
Done
WIP
Backlog
• It could have a broad range of completion time.
• Ready to release in terms of reliability?
Software reliability model (SRM)
3
#Issues
Actual
Predicted
Days
K. Honda, et al., Predicting Time Range Based on Generalized Software Reliability Model , APSEC’14
Logistic
Gompertz
Non-homogeneous
Poisson
process(NHPP)
Our challenges in SRM
1. Predicting past or future?
2. Prediction with respect to each release,
iteration, or testing level?
3. Uncertainty?
4
Our challenges in SRM
1. Predicting past or future?
2. Prediction with respect to each release,
iteration, or testing level?
3. Uncertainty?
5
Predicting, what?
Future!
6
Mbz1, The fossils from Cretaceous age found in Lebanon.jpg CC BY-SA 3.0
https://en.wikipedia.org/wiki/Fossil#/media/File:The_fossils_from_Cretaceou
s_age_found_in_Lebanon.jpg
Past?
Future
NEXT EXIT
7
#Issues
Actual
Predicted
Day
K. Honda, et al., Predicting Time Range Based on Generalized Software Reliability Model , APSEC’14
Industrial case
SRM for runtime future prediction
8
0
5
10
15
20
25
0 0.2 0.4 0.6 0.8 1
#Issues
#Total issues predicted
at each time point
75 75300
SRM for runtime future prediction
9
0
5
10
15
20
25
0 0.2 0.4 0.6 0.8 1
#Total issues predicted
at each time point
75 75 120120
#Issues
200
10
#Issues
#Total issues predicted
at each time point
75 120
#Issues
Something
happened!
Predication
became stable.
SRM as actionable metric!
Our challenges in SRM
1. Predicting past or future?
2. Prediction with respect to each release,
iteration, or testing level?
3. Uncertainty?
11
Prediction with respect to release: a OSS case
“Foundation”
http://foundation.zurb.com/
12
438 500
Predicted release
date: 498
Our challenges in SRM
1. Predicting past or future?
2. Prediction with respect to each release,
iteration, or testing level?
3. Uncertainty?
14
Uncertainty
1515
Prediction with uncertainty
0
10
20
30
40
50
60
70
80
90
0 2 4 6 8 10 12 14
#Issues
Time
ActualData
Our model
16
Prediction with uncertainty
0
10
20
30
40
50
60
70
80
90
0 2 4 6 8 10 12 14
#Issues
Time
ActualData
Our model
-
+
17
Lower (worst case)
Upper (best case)
T1 T2
Uncertainty patterns and prediction
18
0 0.5 1
ConstantIncrease Decrease
Conclusion and future work
• Our contributions: using SRM
– Predicting future
– Prediction with respect to release and testing level
– Uncertainty patterns
– Tool available as Jenkins plug-in
19
https://jenkins-ci.org/
• Future work
– How to specify appropriate uncertainty pattern?
– Prediction with respect to release, testing level or
iteration?
– How work well for Agile developments?
– Combination of feature completion (Cumulative Flow
Diagram) and issues (Software Reliability Model)?
Thank you very much! Questions?
• washizaki@waseda.jp
• @Hiro_Washi
20

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Predicting Release Time based on Software Reliability Model

  • 1. Predicting Release Time based on Software Reliability Model Hironori Washizaki, Kiyoshi Honda, Yoshiaki Fukazawa Waseda University Twitter: @Hiro_Washi washizaki@waseda.jp http://www.washi.cs.waseda.ac.jp/ Agile 2015 Research Track, August 6th, 2015
  • 2. When to release? • Predicting completion based on cumulative flow diagram [Power, Agile’14] 2 Ken Power, "Metrics for Understanding Flow,“ Proceedings of the Agile 2014 Conference, 2014. O blog da ASPERCOM Treinamentos, O Cumulative Flow Diagram, http://blog.aspercom.com.br/2012/04/03/cumulative-flow-diagram/ T1 T2 Done WIP Backlog • It could have a broad range of completion time. • Ready to release in terms of reliability?
  • 3. Software reliability model (SRM) 3 #Issues Actual Predicted Days K. Honda, et al., Predicting Time Range Based on Generalized Software Reliability Model , APSEC’14 Logistic Gompertz Non-homogeneous Poisson process(NHPP)
  • 4. Our challenges in SRM 1. Predicting past or future? 2. Prediction with respect to each release, iteration, or testing level? 3. Uncertainty? 4
  • 5. Our challenges in SRM 1. Predicting past or future? 2. Prediction with respect to each release, iteration, or testing level? 3. Uncertainty? 5
  • 6. Predicting, what? Future! 6 Mbz1, The fossils from Cretaceous age found in Lebanon.jpg CC BY-SA 3.0 https://en.wikipedia.org/wiki/Fossil#/media/File:The_fossils_from_Cretaceou s_age_found_in_Lebanon.jpg Past? Future NEXT EXIT
  • 7. 7 #Issues Actual Predicted Day K. Honda, et al., Predicting Time Range Based on Generalized Software Reliability Model , APSEC’14 Industrial case
  • 8. SRM for runtime future prediction 8 0 5 10 15 20 25 0 0.2 0.4 0.6 0.8 1 #Issues #Total issues predicted at each time point 75 75300
  • 9. SRM for runtime future prediction 9 0 5 10 15 20 25 0 0.2 0.4 0.6 0.8 1 #Total issues predicted at each time point 75 75 120120 #Issues 200
  • 10. 10 #Issues #Total issues predicted at each time point 75 120 #Issues Something happened! Predication became stable. SRM as actionable metric!
  • 11. Our challenges in SRM 1. Predicting past or future? 2. Prediction with respect to each release, iteration, or testing level? 3. Uncertainty? 11
  • 12. Prediction with respect to release: a OSS case “Foundation” http://foundation.zurb.com/ 12 438 500 Predicted release date: 498
  • 13. Our challenges in SRM 1. Predicting past or future? 2. Prediction with respect to each release, iteration, or testing level? 3. Uncertainty? 14
  • 15. Prediction with uncertainty 0 10 20 30 40 50 60 70 80 90 0 2 4 6 8 10 12 14 #Issues Time ActualData Our model 16
  • 16. Prediction with uncertainty 0 10 20 30 40 50 60 70 80 90 0 2 4 6 8 10 12 14 #Issues Time ActualData Our model - + 17 Lower (worst case) Upper (best case) T1 T2
  • 17. Uncertainty patterns and prediction 18 0 0.5 1 ConstantIncrease Decrease
  • 18. Conclusion and future work • Our contributions: using SRM – Predicting future – Prediction with respect to release and testing level – Uncertainty patterns – Tool available as Jenkins plug-in 19 https://jenkins-ci.org/ • Future work – How to specify appropriate uncertainty pattern? – Prediction with respect to release, testing level or iteration? – How work well for Agile developments? – Combination of feature completion (Cumulative Flow Diagram) and issues (Software Reliability Model)?
  • 19. Thank you very much! Questions? • washizaki@waseda.jp • @Hiro_Washi 20