Predicting Release Time based on Software Reliability Model
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Predicting Release Time based on Software Reliability Model Hironori Washizaki , Kiyoshi Honda, Yoshiaki Fukazawa Waseda University Twitter: @Hiro_Washi [email protected] http://www.washi.cs.waseda.ac.jp/ Agile 2015 Research Track, August 6 th , 2015
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Transcript of Predicting Release Time based on Software Reliability Model
Predicting Release Time based on Software Reliability Model
Hironori Washizaki, Kiyoshi Honda, Yoshiaki Fukazawa
Waseda University
Twitter: @Hiro_Washi
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]
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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)
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#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?
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Our challenges in SRM
1. Predicting past or future?
2. Prediction with respect to each release, iteration, or testing level?
3. Uncertainty?
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Predicting, what?
Future!
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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
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#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
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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 75 300
SRM for runtime future prediction
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0
5
10
15
20
25
0 0.2 0.4 0.6 0.8 1
#Total issues predicted
at each time point
75 75 120 120
#Issues
200
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#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?
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Prediction with respect to release: a OSS case
“Foundation” http://foundation.zurb.com/
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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?
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Uncertainty
15 15
Prediction with uncertainty
0
10
20
30
40
50
60
70
80
90
0 2 4 6 8 10 12 14
#Is
su
es
Time
ActualData
Our model
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Prediction with uncertainty
0
10
20
30
40
50
60
70
80
90
0 2 4 6 8 10 12 14
#Is
su
es
Time
ActualData
Our model
-
+
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Lower (worst case)
Upper (best case)
T1 T2
Uncertainty patterns and prediction
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0 0.5 1
Constant Increase 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
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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)?
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