The document discusses the dynamics of software evolution, focusing on the laws governing it and various methodologies for forecasting software changes. It reviews the historical context of software evolution theories, empirical studies validating these laws, and the effectiveness of time series analysis over traditional regression models for predictions. Future work includes further investigation into the controversial aspects of software evolution and refining forecasting techniques.

1. The dynamics of software evolution
EVOLUMONS 2011
Research Seminar on Software Evolution
Université de Mons, Belgium
January 26th 2011
Israel Herraiz
Universidad Alfonso X el Sabio
<isra@herraiz.org>
<herraiz@uax.es>
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2. (c) 2011 Israel Herraiz
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Get the full bibliographic references listed in these slides at
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3. Outline
● The laws of software evolution
● The nature of software evolution (for libre
software)
● How to accurately forecast software evolution.
And why it works.
● What's next?
● And what did I learn during all these years of
work?
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4. The laws of software evolution
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5. My background
● Educated as a chemical and mechanical
engineer
● Wasted my time in the chemical industry. But I
did (and do) love doing software!
– http://caflur.sf.net http://gpinch.sf.net
● Involved in the open source community since
around 2001, started a PhD in 2004 in the
Libresoft research group
– http://libresoft.es
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6. How it all started
● Godfrey and Tu ● My supervisors and I
[GT00] [GT01] wrote a paper on the
studied the evolution topic [RAGBH05]
of the Linux kernel ● At the time, I thought
● They said that the it was just one more
laws of software paper
evolution were not ● It turned out to be our
valid for Linux most cited paper
– Laws of software
evolution. What is
● Completely puzzled
that? me
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7. The topic background:
Software evolution
● How and why does
software evolve?
● Meir M. Lehman
Laws of software
evolution
● “Program evolution.
Processes of
software change”
published in 1985
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8. The laws in the seventies
● Laws of Program Evolution Dynamics (1974)
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[Leh74] [Leh85b]
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9. The evolution of the laws of
software evolution [Leh96] [LRW+97]
[MFRP06]
[Leh78] [Leh80]
[Leh85c] [LB85]
[Leh74]
[Leh85b]
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10. The laws in the present day
(I – IV)
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11. The laws in the present day
(V – VIII)
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12. Empirical studies of software
evolution
See “Empirical Studies of Open Source Evolution” by
Juan Fernandez-Ramil, Angela Lozano, Michel Wermelinger, Andrea Capiluppi 12
in Tom Mens, Serge Demeyer (eds.) Software Evolution
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13. Why the controversy about the laws
of software evolution?
● Fernandez-Ramil et al. found in the literature
empirical validation for the I, VI, VII (partially)
and VIII (partially)
● The most interesting part (for me)
– Statistical analysis of software projects and their
evolution, using time series analysis among other
techniques (suggested in ¡1974!) [Leh74] [Leh85b]
– “For maximum cost-effectiveness, management
consideration and judgement should include the entire
history of the project with the current state having the
strongest, but not exclusive, influence”
[Leh78] [Leh85c]
●
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14. The nature of (libre) software
evolution
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15. The nature of (libre) software
evolution
● The goal is to develop a theoretical model for
software evolution
● Long pursued goal
● Lehman and Belady in 1971 [BL71] [LB85]
● Woodside progressive and anti-regressive work
[Woo80] (included in [LB85])
● Turski models [Tur96] [Tur02]
– Growth is inversely proportional to complexity
– Complexity is proportional to the square of size
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16. More recent models
● Self-Organized criticality [Wu06] [WHH07]
● Power laws for the size of the system
● Long range correlations in the time series of
changes
● Maintenance Guidance Model [CFR07]
● Those functions that have suffered more changes in
the past are more likely to be changed in the future
● Assumptions:
– Distribution of accumulated changes is asymmetrical
– Developers prioritize changes using past number of
changes and complexity 16
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17. Determinism and evolution
● Self Organized Criticality
● This means that current events are influenced by
very old events
● Against Lehman suggestions [Leh78] [Leh85c]
● In my opinion, counter intuitive
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18. Long range correlated processes
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19. Long range correlated processes
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20. Long range correlated processes
Unreachable
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21. Short range correlated
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22. Short range correlated
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23. Short range correlated
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24. Short range correlated
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25. How is software evolution?
or ?
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26. Autocorrelation coefficients
...
1 2 3 4 5
r(1)
...
1 2 3 4 r(2)
...
1 2 3
.
.
.
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27. r(k) Autocorrelation coefficients
1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
k
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28. r(k) Autocorrelation coefficients
1
Long range
correlated
r k ~k 2d−1
0d 0.5
Short range
correlated
(ARIMA process)
r k ~C 1−k 
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
k
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29. r(k) Autocorrelation coefficients
1
Long range
correlated
r k ~k 2d−1
0d 0.5
Short range
correlated
(ARIMA process)
r k ~ Ai 1−k  Logarithmic
scale
0
k
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30. Empirical study
● 3,821 software projects
– More than 3 developers
– More than 1 year of active history
– 9,234,104 commits / 2,357,438 modification requests
– Projects registered between Nov. 1999 and Dec. 2004
– Datasets publicly available
● See Determinism and evolution
– 5th International Working Conference on
Mining Software Repositories (MSR 2008)
FLOSSMole
+
CVSAnalY-SF
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31. Methodology
● Liner correlation to calculate linearity
● Distribution of the Pearson coefficients
● Smoothing applied to the series before
calculating ACF
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32. Results
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33. Results
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34. Results
Long
memory
processes Short
memory
processes
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35. Looking at the numbers
Quantile Commits MRs
0 0.3235 0.2886
20 0.7394 0.7248
40 0.8178 0.8036
60 0.8906 0.8705
80 0.9783 0.9464
100 0.9998 0.9998
Long memory process
Short memory process
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36. Implications for evolution
● Short memory -> Yesterday's weather
http://doi.ieeecomputersociety.org/10.1109/ICSM.2004.1357788
● When deciding, current situation should have
more influence
● As Lehman said in 1978
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37. How to forecast software evolution
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38. Background
● Forecasting traditionally done using very simple
statistical models
● Regression
● Lehman suggested in 1974 that Time Series
Analysis was the best approach to study
software evolution
● Let's compare time series analysis against
regression models
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39. Case studies
Training set Test set
PostgreSQL
FreeBSD
NetBSD
1993 1995 1997 1999 2001 2003 2005 2007
Time
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40. Case studies
Training set Test set
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41. Time Series Analysis
Original Yes
ACF Clear
time series
PACF pattern?
data
No
Kernel
smoothing
ARIMA p, d, q
Predictions model based on
fitting ACF / PACF
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42. Parameters of the model
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43. Autocorrelation coefficients.
No smoothing
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44. Autocorrelation coefficients.
After smoothing
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45. Parameters of all the models
● Time series ARIMA model
● d=1 q=0 p = 6, 7 or 9
● Regression model
● r > 0.99
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46. How does the model look like?
   
q p
d j i
∇ x t 1−∑  j B =t 1−∑  i B
j=1 i=1
i i
B =B =x t−i
xt
∇ x t =x t −x t−1=1−B x t
d d
∇ x t =1−B x t
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47. How does the model look like?
Predicted / Actual values Estimation
Coefficients Linear component
errors
   
q p
d j i
∇ x t 1−∑  j B =t 1−∑  i B
j=1 i=1
Parameters of
the model Linear component
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48. Results
Time series (ARIMA) vs. regression
ARIMA Regression
FreeBSD 3.93 16.89
NetBSD 1.80 15.94
PostgreSQL 1.48 6.86
Mean Squared Relative Error
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49. Conclusions
● Time Series more accurate than Regression
Analysis for macroscopic predictions
● Basic model. More components can be added.
● Seasonality
● Multi-variable, combining different factors
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50. More results
● Ok, so you predicted last year...which is past...
● What about predicting real future?
MSR Challenge 2007 winners
Goal:
predicting the number of changes
in Eclipse in the next three months
http://dx.doi.org/10.1109/MSR.2007.10
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51. Why this works?
● Isn't it too accurate?
● Why do you think this works?
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52. What's next?
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53. Further work
● Write a paper about the controversy around the
validation of the laws of software evolution
● In progress
● Write a paper about the short memory nature of
evolution
● Using Time Series Analysis to show it
● And ARIMA as a forecasting tool
● Extracting principles and guidelines for software
projects management
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54. And what I did learn during all these
years?
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55. Things I appreciate my advisors did
● Freedom of movements
● Pressure to get my own funding
● Unconditional support
● Demanding and challenging environment
● Opportunity to coordinate projects
● And to participate in many meetings alone
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56. Things that I did not know and I do
now
● Know-how about conferences and journals
● English skills
● Writing skills (papers and proposals)
● Presentation skills
● Self-motivation
– Brick walls are there for the rest of people
– Experience is what you get when you don't get what
you want
– Never give up
– http://www.youtube.com/watch?v=ji5_MqicxSo 56
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57. Take away
Laws of Statistical
Software Evolution approach
Controversy Replicable study
Short memory Brick walls are
dynamics a good thing
ARIMA Keep working.
accurate forecast Don't give up
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