Icse 2013-tutorial-data-science-for-software-engineering

ICSE’13 Tutorial: Data Science for
Software Engineering
Tim Menzies, West Virginia University
Ekrem Kocaguneli, West Virginia University
Fayola Peters, West Virginia University
Burak Turhan, University of Oulu
Leandro L. Minku, The University of Birmingham
ICSE 2013
May 18th - 26th, 2013
San Francisco, CA
http://bit.ly/icse13tutorial

Who we are…
1
Tim Menzies
West Virginia University
tim@menzies.us
Ekrem Kocaguneli
ekrem@kocaguneli.com
Fayola Peters
fayolapeters@gmail.com
Burak Turhan
University of Oulu
turhanb@computer.org
Leandro L. Minku
The University of Birmingham
L.L.Minku@cs.bham.ac.uk

OUTLINE
• PART 0: Introduction
• PART 1: Organization Issues
– Rule #1: Talk to the users
– Rule #2: Know your domain
– Rule #3: Suspect your data
– Rule #4: Data science is cyclic
• PART 2: Data Issues
– How to solve lack or scarcity of data
– How to prune data, simpler & smarter
– How to advance simple CBR methods
– How to keep your data private
• PART 3: Model Issues
– Problems of SE models
– Solutions
• Envy-based learning
• Ensembles
2

OUTLINE
– Solutions
• Ensembles
3

What can we share?
• Two software project
managers meet
– What can they learn
from each other?
• They can share
1. Data
2. Models
3. Methods
• techniques for turning
data into models
4. Insight into the domain
• The standard mistake
– Generally assumed that
models can be shared,
without modification.
– Yeah, right…
4

SE research = sparse sample of a
very diverse set of activities
5
Microsoft research,
Redmond, Building 99
Other studios,
many other projects
And they are all different.

Models may not move
(effort estimation)
• 20 * 66% samples of
data from NASA
• Linear regression on
each sample to learn
effort = a*LOCb *Σiβixi
• Back select to remove
useless xi
• Result?
– Wide βivariance
6* T. Menzies, A.Butcher, D.Cok, A.Marcus, L.Layman, F.Shull, B.Turhan, T.Zimmermann, "Local vs. Global Lessons for Defect Prediction and
Effort Estimation," IEEE TSE pre-print 2012. http://menzies.us/pdf/12gense.pdf

Models may not move
(defect prediction)
7* T. Menzies, A.Butcher, D.Cok, A.Marcus, L.Layman, F.Shull, B.Turhan, T.Zimmermann, "Local vs. Global Lessons for Defect Prediction and
Effort Estimation," IEEE TSE pre-print 2012. http://menzies.us/pdf/12gense.pdf

Oh woe is me
• No generality in SE?
• Nothing we can learn
from each other?
• Forever doomed to never
make a conclusion?
– Always, laboriously,
tediously, slowly, learning
specific lessons that hold
only for specific projects?
• No: 3 things we might
want to share
– Models, methods, data
• If no general models, then
– Share methods
• general methods for
quickly turning local data
into local models.
– Share data
• Find and transfer relevant
data from other projects to
us
8

The rest of this tutorial
• Data science
– How to share data
– How to share methods
• Maybe one day, in the future,
– after we’ve shared enough data and methods
– We’ll be able to report general models
– ICSE 2020?
• But first,
– Some general notes on data mining
9

OUTLINE
– Solutions
• Ensembles
10

OUTLINE
–Rule #1: Talk to the users
– Solutions
• Ensembles
11

The great myth
• Let’s face it:
– Humans are a pest
– And experts doubly so.
• “The notion of ‘user’ cannot be
precisely defined and therefore
has no place in CS and SE”
– EdsgerDijkstra, ICSE’4, 1979
• http://en.wikipedia.org/wiki/List_
of_cognitive_biases
• 96 Decision-making, belief and
behavioral biases
– Attentional bias – paying more
attention to emotionally dominant
stimuli in one's environment and to
neglect relevant data
• 23 Social biases
– Worse-than-average effect –
believing we are worse than others at
tasks which are difficult
• 52 Memory errors and biases
– Illusory correlation – inaccurately
remembering a relationship between
two event
12

The great myth
• Wouldn’t it be
wonderful if we did not
have to listen to them
– The dream of
oldeworlde machine
learning
• Circa 1980s
– Dispense with live
experts and resurrect
dead ones.
• But any successful
learner needs biases
– Ways to know what’s
important
• What’s dull
• What can be ignored
– No bias? Can’t ignore
anything
• No summarization
• No generalization
• No way to predict the future
13

Christian Bird, data miner,
Msoft research, Redmond
• Microsoft Research,
Redmond
– Assesses learnersby
“engagement”
A successful “Bird”
session:
• Knowledge engineers enter
with sample data
• Users take over the
spreadsheet
• Run many ad hoc queries
• In such meetings, users often…
• demolish the model
• offer more data
• demand you come back
next week with something
better 14
Expert data scientists spend more time
with users than algorithms

Also: Users control budgets
• Why talk to users?
– Cause they own the wallet
• As the Mercury astronauts used to say
– No bucks, no Buck Rodgers
• We need to give users a sense of comfort that
we know what we are doing
– That they are part of the process
– That we understand their problem and processes
– Else, budget = $0
15

The Inductive
Engineering Manifesto
• Users before algorithms:
– Mining algorithms are only useful in industry if
users fund their use in real-world applications.
• Data science
– Understanding user goals to inductively generate
the models that most matter to the user.
16
• T. Menzies, C. Bird, T. Zimmermann, W. Schulte, and E. Kocaganeli.
The inductive software engineering manifesto. (MALETS '11).

OUTLINE
–Rule #2: Know your domain
– Solutions
• Ensembles
17

Algorithms is only part of the story
18
• Drew Conway, The Data Science Venn Diagram, 2009,
• http://www.dataists.com/2010/09/the-data-science-venn-diagram/
• Dumb data miners miss important
domains semantics
• An ounce of domain knowledge is
worth a ton to algorithms.
• Math and statistics only gets you
machine learning,
• Science is about discovery and building
knowledge, which requires some
motivating questions about the world
• The culture of academia, does not
reward researchers for understanding
domains.

Case Study #1: NASA
• NASA’s Software Engineering Lab, 1990s
– Gave free access to all comers to their data
– But you had to come to get it (to Learn the domain)
– Otherwise: mistakes
• E.g. one class of software module with far more errors that
anything else.
– Dumb data mining algorithms: might learn that this kind of module in
inherently more data prone
• Smart data scientists might question “what kind of
programmer work that module”
– A: we always give that stuff to our beginners as a learning exercise
19* F. Shull, M. Mendonsa, V. Basili, J. Carver, J. Maldonado, S. Fabbri, G. Travassos, and M. Ferreira, "Knowledge-
Sharing Issues in Experimental Software Engineering", EMSE 9(1): 111-137, March 2004.

Case Study #2: Microsoft
• Distributed vs centralized
development
• Who owns the files?
– Who owns the files with most bugs
• Result #1 (which was wrong)
– A very small number of people
produce most of the core changes to
a “certain Microsoft product”.
– Kind of an uber-programmer result
– I.e. given thousands of programmers
working on a project
• Most are just re-arrange deck chairs
• TO improve software process, ignore
the drones and focus mostly on the
queen bees
• WRONG:
– Microsoft does much auto-
generation of intermediary build
files.
– And only a small number of people
are responsible for the builds
– And that core build team “owns”
those auto-generated files
– Skewed the results. Send us down
the wrong direction
• Needed to spend weeks/months
understanding build practices
– BEFORE doing the defect studies
20* E. Kocaganeli, T. Zimmermann, C.Bird, N.Nagappan, T.Menzies. Distributed Development
Considered Harmful?. ICSE 2013 SEIP Track, San Francisco, CA, USA, May 2013.

OUTLINE
–Rule #3: Suspect your data
– Solutions
• Ensembles
21

You go mining with the data you have—not
the data you might want
• In the usual case, you cannot control data
collection.
– For example, data mining at NASA 1999 – 2008
• Information collected from layers of sub-contractors and
sub-sub-contractors.
• Any communication to data owners had to be mediated by
up to a dozen account managers, all of whom had much
higher priority tasks to perform.
• Hence, we caution that usually you must:
– Live with the data you have or dream of accessing at
some later time.
22

Rinse before use
• Data quality tests (*)
– Linear time checks for (e.g.) repeated rows
• Column and row pruning for tabular data
– Bad columns contain noise, irrelevancies
– Bad rows contain confusing outliers
– Repeated results:
• Signal is a small nugget within the whole data
• R rows and C cols can be pruned back to R/5 and C0.5
• Without losing signal
23* M. Shepperd, Q. Song, Z. Sun, C. Mair,
"Data Quality: Some Comments on the NASA Software Defect Data Sets," IEEE TSE, 2013, pre-prints

e.g. NASA
effort data
24
Nasa data: most
Projects highly complex
i.e. no information in saying
“complex”
The more features we
remove for smaller
projects the better
the predictions.

OUTLINE
–Rule #4: Data science is cyclic
– Solutions
• Ensembles
25

Do it again, and again,
and again, and …
26
In any industrial
application, data science
is repeated multiples
time to either answer an
extra user question,
make some
enhancement and/or
bug fix to the method,
or to deploy it to a
different set of users.

Thou shall not click
• For serious data science studies,
– to ensure repeatability,
– the entire analysis should be automated
– using some high level scripting language;
• e.g. R-script, Matlab, Bash, ….
27

OUTLINE
– Solutions
• Ensembles
30

OUTLINE
–How to solve lack or scarcity of data
– Solutions
• Ensembles
31

How to Solve Lack or Scarcity of Local
Data
32

What are my options?
Isn’t local (within) data better?
It may not be available
It may be scarce
Tedious data collection effort
Too slow to collect
The verdict with global (cross) data?
Effort estimation1:
No clear winners, either way
Defect Prediction2:
Can use global data as a stop gap
33
1 Barbara A. Kitchenham, Emilia Mendes, GuilhermeHortaTravassos: Cross versus Within-Company Cost Estimation Studies: A Systematic
Review. IEEE Trans. Software Eng. 33(5): 316-329 (2007)
2 B. Turhan, T. Menzies, A. Bener and J. Distefano, “On the relative value of cross-company and within-company data for defect
prediction”, Empirical Software Engineering Journal, Vol.14/5, pp.540-578, 2009.

Comparing options
• For NASA data
– Seven test sets from 10% of
each source
• Treatment CC (using global)
– Train on the 6 other data sets
• Treatment WC (using local)
– Train on the remaining 90% of
the local data
34

NN-Filtering
Step 1: Calculate the pairwise
Euclidean distances between
the local (test) set and the
candidate (global) training set.
Step 2: For each test datum,
pick its k nearest neighbors
from global set.
Step 3: Pick unique instances
from the union of those
selected across all local set to
construct the final training set
35
Now, train your favorite model on the
filtered training set!
B. Turhan, A. Bener, and T. Menzies, “Nearest Neighbor Sampling for Cross Company Defect Predictors”, in Proceedings of the 1st
International Workshop on Defects in Large Software Systems (DEFECTS 2008), pp. 26, 2008.

More Comparisons: PD
• For NASA data
– Seven test sets from 10% of each
source
– Train on the remaining 90% of the
local data
• Treatment NN (using global+NN)
– Initialize train set with 6 other data
sets,
– Prune the train set to just the 10
nearest neighbors (Euclidean)
of the test set (discarding repeats)
36
B. Turhan, T. Menzies, A. Bener and J. Distefano, “On the
relative value of cross-company and within-company data for
defect prediction”, Empirical Software Engineering Journal,
Vol.14/5, pp.540-578, 2009.

More Comparisons: PF
• For NASA data
– Seven test sets from 10% of each
source
– Train on the remaining 90% of the
local data
– Initialize train set with 6 other data
sets,
37
Vol.14/5, pp.540-578, 2009.

Vol.14/5, pp.540-578, 2009.
• For SOFTLAB data
– Three test sets from embedded
systems
– Train on the seven NASA data sets
– Train on the remaining two local
test data
– Initialize train set with 7 NASA data
sets,
External Validity

“Theoriescan be learnedfrom a
verysmallsample of availabledata”
Microsampling
• GivenN defectivemodules:
– M = {25, 50, 75, ...} <= N
– Select M defectiveand M
defect-freemodules.
– Learntheories on 2M
instances
• Undersampling: M=N
• 8/12 datasets -> M = 25
• 1/12 datasets -> M = 75
• 3/12 datasets -> M = {200,
575, 1025}
T. Menzies, B. Turhan, A. Bener, G. Gay, B. Cukic, Y. Jiang, “Implications of Ceiling Effects in Defect Predictors”, in Proceedings of the 4th
International Workshop on Predictor Models in Software Engineering (PROMISE 2008), pp. 47-54, 2008.

How about mixing local and global?
• Is it feasible to use additional data from other projects:
– (Case 1) When there is limited local project history, i.e. no prior releases
– (Case 2) When there is existing local project history, i.e. many releases over some period
42B. Turhan, A. T. Mısırlı, A. Bener, “Empirical Evaluation of The Effects of Mixed Project Data on Learning Defect Predictors”, (in print)
Journal of Information and Software Technology, 2013
• For 73 versions of 41 projects
– Reserve test sets from 10% of each
project
– Additional test sets if the project has
multiple releases
• Treatment WP (using local)
– Train on 10%..90% of the local data
– Train on the previous releases
• Treatment WP+CP (using global)
– Enrich training sets above with NN-
filtered data from all other projects
Case 1: WP(10%) + CP is as good
as WP(90%)
Case 2: WP+CP is significantly
better than WP (with small effect
size)

OUTLINE
–How to prune data, simpler & smarter
– Solutions
• Ensembles
43

How to Prune Data,
Simpler and Smarter
44
Data is the new
oil

And it has a cost too
45
e.g. $1.5M spent by NASA in the period 1987 to 1990
to understand the historical records of all their
software in support of the planning activities for the
International Space Station [1]
Do we need to discuss all
the projects and all the
features in a client meeting
or in a Delphi session?
Similarly, do we need all
the labels for supervised
methods?
[1] E. Kocaguneli, T. Menzies, J. Keung, D. Cok, and R. Madachy, “Active learning and effort estimation: Finding the essential
content of software effort estimation data,” IEEE Trans. on Softw. Eng., vol. Preprints, 2013.

Data for Industry / Active Learning
46
Concepts of E(k) matrices and popularity…
Let’s see it in action: Point to the person closest to you

47
Instance pruning
1. Calculate “popularity” of
instances
2. Sorting by popularity,
3. Label one instance at a time
4. Find the stopping point
5. Return closest neighbor from
active pool as estimate
1. Calculate the popularity of
features
2. Select non-popular features
Synonym pruning
We want to find the dissimilar
features, that are unlike others
We want the instances that are
similar to others

48
Finding the stopping point
• If all popular instances are exhausted.
Stop asking for labels if one of the rules fire
• Or if there is no MRE (magnitude of relative error = abs(actual-
predicted)/actual) improvement for n consecutive times.
• Or if the ∆ between the best and the worst error of the last n
times is very small. (∆ = 0.1; n = 3)

49
QUICK: An active learning solution, i.e. unsupervised
Instances are labeled with a cost by the expert
• We want to stop before all the instances are labeled

50
Picking random
training instance is
not a good idea
More popular instances
in the active pool
decrease error
One of the stopping
point conditions fires
X-axis: Instances sorted in decreasing popularity numbers
Y-axis:MedianMRE

51
At most 31% of all
the cells
On median 10%
Intrinsic dimensionality: There is a consensus in
the high-dimensional data analysis community
that the only reason any methods work in very
high dimensions is that, in fact, the data is not
truly high-dimensional[1]
[1] E. Levina and P.J. Bickel. Maximum likelihood estimation of intrinsic dimension. In Advances in Neural Information
Processing Systems, volume 17, Cambridge, MA, USA, 2004. The MIT Press.

OUTLINE
–How to advance simple CBR methods
– Solutions
• Ensembles
52

Case-based reasoning
(CBR) methods make use
of similar past projects
for estimation
53
They are very widely used as [1]:
• No model-calibration to local data
• Can better handle outliers
• Can work with 1 or more attributes
• Easy to explain
Two promising research areas
• weighting the selected analogies[2]
• improving design options [3]
How to Advance Simple CBR Methods
[1] F. Walkerden and R. Jeffery, “An empirical study of analogy-based software effort estimation,” Empirical Software
Engineering, vol. 4, no. 2, pp. 135–158, 1999.
[2] E. Mendes, I. D. Watson, C. Triggs, N. Mosley, and S. Counsell, “A comparative study of cost estimation models for web
hypermedia applications,” Empirical Software Engineering, vol. 8, no. 2, pp. 163–196, 2003.
[3] J. W. Keung, “Theoretical Maximum Prediction Accuracy for Analogy-Based Software Cost Estimation,” 15th Asia-Pacific
Software Engineering Conference, pp. 495– 502, 2008.

In none of the scenarios did we
see a significant improvement
54
Compare performance of
each k-value with and
without weighting.
Building on the previous research [1], we adopted two different
strategies[2]
We used kernel weighting to
weigh selected analogies
a) Weighting analogies [3]
[1] E. Mendes, I. D. Watson, C. Triggs, N. Mosley, and S. Counsell, “A comparative study of cost estimation models for web
hypermedia applications,” Empirical Software Engineering, vol. 8, no. 2, pp. 163–196, 2003.
*2+ W. Keung, “Theoretical Maximum Prediction Accuracy for Analogy-Based Software Cost Estimation,” 15th Asia-Pacific
Software Engineering Conference, pp. 495– 502, 2008.
[3] Kocaguneli, Ekrem, Tim Menzies, and Jacky W. Keung. "Kernel methods for software effort estimation." Empirical Software
Engineering 18.1 (2013): 1-24.

55
D-ABE
• Get best estimates of all training
instances
• Remove all the training instances
within half of the worst MRE (acc.
to TMPA).
• Return closest neighbor’s estimate
to the test instance.
c
t
db
e
a
f
Test instance
Training Instances
Worst MRE
Close to the
worst MRE
Return the
closest
neighbor’s
estimate
b) Designing ABE methods
Easy-path: Remove training
instance that violate assumptions
TEAK will be discussed later.
D-ABE: Built on theoretical
maximum prediction accuracy
(TMPA) [1]
[1] W. Keung, “Theoretical Maximum Prediction Accuracy for Analogy-
Based Software Cost Estimation,” 15th Asia-Pacific Software Engineering
Conference, pp. 495– 502, 2008.

D-ABE Comparison to
static k w.r.t. MMRE
56
D-ABE Comparison to
static k w.r.t. win, tie, loss

Finding enough local training data is
a fundamental problem [1]
Merits of using cross-data
from another company is
questionable [2]
We use a relevancy filtering method called TEAK
on public and proprietary data sets.
How to Advance Simple CBR Methods/
Using CBR for cross company learning
[1] B. Turhan, T. Menzies, A. Bener, and J. Di Stefano, “On the relative value of cross-company and within-company data for defect
prediction,” Empirical Software Engineering, vol. 14, no. 5, pp. 540–578, 2009.
*2+ E. Kocaguneli and T. Menzies, “How to find relevant data for effort estimation,” in ESEM’11: International Symposium on Empirical
Software Engineering and Measurement, 2011.
[3] B. A. Kitchenham, E. Mendes, and G. H. Travassos, “Cross versus within-company cost estimation studies: A systematic review,” IEEE
Trans. Softw. Eng., vol. 33, no. 5, pp. 316–329, 2007.
*4+ T. Zimmermann, N. Nagappan, H. Gall, E. Giger, and B. Murphy, “Cross-project defect prediction: A large scale experiment on data
vs. domain vs. process,” ESEC/FSE, pp. 91–100, 2009.
Similar amounts of evidence for
and against the performance of
cross-data [3, 4]

58
Cross data works as well as within data
for 6 out of 8 proprietary data sets, 19 out
of 21 public data sets after TEAK’s
relevancy filtering
Similar projects,
dissimilar effort
values, hence
high variance
Similar projects,
similar effort
values, hence
low variance
How to Advance Simple CBR Methods/
Using CBR for cross company learning
Build a second GAC
tree with low-
variance instances
Return closest neighbor’s
value from the lowest
variance region
In summary: Design options of CBR helps, but not
fiddling with single instances and weights!
[1] E. Kocaguneli and T. Menzies, “How to find relevant data for effort estimation,” in ESEM’11: International Symposium on Empirical
Software Engineering and Measurement, 2011.

OUTLINE
–How to keep your data private
– Solutions
• Ensembles
59

Is Data Sharing Worth the Risk to
Individual Privacy
• Former Governor Massachusetts.
• Victim of re-identification privacy breach.
• Led to sensitive attribute disclosure of his medical records.
What would William Weld say?

Is Data Sharing Worth the Risk to
Individual Privacy
What about NASA contractors?
Subject to competitive bidding
every 2 years.
Unwilling to share data
that would lead to
sensitive attribute disclosure.
e.g. actual software
development times

When To Share – How To Share
So far we cannot guarantee
100% privacy.
What we have is a directive
as to whether data is private
and useful enough to share...
We have a lot of privacy
algorithms geared toward
minimizing risk.
Old School
K-anonymity
L-diversity
T-closeness
But What About Maximizing Benefits (Utility)?
The degree of risk to the
data sharing entity must
not exceed the benefits of
sharing.

Balancing Privacy and Utility
or...
Minimize risk of privacy disclosure while maximizing utility.
Instance Selection with CLIFF
Small random moves with MORPH
= CLIFF + MORPH
F. Peters, T. Menzies, L. Gong, H. Zhang, "Balancing Privacy and Utility in Cross-Company Defect Prediction," IEEE Transactions on Software Engineering,
24 Jan. 2013. IEEE computer Society Digital Library. IEEE Computer Society

CLIFF
Don't share all the data.

CLIFF
"a=r1"
powerful for selection for
class=yes
more common in "yes"
than "no"
CLIFF
step1:
for each class find ranks
of all values

CLIFF
"a=r1"
powerful for selection for
class=yes
more common in "yes"
than "no"
CLIFF
step2:
multiply ranks of each
row

CLIFF
CLIFF
step3: select the most powerful
rows of each class
Note linear time
Can reduce N rows to 0.1N
So an O(N2) NUN algorithm
now
takes time O(0.01)
Scalability

MORPH
Push the CLIFF data from their original position.
y = x ± (x − z) ∗ r
x ∈ D, the original
instance
z ∈ D the NUN of x
y the resulting
MORPHed
instance
F. Peters and T. Menzies, “Privacy and utility for defect prediction: Experiments with morph,” in Software Engineering (ICSE), 2012 34th
International Conference on, june 2012, pp. 189 –199.
F. Peters, T. Menzies, L. Gong, H. Zhang, "Balancing Privacy and Utility in Cross-Company Defect Prediction,"
IEEE Transactions on Software Engineering, 24 Jan. 2013. IEEE computer Society Digital Library. IEEE Computer Society

Case Study: Cross-Company Defect Prediction (CCDP)
Sharing Required.
Zimmermann et al.
Local data not always
available
• companies too small
• product in first release, so
no past data.
Kitchenham et al.
• no time for collection
• new technology can make all
data irrelevant
T. Zimmermann, N. Nagappan, H. Gall, E. Giger, and B. Murphy, “Cross-project defect prediction: a large scale experiment on data vs. domain vs. process.”
in ESEC/SIGSOFT FSE’09,2009
B. A. Kitchenham, E. Mendes, and G. H. Travassos, “Cross versus within-company cost estimation studies: A systematic review,”
IEEE Transactions on Software Engineering, vol. 33, pp. 316–329, 2007
- Company B has little or no data to build a defect model;
- Company B uses data from Company A to build defect models;

CCDP
Better with data filtering
Initial results with cross-company defect prediction
- negative(Zimmerman FSE '09)
- or inconclusive (Kitchenham TSE '07)
More recent work show better results
- Turhan et al. 2009 (The Burak Filter)
B. Turhan, T. Menzies, A. Bener, and J. Di Stefano, “On the relative value of cross-company and within-company data for defect prediction,”
Empirical Software Engineering, vol. 14, pp. 540–578, 2009.
F. Peters, T. Menzies, and A. Marcus, “Better Cross Company Defect Prediction,” Mining Software Repositories (MSR), 2013 10th IEEE Working Conference
on, (to appear)

Making Data Private for CCDP
Here is how we look at the data
Terms
Non-Sensitive Attribute (NSA)
Sensitive Attribute
Class Attribute

Measuring the Risk
IPR = Increased Privacy Ratio
Queries Original Privatized Privacy Breach
Q1 0 0 yes
Q2 0 1 no
Q3 1 1 yes
yes = 2/3
IPR = 1- 2/3 = 0.33

Measuring the Utility
The g-measure
Probability of detection (pd)
Probability of False alarm (pf)
Actual
yes no
Predicted yes TP FP
no FN TN
pd TP/(TP+FN)
pf FP/(FP+TN)
g-measure 2*pd*(1-pf)/(pd+(1-pf))

Comparing CLIFF+MORPH to Data Swapping and K-anonymity
Data Swapping (s10, s20, s40)
A standard perturbation
technique used for privacy
To implement...
• For each NSA a certainpercent
of the values areswapped with
anyothervalue in that NSA.
• For our experiments,these
percentages are 10, 20 and 40.
k-anonymity (k2, k4)
The Datafly Algorithm.
To implement...
• Make a generalizationhierarchy.
• Replace values in the
NSAaccording to thehierarchy.
• Continue until there are k or
fewer distinct instancesand
suppress them.
K. Taneja, M. Grechanik, R. Ghani, and T. Xie, “Testing software in age of data privacy: a balancing act,” in Proceedings of the 19th ACM SIGSOFT symposium and the 13th European
conference on Foundations of software engineering, ser. ESEC/FSE ’11. New York, NY, USA: ACM, 2011, pp. 201–211.
L. Sweeney, “Achieving k-anonymity privacy protection using generalization and suppression,” Int. J. Uncertain. Fuzziness Knowl.-Based Syst., vol. 10, no. 5, pp. 571–588, Oct. 2002.

Comparing CLIFF+MORPH to Data Swapping and K-anonymity

OUTLINE
– Solutions
• Ensembles
80

OUTLINE
–Problems of SE models
– Solutions
• Ensembles
81

Problems of SE Models
• Instability is the problem of not being able to
elicit same/similar results under changing
conditions
– E.g. data set, performance measure etc.
82
• We will look at instability in 2 areas
– Instability in Effort Estimation
– Instability in Process

83
There is no agreed upon
best estimation method [1]
Methods change ranking w.r.t.
conditions such as data sets, error
measures [2]
Experimenting with: 90 solo-
methods, 20 public data sets, 7
error measures
Problems of SE Models/
Instability in Effort
[1] M. Jorgensen and M. Shepperd, “A systematic review of software development cost estimation studies,” IEEE Trans. Softw.
Eng., vol. 33, no. 1, pp. 33–53, 2007.
[2] I. Myrtveit, E. Stensrud, and M. Shepperd, “Reliability and validity in comparative studies of software prediction models,”
IEEE Trans. Softw. Eng., vol. 31, no. 5, pp. 380–391, May 2005.

84
1. Rank methods acc. to win, loss
and win-loss values
2. δr is the max. rank change
3. Sort methods acc. to loss and
observe δr values

85
We have a set of
superior methods to
recommend
Assembling solo-methods
may be a good idea
Baker et al. [1], Kocaguneli et al.
[2], Khoshgoftaaret al. [3]failed to
outperform solo-methods
But the previous evidence of
assembling multiple methods in
SEE is discouraging
Top 13 methods are CART & ABE
methods (1NN, 5NN)
[1] D. Baker, “A hybrid approach to expert and model-based effort estimation,” Master’s thesis, Lane Department of Computer
Science and Electrical Engineering, West Virginia University, 2007, available from
https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5443.
[2] E. Kocaguneli, Y. Kultur, and A. Bener, “Combining multiple learners induced on multiple datasets for software effort
prediction,” in Interna- tional Symposium on Software Reliability Engineering (ISSRE), 2009, student Paper.
[3] T. M. Khoshgoftaar, P. Rebours, and N. Seliya, “Software quality analysis by combining multiple projects and learners,”
Software Quality Control, vol. 17, no. 1, pp. 25–49, 2009.

86
Combine top 2,4,8,13 solo-methods
via mean, median and IRWM
Re-rank solo and multi-methods
together

Instability in Process: Dataset Shift/Concept Drift
87Candela JQ, Sugiyama M, Schwaighofer A, Lawrence ND (eds) (2009) Dataset shift in machine learning. The MIT Press, Cambridge, MA

Dataset Shift: Covariate Shift
• Consider a size-based effort
estimation model
– Effective for projects within the
traditional operational
boundaries of a company
• What if a change with impact
on products’ size:
– new business domains
– change in technologies
– change in development
techniques
Before
After
Effort
89
p(Xtrain) ≠ p(Xtest)
Size
B. Turhan, “On the Dataset Shift Problem in Software Engineering Prediction Models”, Empirical Software Engineering Journal, Vol.17/1-2,
pp.62-74, 2012.

Dataset Shift: Prior Probability Shift
• Now, consider a defect
prediction model…
• … and again, what if defect
characteristics change:
– Process improvement
– More QA resources
– Increased experience over
time
– Basically you improve over
time!
Before
After
%Defects
90
p(Ytrain) ≠ p(Ytest)
kLOC
B. Turhan, “On the Dataset Shift Problem in Software Engineering Prediction Models”, Empirical Software Engineering Journal, Vol.17/1-2,
pp.62-74, 2012.

Dataset Shift: Usual Suspects
Sample Selection Bias & Imbalanced Data
91B. Turhan, “On the Dataset Shift Problem in Software Engineering Prediction Models”, Empirical Software Engineering Journal, Vol.17/1-
2, pp.62-74, 2012.

Dataset Shift: Usual Suspects
Sample Selection Bias &Imbalanced Data
92B. Turhan, “On the Dataset Shift Problem in Software Engineering Prediction Models”, Empirical Software Engineering Journal, Vol.17/1-2,
pp.62-74, 2012.

Dataset Shift
Domain shift
• Be consistent in the way you
measure concepts for
model training and testing!
• *: “…the metrics based
assessment of a software
system and measures taken
to improve its design differ
considerably from tool to
tool.”
Source Component Shift
• a.k.a. Data Heterogeneity
• Ex: ISBSG contains data
from 6000+ projects from
30+ countries.
Where do the training data
come from?
vs.
Where do the test data come
from?
93
* RüdigerLincke, Jonas Lundberg, and WelfLöwe. “Comparing software metrics tools”, ISSTA '08
B. Turhan, “On the Dataset Shift Problem in Software Engineering Prediction Models”, Empirical Software Engineering Journal, Vol.17/1-
2, pp.62-74, 2012.

94B. Turhan, “On the Dataset Shift Problem in Software Engineering Prediction Models”, Empirical Software Engineering Journal, Vol.17/1-
2, pp.62-74, 2012.
Outlier
‘Detection
’
Relevancy
Filtering
Instance
Weighting
Stratification
Cost
Curves
Mixture
Models
Managing Dataset Shift
Covariate
Shift
Prior
Probability
Shift
Sampling
Imbalanced
Data
Domain
Shift
Source
Component
Shift

OUTLINE
–Solutions
• Ensembles 95

• Seek the fence
where the grass
is greener on the
other side.
• Learn from
there
• Test on here
• Cluster to find
“here” and
“there”
12/1/2011 96
Envy =
The WisDOM Of
the COWs

12/1/2011 97
@attribute recordnumber real
@attribute projectname {de,erb,gal,X,hst,slp,spl,Y}
@attribute cat2 {Avionics, application_ground, avionicsmonitoring, … }
@attribute center {1,2,3,4,5,6}
@attribute year real
@attribute mode {embedded,organic,semidetached}
@attribute rely {vl,l,n,h,vh,xh}
@attribute data {vl,l,n,h,vh,xh}
…
@attribute equivphyskloc real
@attribute act_effort real
@data
1,de,avionicsmonitoring,g,2,1979,semidetached,h,l,h,n,n,l,l,n,n,n,n,h,h,n,l,25.9,117.6
4,de,avionicsmonitoring,g,2,1979,semidetached,h,l,h,n,n,l,l,n,n,n,n,h,h,n,l,8.2,36
….
DATA = MULTI-DIMENSIONAL VECTORS

CAUTION: data may not divide neatly
on raw dimensions
• The best description for SE projects may be
synthesize dimensions extracted from the raw
dimensions
12/1/2011 98

Fastmap
12/1/2011 99
Fastmap: Faloutsos [1995]
O(2N) generation of axis of large variability
• Pick any point W;
• Find X furthest from W,
• Find Y furthest from Y.
c = dist(X,Y)
All points have distance a,b to (X,Y)
• x = (a2 + c2 − b2)/2c
• y= sqrt(a2 – x2)
Find median(x), median(y)
Recurse on four quadrants

Hierarchical partitioning
Prune
• Find two orthogonal dimensions
• Find median(x), median(y)
• Recurse on four quadrants
• Combine quadtree leaves
with similar densities
• Score each cluster by median
score of class variable
100
Grow

Q: why cluster Via FASTMAP?
• A1: Circular methods (e.g. k-means)
assume round clusters.
• But density-based clustering allows
clusters to be any shape
• A2: No need to pre-set the number of
clusters
• A3: cause other methods
(e.g. PCA) are much slower
• Fastmap is the O(2N)
• Unoptimized Python:
12/1/2011 101

12/1/2011 102
Learning via “envy”

• Seek the fence
where the grass
is greener on the
other side.
• Learn from
there
• Test on here
• Cluster to find
“here” and
“there”
12/1/2011 103
Envy =
The WisDOM Of
the COWs

Prune
104
Grow

Prune
• This cluster envies its neighbor with
better score and max
abs(score(this) - score(neighbor))
105
Grow
Where is grass greenest?

Q: How to learn rules from
neighboring clusters
• A: it doesn’t really matter
– Many competent rule learners
• But to evaluate global vs local rules:
– Use the same rule learner for local vs global rule learning
• This study uses WHICH (Menzies [2010])
– Customizable scoring operator
– Faster termination
– Generates very small rules (good for explanation)
106

Data from
http://promisedata.googlecode.com
• Effort reduction =
{ NasaCoc, China } :
COCOMO or function points
• Defect reduction =
{lucene,xalanjedit,synapse,etc } :
CK metrics(OO)
• Clusters have untreated class
distribution.
• Rules select a subset of the
examples:
– generate a treated class
distribution
107
0 20 40 60 80 100
25th
50th
75th
100th
untreated global local
Distributions have percentiles:
Treated with rules
learned from all data
Treated with rules learned
from neighboring cluster

• Lower median efforts/defects (50th percentile)
• Greater stability (75th – 25th percentile)
• Decreased worst case (100th percentile)
By any measure,
Local BETTER THAN GLOBAL
108

Rules learned in each cluster
• What works best “here” does not work “there”
– Misguided to try and tame conclusion instability
– Inherent in the data
• Can’t tame conclusion instability.
• Instead, you can exploit it
• Learn local lessons that do better than overly generalized global theories
12/1/2011 109

OUTLINE
– Solutions
• Ensembles
110

Solutions to SE Model Problems/
Ensembles of Learning Machines*
 Sets of learning machines grouped together.
 Aim: to improve predictive performance.
...
estimation1 estimation2 estimationN
Base learners
E.g.: ensemble estimation = Σ wi estimationi
B1 B2 BN
* T. Dietterich. Ensemble Methods in Machine Learning. Proceedings of the First International Workshop in
Multiple Classifier Systems. 2000.

Ensembles of Learning Machines
 One of the keys:
Diverse* ensemble: “base learners” make different
errors on the same instances.
* G. Brown, J. Wyatt, R. Harris, X. Yao. Diversity Creation Methods: A Survey and Categorisation. Journal of
Information Fusion 6(1): 5-20, 2005.

Ensembles of Learning Machines
 One of the keys:
Diverse ensemble: “base learners” make different
errors on the same instances.
 Three different types of ensembles that have
been applied for software effort estimation will
be presented in the next slides.
Different ensemble approaches can be seen as different ways to
generate diversity among base learners!

Static Ensembles
Training data
(completed projects)
training
Ensemble
 An existing training set is used for
creating/training the ensemble.
BNB1 B2
...

Static Ensembles
 Bagging ensembles of Regression Trees (Bag+RTs)*
Study with 13 data sets from PROMISE and ISBSG
repositories.
Bag+RTs:
 Obtained the highest rank across data set in terms of Mean
Absolute Error (MAE).
 Rarely performed considerably worse (>0.1SA, SA = 1 – MAE /
MAErguess) than the best approach:
* L. Minku, X. Yao. Ensembles and Locality: Insight on Improving Software Effort Estimation. Information and
Software Technology, Special Issue on Best Papers from PROMISE 2011, 2012 (in
press), http://dx.doi.org/10.1016/j.infsof.2012.09.012.

Static Ensembles
 Bagging* ensembles of regression trees
* L. Breiman. Bagging Predictors. Machine Learning 24(2):123-140, 1996.
Training data
Ensemble
RT1 RT2 RTN...
Sample
uniformly with
replacement

Static Ensembles
 Bagging ensembles of regression trees
Functional Size
Functional Size Effort = 5376
Effort = 1086 Effort = 2798
>= 253< 253
< 151 >= 151
Regression trees:
 Estimation by analogy.
 Divide projects
according to attribute
value.
 Most impactful
attributes are in higher
levels.
 Attributes with
insignificant impact are
not used.
 E.g., REPTrees*.
* M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H. Witten.
The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1), 2009.
http://www.cs.waikato.ac.nz/ml/weka.

Static Ensembles
 Bagging ensembles of regression trees
 Weka: classifiers – meta – bagging
 classifiers – trees – REPTree

Static Ensembles
 Multiple-objective Pareto ensembles
There are different measures/metrics of performance for
evaluating SEE models.
Different measures capture different quality features of the
models.
 E.g.: MAE, standard
deviation, PRED, etc.
 There is no agreed
single measure.
 A model doing well
for a certain
measure may not do
so well for another.
Multilayer
Perceptron (MLP)
models created
using Cocomo81.

Static Ensembles
 Multi-objective Pareto ensembles*
We can view SEE as a multi-objective learning
problem.
A multi-objective approach (e.g. Multi-Objective
Evolutionary Algorithm (MOEA)) can be used to:
 Better understand the relationship among measures.
 Create ensembles that do well for a set of measures, in
particular for larger data sets (>=60).
Sample result: Pareto ensemble of MLPs (ISBSG):
* L. Minku, X. Yao. Software Effort Estimation as a Multi-objective Learning Problem. ACM Transactions on
Software Engineering and Methodology, 2012 (accepted). Author's final version:
http://www.cs.bham.ac.uk/~minkull/publications/MinkuYaoTOSEM12.pdf.

Static Ensembles
 Multi-objective Pareto ensembles
Training data
Ensemble
B1 B2 B3
Multi-objective evolutionary
algorithm creates nondominated
models with several different trade-
offs.
The model with the best performance
in terms of each particular measure
can be picked to form an ensemble
with a good trade-off.

Dynamic Adaptive Ensembles
 Companies are not
static entities – they
can change with time
(data set shift /
concept drift*).
Models need to learn new
information and adapt to
changes.
Companies can start
behaving more or less
similarly to other
companies.
* L. Minku, A. White, X. Yao. The Impact of Diversity on On-line Ensemble Learning in the Presence of Concept
Drift. IEEE Transactions on Knowledge and Data Engineering, 22(5):730-742, 2010.
Predicting effort for a single company from ISBSG based
on its projects and other companies' projects.

 Dynamic Cross-company Learning (DCL)*
Cross-company
Training Set 1
Cross-company
Training Set 1
Cross-company (CC)
m training sets with
different productivity
CC model 1 CC model m
w1 wm
...
...
...
Within-company (WC)
training data
(projects arriving with
time)
CC
model
1
CC
model
m
...
WC
model
1
w1 wm
wm+1
* L. Minku, X. Yao. Can Cross-company Data Improve Performance in Software Effort Estimation? Proceedings
of the 8th International Conference on Predictive Models in Software Engineering, p. 69-78, 2012.
http://dx.doi.org/10.1145/2365324.2365334.
• Dynamic weights control how much a
certain model contributes to predictions:
 At each time step, “loser” models
have weight multiplied by Beta.
 Models trained with “very different”
projects from the one to be predicted can
be filtered out.

 Dynamic Cross-company Learning (DCL)
DCL uses new completed projects that arrive with time.
DCL determines when CC data is useful.
DCL adapts to changes by using CC data.
Predicting effort for a single company from ISBSG based on its projects and other companies' projects.

What have we covered?
125
Organizational Issues
Data Issues
Model Issues

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