Software Analytics: Towards Software Mining that Matters

Software Analytics:
Towards Software Mining
that Matters
Tao Xie
University of Illinois at Urbana-Champaign
http://www.cs.illinois.edu/homes/taoxie/
taoxie@illinois.edu

Should I testreview my?

©A. Hassan

Software analytics is to enable software practitioners
to perform data exploration and analysis in order to
obtain insightful and actionable information for datadriven tasks around software and services.

[MALETS’11 Zhang et al.]

Software Intelligence &
Analytics for Software Development

http://people.engr.ncsu.edu/txie/publications/foser10-si.pdf
http://thomas-zimmermann.com/publications/files/buse-foser-2010.pdf

• use Data Exploration and Analysis
 Mining Software Repositories (MSR)

• for Software Practitioners
Beyond Software Developers

• obtain Insightful and Actionable info
Need get real as well

• Analytic Techniques
• Producing Impact on Practice

Look through your
software
data

©A. Hassan

Mine through
the data!

An international effort to
make software repositories actionable
http://msrconf.org

http://promisedata.org
©A. Hassan

Promise Data
Repository

Mine through
the data!

An international effort to
make software repositories actionable
http://msrconf.org

http://promisedata.org
©A. Hassan

Mining Software Repositories (MSR)
• Transforms static recordkeeping repositories to
active repositories
• Makes repository data
actionable by uncovering
hidden patterns and
trends
Field logs CVS/SVN
Bugzilla

Mailinglist

Crashes
11

©A. Hassan

Source Control
CVS/SVN

Bugzilla

Mailing
lists
12

Source Control
CVS/SVN

Bugzilla

Mailing
lists

Crash
Repos

Field
Logs
12

Source Control
CVS/SVN

Bugzilla

Mailing
lists

Historical Repositories

Crash
Repos

Field
Logs
12

Source Control
CVS/SVN

Bugzilla

Mailing
lists


Crash
Repos

Field
Logs

Runtime Repos
12

Sourceforge
GoogleCode

Code Repos

Source Control
CVS/SVN

Bugzilla

Mailing
lists


Crash
Repos

Field
Logs

Runtime Repos
12

MSR researchers
analyze and cross-link repositories

Bugzilla

Mailinglist

CVS/SVN

Crashes

©A. Hassan

MSR researchers
discussions

Buggy change &
Fixing change

fixed
bug
Bugzilla

Mailinglist

CVS/SVN

Field
crashes

Crashes

©A. Hassan

MSR researchers
discussions

Buggy change &
Fixing change

fixed
bug
Bugzilla

Mailinglist

CVS/SVN

Field
crashes

Crashes

New Bug Report

©A. Hassan

MSR researchers
discussions

Buggy change &
Fixing change

fixed
bug
Bugzilla

Mailinglist

CVS/SVN

Field
crashes

Crashes

New Bug Report
Estimate fix effort
Mark duplicates
Suggest experts and fix!
©A. Hassan

We continue to help
practitioners (esp. developers)

©A. Hassan

Detection and Management of
Code Clones

©A. Hassan

Support
Logs

Source
Code

©A. Hassan

• use Data Exploration and Analysis
 Mining Software Repositories (MSR)

• for Software Practitioners
Beyond Software Developers

• obtain Insightful and Actionable info
Need get real as well

• Analytic Techniques
• Case Studies

Predicting Bugs
• Studies have shown that most complexity metrics
correlate well with LOC!
– Graves et al. 2000 on commercial systems
– Herraiz et al. 2007 on open source systems

• Noteworthy findings:
– Previous bugs are good predictors of future bugs
– The more a file changes, the more likely it will have
bugs in it
– Recent changes affect more the bug potential of a file
over older changes (weighted time damp models)
– Number of developers is of little help in predicting bugs
– Hard to generalize bug predictors across projects
unless in similar domains [Nagappan, Ball et al. 2006]
23

Using Imports in Eclipse to Predict Bugs
71% of files that import compiler packages,
had to be fixed later on.
import org.eclipse.jdt.internal.compiler.lookup.*;
import org.eclipse.jdt.internal.compiler.*;
import org.eclipse.jdt.internal.compiler.ast.*;
import org.eclipse.jdt.internal.compiler.util.*;
...
import org.eclipse.pde.core.*;
import org.eclipse.jface.wizard.*;
import org.eclipse.ui.*;

14% of all files that import ui packages,
had to be fixed later on.
[Schröter et al. 06]
24

Don’t program on Fridays ;-)

Percentage of bug-introducing changes for eclipse

[Zimmermann et al. 05]
25

Failure is a 4-letter Word

[PROMISE’11 Zeller et al.]
26

Actionable Alone is not Enough!

[PROMISE’11 Zeller et al.]
27

Who produces more buggy code?

©A. Hassan

Analytic Techniques in SE
• Association rules and frequent patterns
• Classification
• Clustering
• Text mining/Natural language processing
• Visualization
More details are at
• https://sites.google.com/site/xsoftanalytics/
30

Solution-Driven Problem-Driven
Where can I apply X miner?
Basic
mining
algorithms
E.g., association rule,
frequent itemset mining…

Advanced
mining
algorithms
E.g., frequent partial order
mining [ESEC/FSE 07]

What patterns do
we really need?
New/adapted
mining
algorithms

E.g., [ICSE 09], [ASE 09]

49

Mining  Searching + Mining
Traditional approaches
Code repositories

1

2

mining

patterns

Eclipse, Linux, …

50

Code repositories

1
Eclipse, Linux, …

2

mining

patterns

Often lack sufficient relevant data points (Eg. API call sites)

51

Code repositories

1

patterns

mining

2

Eclipse, Linux, …

Often lack sufficient relevant data points (Eg. API call sites)

Our new approaches
Code repositories

1

2

…

Open source code
on the web

N

searching

mining

patterns

Code search engine
e.g.,
53
53

52

Existing approaches produce high % of false positives
One major observation:
Programmers often write code in different ways for
achieving the same task
Some ways are more frequent than others

S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.

Frequent
ways

Infrequent
ways


Frequent
ways

Infrequent
ways

mine patterns

Mined Patterns

Frequent
ways
mine patterns

Infrequent
ways
detect violations

Mined Patterns

Example: java.util.Iterator.next()
Java.util.Iterator.next() throws NoSuchElementException when invoked on a list
without any elements
Code Sample 1

PrintEntries1(ArrayList<string>
entries)

{
…
Iterator it = entries.iterator();
if(it.hasNext()) {
string last = (string) it.next();
}
…
}

Code Sample 2

entries)
{
…
if(entries.size() > 0) {
}
…
}
58


Code Sample 1
entries)
{
…
if(it.hasNext()) {
}
…
}

Code Sample 2
entries)
{
…
}
…
}

59


Code Sample 1
entries)
{
…
if(it.hasNext()) {
}
…
}

Code Sample 2
entries)
{
…
}
…
}

1243 code
examples
60


Code Sample 1

Code Sample 2

entries)
{
…
if(it.hasNext()) {
}
…
}

entries)
{
…
}
…
}

Sample 1 (1218 / 1243)
1243 code
examples
61


Code Sample 1

Code Sample 2

entries)
{
…
if(it.hasNext()) {
}
…
}

entries)
{
…
}
…
}
Sample 2 (6/1243)

Sample 1 (1218 / 1243)
1243 code
examples
62


Code Sample 1

Code Sample 2

entries)
{
…
if(it.hasNext()) {
}
…
}

entries)
{
…
}
…
}
Sample 2 (6/1243)

Sample 1 (1218 / 1243)
1243 code
examples
Mined Pattern from existing approaches:
“boolean check on return of Iterator.hasNext before Iterator.next”

63


Code Sample 1

Code Sample 2

entries)
{
…
if(it.hasNext()) {
}
…
}

entries)
{
…
}
…
}
Sample 2 (6/1243)

Sample 1 (1218 / 1243)
1243 code
examples

64


Code Sample 1

Code Sample 2

entries)
{
…
if(it.hasNext()) {
}
…
}

entries)
{
…
}
…
}
Sample 2 (6/1243)

Sample 1 (1218 / 1243)
1243 code
examples

65


Code Sample 1
entries)
{
…
if(it.hasNext()) {
}
…
}

Code Sample 2
entries)
{
…
}
…
}


Code Sample 1
entries)
{
…
if(it.hasNext()) {
}
…
}

Code Sample 2
entries)
{
…
}
…
}

 Require more general patterns (alternative patterns): P1 or P2


Code Sample 1
entries)
{
…
if(it.hasNext()) {
}
…
}

Code Sample 2
entries)
{
…
}
…
}

P1 : boolean check on return of Iterator.hasNext before Iterator.next


Code Sample 1
entries)
{
…
if(it.hasNext()) {
}
…
}

Code Sample 2
entries)
{
…
}
…
}

P2 : boolean check on return of ArrayList.size before Iterator.next


Code Sample 1
entries)
{
…
if(it.hasNext()) {
}
…
}

Code Sample 2
entries)
{
…
}
…
}

P2 : boolean check on return of ArrayList.size before Iterator.next

 Cannot be mined by existing approaches, since alternative P2 is
infrequent


Our Solution: ImMiner Algorithm
[ASE 09]

 Mines alternative patterns of the form P1 or P2
 Based on the observation that infrequent alternatives such as P2
are frequent among code examples that do not support P1

72


[ASE 09]

1243 code examples

Sample 2 (6/1243)

Sample 1 (1218 / 1243)

73


[ASE 09]

1243 code examples

Sample 2 (6/1243)

Sample 1 (1218 / 1243)

P2 is infrequent among
entire 1243 code examples
74


[ASE 09]

1243 code examples

Sample 2 (6/1243)

Sample 1 (1218 / 1243)

P2 is frequent among code
examples not supporting P1
75


Alternative Patterns
 ImMiner mines three kinds of alternative
patterns of the general form “P1 or P2”
Balanced: all alternatives (both P1 and P2) are frequent
Imbalanced: some alternatives (P1) are frequent and
others are infrequent (P2). Represented as “P1 or P^2”
Single: only one alternative
76


ImMiner Algorithm
 Uses frequent-itemset mining [Burdick et al. ICDE 01]
iteratively
 An input database with the following APIs
for Iterator.next()
Input database

Mapping of IDs to APIs


ImMiner Algorithm: Frequent Alternatives
Input database

Frequent itemset
mining
(min_sup 0.5)

Frequent item: 1
P1: boolean-check on the return of Iterator.hasNext()
before Iterator.next()

ImMiner: Infrequent Alternatives of P1

79

 Split input database into two databases: Positive and Negative

80

Positive database (PSD)

81


Negative database (NSD)

82



 Mine patterns that are frequent in NSD and are infrequent in PSD
 Reason: Only such patterns serve as alternatives for P1

83



 Mine patterns that are frequent in NSD and are infrequent in PSD
 Reason: Only such patterns serve as alternatives for P1
 Alternative Pattern : P2 “const check on the return of ArrayList.size()
before Iterator.next()”
 Alattin applies ImMiner algorithm to detect neglected conditions
84

Neglected Conditions
 Neglected conditions refer to

 Missing conditions that check the arguments
or receiver of the API call before the API call
 Missing conditions that check the return or
receiver of the API call after the API call

 One primary reason for many fatal issues
 security or buffer-overflow vulnerabilities
[Chang et al. ISSTA 07]

Machine Learning that Matters

[ICML’12 Wagstaﬀ]
http://arxiv.org/ftp/arxiv/papers/1206/1206.4656.pdf

• Hyper-Focus on Benchmark Data Sets
• Hyper-Focus on Abstract Metrics
• Lack of Follow-Through


• Meaningful Evaluation Methods
• Involvement of the World Outside ML
• Eyes on the Prize


MSRA Software Analytics Group
Utilize data-driven approach to help create highly performing, user
friendly, and efficiently developed and operated software and services.

Contact: Dongmei Zhang (dongmeiz@microsoft.com)
http://research.microsoft.com/groups/sa/


Software
Users

Software
Development

Process

Software
Systems

Research Topics

Information Visualization
Software
Users

Software
Development

Process

Analysis Algorithms
Software
Systems
Large-scale Computing

Research Topics

Technology Pillars


Information Visualization
Software
Users

Software

Vertical

Development

Process

Analysis Algorithms
Horizontal

Software
Systems

Large-scale Computing

Research Topics

Technology Pillars


Software Analytics in Practice

Adoption Challenges for
Software Analytics

Must show value
before data quality
improves

Correlation vs.
Causation

OSDI 2008 26% vs. xSE ?%
Developers, Programmers, Architects
Among All Attendees


OSDI 2008 26% vs. xSE ?%
Developers, Programmers, Architects
Among All Attendees

MSR 11 Keynote

ICSE 09 Keynote


MSR 12 Keynote

ICSM 11 Keynote

SCAM 12 Keynote

"Are Automated Debugging [Research]
Techniques Actually Helping Programmers?"
• 50 years of automated debugging research
– N papers  only 5 evaluated with actual programmers

“

”

[ISSTA11 Parnin&Orso]

Are Regression Testing [Research] Techniques
Actually Helping Industry?
• Likely most studied testing problems
– N papers

“

”
[STVR11 Yoo&Harman]

Are [Some] Failure-Proneness Prediction
[Research] Techniques Actually Helping?
• Empirical software engineering (on prediction)
– N papers

”

[PROMISE11 Zeller et al.]

A Researcher's Observation in HCI
Research Community
• “The reviewers simply do not value the
difficulty of building real systems and how
hard controlled studies are to run on real
systems for real tasks. This is in contrast
with how easy it is to build new interaction
techniques and then to run tight,
controlled studies on these new
techniques with small, artificial tasks”
“I give up on CHI/UIST” by James Landay
http://dubfuture.blogspot.com/2009/11/i-give-up-on-chiuist.html

Source©J. Landay

Research Community
• “This attitude is a joke and it offers
researchers no incentive to do systems
work. Why should they? Why should we
put 3-4 person years into every CHI
publication? Instead we can do 8 weeks of
work on an idea piece or create a new
interaction technique and test it tightly in
8-12 weeks and get a full CHI paper.”

Source©J. Landay

Research Community
• “When will this community wake up and
understand that they are going to run out any
work on creating new systems (rather than
small pieces of systems) and cede that
important endeavor to industry?”
• “We are our own worst enemies. I think we
have been blinded by the perception that "true
scientific" research is only found in controlled
experiments and nice statistics.”

Source©J. Landay

Research Community
• “When will this community wake up and
understand that they are going to run out any
work on creating new systems (rather than
Does
small pieces of systems) and cede that
our research community
important endeavor to industry?”
have similar issues??
• “We are our own worst enemies. I think we
have been blinded by the perception that "true
scientific" research is only found in controlled
experiments and nice statistics.”

Source©J. Landay

MS Academic Search: “Pointer
Analysis”

“Pointer Analysis: Haven’t We Solved
This Problem Yet?” [Hind PASTE’01]
“During the past 21 years, over 75 papers
and 9 Ph.D. theses have been published on
pointer analysis. Given the tones of work on
this topic one may wonder, “Haven't we
solved this problem yet?'' With input from
many researchers in the field, this paper
describes issues related to pointer analysis
and remaining open problems.”
Michael Hind. Pointer analysis: haven't we solved this problem yet?. In Proc.
ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and
Engineering (PASTE 2001)
Source©M. Hind

58

“Pointer Analysis: Haven’t We Solved
This Problem Yet?” [Hind PASTE’01]
Section 4.3 Designing an Analysis for a Client’s Needs

“Barbara Ryder expands on this topic: “… We can all
write an unbounded number of papers that compare
different pointer analysis approximations in the
abstract. However, this does not accomplish the key
goal, which is to design and engineer pointer
analyses that are useful for solving real

software problems for realistic programs.”
Michael Hind. Pointer analysis: haven't we solved this problem yet?. In Proc.
ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and
Engineering (PASTE 2001)
Source©M. Hind&B. Ryder

59

MS Academic Search: “Clone
Detection”

MS Academic Search: “Clone
Detection”
Typically focus/evaluate on
intermediate steps (e.g., clone
detection) instead of ultimate
tasks (e.g., bug detection or
refactoring), even when the
field already grows mature
with n years of efforts on
intermediate steps

Some Success Stories of Applying
Clone Detection [Focus on Ultimate Tasks]
MSRA
XIAO
Yingnong Dang, Dongmei Zhang, Song
Ge, Chengyun Chu, Yingjun Qiu, and
Tao Xie. XIAO: Tuning Code Clones at
Hands of Engineers in Practice. In
Proc. ACSAC 2012,
http://research.microsoft.com/en-us/groups/sa/

Zhenmin Li, Shan Lu, Suvda Myagmar,
and Yuanyuan Zhou. CP-Miner: a tool
for finding copy-paste and related
bugs in operating system code. In
Proc. OSDI 2004.
http://patterninsight.com/

http://www.blackducksoftware.com/

61

Suggested Actions  Tech Adoption
•
•
•
•

Get research problems from real practice
Get feedback from real practice
Collaborate across disciplines
Collaborate with industry

•Software Analytics
Data Exploration and Analysis
For Software Practitioners
Obtain Insightful and Actionable info
With Analytic Techniques

• Producing Impact on Practice

Acknowledgments
• Microsoft Research Asia Software Analytics
Group
• Ahmed Hassan, Lin Tan, Jian Pei
• Many other colleagues

64

Software Analytics: Towards Software Mining that Matters

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

Similar to Software Analytics: Towards Software Mining that Matters

Similar to Software Analytics: Towards Software Mining that Matters (20)

More from Tao Xie

More from Tao Xie (17)

Recently uploaded

Recently uploaded (20)

Software Analytics: Towards Software Mining that Matters