software analytics: towards software mining that matters
DESCRIPTION
A keynote talk given at the 2013 second International Workshop on Software Mining: http://lamda.nju.edu.cn/conf/softwaremining13/speaker.htmlTRANSCRIPT
Software Analytics:
Towards Software Mining
that Matters Tao Xie
University of Illinois at Urbana-Champaign http://www.cs.illinois.edu/homes/taoxie/
Should I test\review my?
©A. Hassan
©A. Hassan
©A. Hassan
©A. Hassan
Software analytics is to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for data-driven 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
Look through your software data
©A. Hassan
Mine through the data!
http://msrconf.org
An international effort to make software repositories actionable
http://promisedata.org ©A. Hassan
Mine through the data!
http://msrconf.org
An international effort to make software repositories actionable
http://promisedata.org ©A. Hassan
Mine through the data!
http://msrconf.org
An international effort to make software repositories actionable
http://promisedata.org
Promise Data Repository
©A. Hassan
Mining Software Repositories (MSR)
• Transforms static record-keeping repositories to active repositories
• Makes repository data actionable by uncovering hidden patterns and trends
11
Mailinglist Bugzilla Crashes
Field logs CVS/SVN
©A. Hassan
Mining Software Repositories (MSR)
• Transforms static record-keeping repositories to active repositories
• Makes repository data actionable by uncovering hidden patterns and trends
11
Mailinglist Bugzilla Crashes
Field logs CVS/SVN
©A. Hassan
12
Source Control CVS/SVN
Bugzilla Mailing lists
©A. Hassan
12
Field Logs
Source Control CVS/SVN
Bugzilla Mailing lists
Crash Repos
©A. Hassan
12
Field Logs
Source Control CVS/SVN
Bugzilla Mailing lists
Crash Repos
Historical Repositories ©A. Hassan
12
Field Logs
Source Control CVS/SVN
Bugzilla Mailing lists
Crash Repos
Historical Repositories Runtime Repos ©A. Hassan
12
Field Logs
Source Control CVS/SVN
Bugzilla Mailing lists
Crash Repos
Historical Repositories Runtime Repos
Code Repos
Sourceforge GoogleCode
©A. Hassan
Bugzilla CVS/SVN Mailinglist Crashes
MSR researchers analyze and cross-link repositories
©A. Hassan
Bugzilla CVS/SVN Mailinglist Crashes
MSR researchers analyze and cross-link repositories
fixed bug
discussions Buggy change &
Fixing change Field crashes
©A. Hassan
Bugzilla CVS/SVN Mailinglist Crashes
MSR researchers analyze and cross-link repositories
fixed bug
discussions Buggy change &
Fixing change Field crashes
New Bug Report
©A. Hassan
Bugzilla CVS/SVN Mailinglist Crashes
MSR researchers analyze and cross-link repositories
fixed bug
discussions Buggy change &
Fixing change Field crashes
Estimate fix effort Mark duplicates
Suggest experts and fix!
New Bug Report
©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 • Producing Impact on Practice
We continue to help practitioners (esp. developers)
©A. Hassan
©A. Hassan
©A. Hassan
©A. Hassan
©A. Hassan
©A. Hassan
©A. Hassan
©A. Hassan
©A. Hassan
©A. Hassan
Detection and Management of Code Clones
©A. Hassan
Support Logs
Source Code
©A. Hassan
©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
24
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.
71% of files that import compiler packages, had to be fixed later on.
[Schröter et al. 06]
25
Percentage of bug-introducing changes for eclipse
Don’t program on Fridays ;-)
[Zimmermann et al. 05]
26
Failure is a 4-letter Word
[PROMISE’11 Zeller et al.]
27
Actionable Alone is not Enough!
[PROMISE’11 Zeller et al.]
Who produces more buggy code?
©A. Hassan
Who produces more buggy 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 • Producing Impact on Practice
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
49
Basic mining
algorithms
Solution-Driven Problem-Driven
Advanced mining
algorithms New/adapted
mining algorithms
Where can I apply X miner? What patterns do we really need?
E.g., frequent partial order mining [ESEC/FSE 07]
E.g., association rule, frequent itemset mining… E.g., [ICSE 09], [ASE 09]
50
1 2 mining patterns
Eclipse, Linux, …
Traditional approaches
Code repositories
Mining Searching + Mining
51
1 2 mining patterns
Eclipse, Linux, …
Traditional approaches
Often lack sufficient relevant data points (Eg. API call sites)
Code repositories
Mining Searching + Mining
52
53 53
Code repositories
1 2 N …
1 2 mining patterns
searching mining patterns
Code search engine e.g., Open source code
on the web
Eclipse, Linux, …
Traditional approaches
Our new approaches
Often lack sufficient relevant data points (Eg. API call sites)
Code repositories
Mining Searching + Mining
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.
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
Frequent ways
Infrequent ways
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
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
Frequent ways
Infrequent ways
Mined Patterns
mine patterns
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
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
Frequent ways
Infrequent ways
Mined Patterns
mine patterns detect violations
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
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
Frequent ways
Infrequent ways
Mined Patterns
mine patterns detect violations
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
58
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
Java.util.Iterator.next() throws NoSuchElementException when invoked on a list without any elements
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
59
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
60
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
1243 code examples
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
61
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
1243 code examples
Sample 1 (1218 / 1243)
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
62
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
1243 code examples
Sample 1 (1218 / 1243)
Sample 2 (6/1243)
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
63
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
1243 code examples
Sample 1 (1218 / 1243)
Sample 2 (6/1243)
Mined Pattern from existing approaches:
“boolean check on return of Iterator.hasNext before Iterator.next” S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
64
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
1243 code examples
Sample 1 (1218 / 1243)
Sample 2 (6/1243)
Mined Pattern from existing approaches:
“boolean check on return of Iterator.hasNext before Iterator.next” S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
65
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
1243 code examples
Sample 1 (1218 / 1243)
Sample 2 (6/1243)
Mined Pattern from existing approaches:
“boolean check on return of Iterator.hasNext before Iterator.next” S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
Example: java.util.Iterator.next()
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
Example: java.util.Iterator.next()
Require more general patterns (alternative patterns): P1 or P2
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
Example: java.util.Iterator.next()
Require more general patterns (alternative patterns): P1 or P2
P1 : boolean check on return of Iterator.hasNext before Iterator.next
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
Example: java.util.Iterator.next()
Require more general patterns (alternative patterns): P1 or P2
P1 : boolean check on return of Iterator.hasNext before Iterator.next
P2 : boolean check on return of ArrayList.size before Iterator.next
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
Example: java.util.Iterator.next()
Require more general patterns (alternative patterns): P1 or P2
P1 : boolean check on return of Iterator.hasNext before Iterator.next
P2 : boolean check on return of ArrayList.size before Iterator.next Cannot be mined by existing approaches, since alternative P2 is infrequent
PrintEntries1(ArrayList<string> entries) { … Iterator it = entries.iterator(); if(it.hasNext()) { string last = (string) it.next(); } … }
Code Sample 1
PrintEntries2(ArrayList<string> entries) { … if(entries.size() > 0) { Iterator it = entries.iterator(); string last = (string) it.next(); } … }
Code Sample 2
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
72
Our Solution: ImMiner Algorithm
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
[ASE 09]
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
73
Our Solution: ImMiner Algorithm
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
1243 code examples
Sample 1 (1218 / 1243)
Sample 2 (6/1243)
[ASE 09]
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
74
Our Solution: ImMiner Algorithm
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
1243 code examples
Sample 1 (1218 / 1243)
Sample 2 (6/1243)
P2 is infrequent among entire 1243 code examples
[ASE 09]
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
75
Our Solution: ImMiner Algorithm
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
1243 code examples
Sample 1 (1218 / 1243)
Sample 2 (6/1243)
P2 is frequent among code examples not supporting P1
[ASE 09]
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
76
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
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
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
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
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() S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
79
ImMiner: Infrequent Alternatives of P1
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
80
ImMiner: Infrequent Alternatives of P1 Split input database into two databases: Positive and Negative
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
81
ImMiner: Infrequent Alternatives of P1
Positive database (PSD)
Split input database into two databases: Positive and Negative
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
82
ImMiner: Infrequent Alternatives of P1
Positive database (PSD)
Negative database (NSD)
Split input database into two databases: Positive and Negative
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
83
ImMiner: Infrequent Alternatives of P1
Positive database (PSD)
Negative database (NSD)
Split input database into two databases: Positive and Negative
Mine patterns that are frequent in NSD and are infrequent in PSD Reason: Only such patterns serve as alternatives for P1
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
84
ImMiner: Infrequent Alternatives of P1
Positive database (PSD)
Negative database (NSD)
Split input database into two databases: Positive and Negative
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
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
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]
S.Thummalapenta and T. Xie. Alattin: Mining Alternative Patterns for Detecting Neglected Conditions. ASE 2009.
• 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
Machine Learning that Matters
http://arxiv.org/ftp/arxiv/papers/1206/1206.4656.pdf
[ICML’12 Wagstaff]
• Hyper-Focus on Benchmark Data Sets
• Hyper-Focus on Abstract Metrics
• Lack of Follow-Through
http://arxiv.org/ftp/arxiv/papers/1206/1206.4656.pdf
[ICML’12 Wagstaff]
• Meaningful Evaluation Methods
• Involvement of the World Outside ML
• Eyes on the Prize
http://arxiv.org/ftp/arxiv/papers/1206/1206.4656.pdf
[ICML’12 Wagstaff]
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 ([email protected])
http://research.microsoft.com/groups/sa/
MSRA Software Analytics Group
Utilize data-driven approach to help create highly performing, user friendly, and efficiently developed and operated software and services.
Software Development
Process
Software Systems
Software Users
Research Topics
Contact: Dongmei Zhang ([email protected])
http://research.microsoft.com/groups/sa/
MSRA Software Analytics Group
Utilize data-driven approach to help create highly performing, user friendly, and efficiently developed and operated software and services.
Software Development
Process
Software Systems
Software Users
Information Visualization
Analysis Algorithms
Large-scale Computing
Research Topics Technology Pillars
Contact: Dongmei Zhang ([email protected])
http://research.microsoft.com/groups/sa/
MSRA Software Analytics Group
Utilize data-driven approach to help create highly performing, user friendly, and efficiently developed and operated software and services.
Software Development
Process
Software Systems
Software Users
Information Visualization
Analysis Algorithms
Large-scale Computing
Research Topics Technology Pillars
Contact: Dongmei Zhang ([email protected])
http://research.microsoft.com/groups/sa/
MSRA Software Analytics Group
Utilize data-driven approach to help create highly performing, user friendly, and efficiently developed and operated software and services.
Software Development
Process
Software Systems
Software Users
Information Visualization
Analysis Algorithms
Large-scale Computing
Research Topics Technology Pillars
Vertical
Horizontal
Contact: Dongmei Zhang ([email protected])
http://research.microsoft.com/groups/sa/
Software Analytics in Practice
Adoption Challenges for Software Analytics
Must show value before data quality
improves
Correlation vs. Causation
ICSE Papers: Industry vs. Academia
Source© Carlo Ghezzi
ICSE Papers: Industry vs. Academia
Source© Carlo Ghezzi
OSDI 2008 26% vs. xSE ?% Developers, Programmers, Architects Among All Attendees
ICSE Papers: Industry vs. Academia
Source© Carlo Ghezzi
OSDI 2008 26% vs. xSE ?% Developers, Programmers, Architects Among All Attendees
ICSM 11 Keynote ICSE 09 Keynote
MSR 12 Keynote MSR 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
• “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.”
A Researcher's Observation in HCI Research Community
“I give up on CHI/UIST” by James Landay http://dubfuture.blogspot.com/2009/11/i-give-up-on-chiuist.html Source©J. Landay
A Researcher's Observation in HCI 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.”
“I give up on CHI/UIST” by James Landay http://dubfuture.blogspot.com/2009/11/i-give-up-on-chiuist.html Source©J. Landay
A Researcher's Observation in HCI 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.”
Does our research community
have similar issues??
“I give up on CHI/UIST” by James Landay http://dubfuture.blogspot.com/2009/11/i-give-up-on-chiuist.html Source©J. Landay
MS Academic Search: “Pointer Analysis”
“Pointer Analysis: Haven’t We Solved This Problem Yet?” [Hind PASTE’01]
58
“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
“Pointer Analysis: Haven’t We Solved This Problem Yet?” [Hind PASTE’01]
59
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
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]
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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.
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://patterninsight.com/
http://www.blackducksoftware.com/
http://research.microsoft.com/en-us/groups/sa/
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
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Q&A
•Software Analytics Data Exploration and Analysis For Software Practitioners Obtain Insightful and Actionable info With Analytic Techniques
• Producing Impact on Practice