Empirical Evaluations in Software Engineering Research: A Personal Perspective
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1 Empirical Evaluations in Software Engineering Research: A Personal Perspective Ahmed E. Hassan Canada Research Chair in Software Analytics Blackberry Ultra Large Scale Systems Chair School of Computing, Queen’s University Kingston, Canada
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Transcript of Empirical Evaluations in Software Engineering Research: A Personal Perspective
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Empirical Evaluations in Software Engineering Research: A Personal Perspective
Ahmed E. Hassan Canada Research Chair in Software Analytics Blackberry Ultra Large Scale Systems Chair
School of Computing, Queen’s University Kingston, Canada
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Today’s goal:
Give concrete ideas Inspire change
Kingston Home of the 1,000 Islands
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Kingston Home of the 1,000 Islands
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Intelligence throughout the lifetime of a software system
from inception to production http://sail.cs.queensu.ca
1851
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SAIL’s new home: A 5,000 sq. ft. historical building
1851
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SAIL’s new home: A 5,000 sq. ft. historical building
1851
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SAIL’s new home: A 5,000 sq. ft. historical building
~15 years of Mining Software Repositories
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Early MSR Days
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Early Day Miner
MSR Today: Easy Peasy!
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Data track GHTorrent
Lots of data!
Then
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Now
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Thanks to a large scale community
effort!
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Yet low practitioner interest!!
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Even though our friendly reviewers are pleased!
Why?!?
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Why?!?
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We continue to raise the bar for studies
Empirical evaluations continues to mature and evolve
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Empirical evaluations continues to mature and evolve
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No validation
Single System
Multiple Systems
GitHub Scale
+ Practitioner Feedback
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No validation
Single System
Multiple Systems
GitHub Scale
+ Practitioner Feedback
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No validation
Single System
Multiple Systems
GitHub Scale
+ Practitioner Feedback
Are things this bad? What can we do to prevent this?
We are killing
research areas
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Software Visualization
Program Understand.
We are following industry instead of leading!!
Web Apps (e.g, AJAX) Data Analytics
Scripting
Infrastructure Mobile Apps IoT
We are doing this to ourselves!!
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Practitioner buy-in
More studies (N+1)
Replication data
Not Soft. Eng!
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Practitioner buy-in
More studies (N+1)
Replication data
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Practitioner buy-in
More studies (N+1)
Replication data
Not Soft. Eng!
Not Software Enginering
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Not Software Enginering
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Mobile AppsLogs
Build SystemsLoad Testing
NoSQL Systems
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"I found the overall presentation disjointed…. This needs to focus more
on the IR issues and less on web analysis.” SIGIR Rejection 98
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Practitioner buy-in
Replication data
Not Soft. Eng!
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Practitioner buy-in
More studies (N+1)
Replication data
Not Soft. Eng!
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We can early-detect
brain cancer!!!
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How about skin/lung cancer?!!!
The “N+1” Critique encourages
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Case Study Fishing
Shallow Analysis
Generalization of approaches instead of generalization of results
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Encourage Meta Analysis
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Encourage Meta Analysis
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Encourage Meta Analysis
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Encourage Meta Analysis
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N=15..241
Total N= 2,659
Encourage Meta Analysis
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Encourage Meta Analysis
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1972
More is less!
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More studies (N+1)
Replication data
Not Soft. Eng!
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Practitioner buy-in
More studies (N+1)
Replication data
Not Soft. Eng!
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Smoking kills!
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What do smokers think?!
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54.9% of the time patients did not receive the recommended care for their condition
[New England Journal of Medicine 2003]
Less than half of developers have
a CS degree [stack overflow survey 2016]
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“A couple billion lines of code later: static checking in the real world” Dawson Engler
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“A couple billion lines of code later: static checking in the real world” Dawson Engler
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“A couple billion lines of code later: static checking in the real world” Dawson Engler
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Do practitioners agree with
each other?
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Practitioners do not agree with each other
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Presentation
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Survey (93 stakeholders)
Practitioners do not agree with each other
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Presentation
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Survey (93 stakeholders)
Semi-structured interviews
(15 Key engineers)
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Interviewee’s list
Practitioners do not agree with each other
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Presentation
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Survey (93 stakeholders)
Semi-structured interviews
(15 Key engineers)
Validation survey Interviews
(25 Senior engineers)
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Interviewee’s list
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Implications
Practitioners do not agree with each other
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Presentation
–---------–-------–---------–-------
–---------–-------–---------–-------
–---------–-------–---------–-------
Survey (93 stakeholders)
Semi-structured interviews
(15 Key engineers)
Validation survey Interviews
(25 Senior engineers)
–---------–-------–---------–-------
–---------–-------–---------–-------
–---------–-------–---------–-------
–---------–-------–---------–-------–---------–-------–---------–-------
Interviewee’s list
–---------–-------–---------–-------–---------–-------–---------–-------–---------–-------–---------–-------
Implications
–---------–-------–---------–-------–---------–-------–---------–-------–---------–-------–---------–-------
Verified implications
Practitioners do not agree with each other
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Presentation
–---------–-------–---------–-------
–---------–-------–---------–-------
–---------–-------–---------–-------
Survey (93 stakeholders)
Semi-structured interviews
(15 Key engineers)
Validation survey Interviews
(25 Senior engineers)
–---------–-------–---------–-------
–---------–-------–---------–-------
–---------–-------–---------–-------
–---------–-------–---------–-------–---------–-------–---------–-------
Interviewee’s list
–---------–-------–---------–-------–---------–-------–---------–-------–---------–-------–---------–-------
Implications
–---------–-------–---------–-------–---------–-------–---------–-------–---------–-------–---------–-------
Verified implications
As low as 68% agreement
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even within the same system
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Practitioner buy-in
More studies (N+1)
Not Soft. Eng!
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Practitioner buy-in
More studies (N+1)
Replication data
Not Soft. Eng!
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Replication data is not sufficient!
MSR Today: Easy Peasy!
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Limited knowledge of machine learning is a serious risk
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A Simple Example
A Simple Example
Logistic Regression
Empirical Hypothesis Testing (is complex code more risky?)
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Bugs ~ f(LOC, CC_max)
NOT about predicting bugs
Including correlated metrics
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Model M1: Bugs ~ f(CC_max, CC_avg, PAR_max, FOUT_max) Model M2: Bugs ~ f(CC_avg, CC_max, PAR_max, FOUT_max)
Including correlated metrics
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Model M1: Bugs ~ f(CC_max, CC_avg, PAR_max, FOUT_max) Model M2: Bugs ~ f(CC_avg, CC_max, PAR_max, FOUT_max)
Including correlated metrics
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Model M1: Bugs ~ f(CC_max, CC_avg, PAR_max, FOUT_max) Model M2: Bugs ~ f(CC_avg, CC_max, PAR_max, FOUT_max)
Including correlated metrics
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Model M1: Bugs ~ f(CC_max, CC_avg, PAR_max, FOUT_max) Model M2: Bugs ~ f(CC_avg, CC_max, PAR_max, FOUT_max)
60% of 2000-2011 studies do not handle correlated metrics [Shihab 2012]
Using F-measure in studies
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Threshold = 0.2 Threshold = 0.5 Threshold = 0.8
Resampling imbalanced data
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Resampling imbalanced data
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Resampling imbalanced data
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Resampling imbalanced data
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Variable importance: Original vs under sampled data
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Variable importance: Original vs under sampled data
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Variable importance: Original vs under sampled data
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Not including control metrics
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Model M1: Bugs ~ f( , CC_max, PAR_max, FOUT_max) Model M2: Bugs ~ f(TLOC, CC_max, PAR_max, FOUT_max)
Not including control metrics
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Model M1: Bugs ~ f( , CC_max, PAR_max, FOUT_max) Model M2: Bugs ~ f(TLOC, CC_max, PAR_max, FOUT_max)
Not including control metrics
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Model M1: Bugs ~ f( , CC_max, PAR_max, FOUT_max) Model M2: Bugs ~ f(TLOC, CC_max, PAR_max, FOUT_max)
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Unseen100
out of sample bootstrap
10-fold cross validation
10x10 Cross Validation
Using 10 fold cross validation
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Unseen25
out of sample bootstrap
100 out of sample
bootstrap
10-fold cross validation
10x10 cross validation
Using 10 fold cross validation
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Unseen25
out of sample bootstrap
100 out of sample
bootstrap
10-fold cross validation
10x10 cross validation
Using 10 fold cross validation
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Unseen25
out of sample bootstrap
100 out of sample
bootstrap
10-fold cross validation
10x10 cross validation
Using 10 fold cross validation
Using default ANOVA
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Model M1: Bugs ~ f( TLOC, ….. ) Model M2: Bugs ~ f( ……,TLOC )
Using default ANOVA
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Model M1: Bugs ~ f( TLOC, ….. ) Model M2: Bugs ~ f( ……,TLOC )
Using default ANOVA
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Model M1: Bugs ~ f( TLOC, ….. ) Model M2: Bugs ~ f( ……,TLOC )
Pitfalls in Analytical Modelling
Pitfalls in Analytical Modelling
Joint work with Kla Tantihamthavorn
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Pitfalls in Analytical Modelling
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Pitfalls in Analytical Modelling
Open Scripts
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Open Scripts
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Encourages community mentorship
Open Scripts
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Encourages community mentorship
Works even for industrial data
Open Scripts
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Encourages community mentorship
Works even for industrial data
Ensures research transparency
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N
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Where do we go from
here?
• Trailblazing research should be encouraged and defended
• Inaccurate analysis is a serious and growing threat
• Deeper analysis is more valuable than large scale studies
• Generalization is not feasible nor desired by practitioners
• Industry is a partner not an idol
Parting thoughts
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http://sail.cs.queensu.ca
Think Different!
