The thesis by Thomas Rausch investigates build failure prediction in continuous integration (CI) workflows through an empirical study of 14 open-source projects. It identifies key errors causing build failures, factors influencing build outcomes, and demonstrates the feasibility of predicting failures using machine learning algorithms. The study reveals that certain process and change metrics significantly affect build outcomes, and suggests future improvements in prediction methods and CI tool integration.

1. Build Failure Prediction in
Continuous Integration Workflows
Master’s Thesis Presentation
Software Engineering & Internet Computing
Thomas Rausch
Advisor: Stefan Schulte
Co-Advisor: Waldemar Hummer

2. I
Introduction

3. Thomas Rausch 3
Build
t
Build
Code Product

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Continuous Integration (CI)
●
Build at every change
●
Identify problems early
●
Allow frequent releases
VCS
CI Server
Feedback
Build
P. M. Duvall, S. Matyas, and A. Glover, Continuous integration:
improving software quality and reducing risk. Pearson Education, 2007.

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Build Failure
Build
Wasted resources
t
Examine Determine Fix
ErrorBuild log

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Software Defect Prediction
Complex
code
Bad
practices
Unstable
environment
Software
defect
M. D’Ambros, M. Lanza, and R. Robbes, Evaluating defect prediction approaches:
a benchmark and an extensive comparison, vol. 17, no. 4–5. 2012.

7. Thomas Rausch 7
Build Failure Prediction?
Complex
code
Bad
practices
Unstable
environment
Build
failure
?
Unstable
environment
?

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Research Questions
Which errors cause CI build failures?
What factors influence build outcomes?
Can we predict build failures?

9. II
Solution Approach

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Empirical Study
Research Setting
●
14 open source projects that employ CI
Topology mapping
a
b
c
d
VCS
Builds Logs
Change history
CI build history

11. Thomas Rausch 11
Outline
I Introduction
II Solution Approach
Study
III Systematic study of build errors
IV Factors influencing build failures
V Build failure prediction
VI Summary & Conclusion

12. III
Systematic Study of
Build Errors

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Build Steps
validatefetch compile inspect test

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Build Failure
validatefetch compile inspect test

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Build Error
Build log output
validatefetch compile inspect test
“[ERROR] Compilation Error”

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Build Error Frequency
Faulty VCS
interaction
Faulty build
configuration Dependency
error
Compilation
error
Coding-rule
violation
Failing test
Crash
40%
30%
20%
10%
0%
62%

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Build Error
t
validatefetch compile inspect test

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Frequency
Seconds
Error Probability Distribution
t

19. IV
Factors Influencing
Build Results

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Causes for Build Failures
Complex
code
Bad
practices
Unstable
environment
Build
failure
?

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Measurable Properties
Complex
code
Bad
practices
Unstable
environment
) = ?fn(

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Measurable Properties
Complex
code
Bad
practices
Unstable
environment
= fn(?)

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Measurable Properties
Change Metrics
●
What was done to the software?
Process Metrics
●
How were the changes applied?

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Measurable Properties
Change Metrics
●
Complexity
●
File types
●
Date and time
●
Author
Changes
.java .txt

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Measurable Properties
Process Metrics
●
Build history
●
Build type
●
Integration scenario
b1
b2
b4
t
b3
a
b
e
c
f
d
g
VCS
commit graph
CI build
information
Topology mapping

26. Thomas Rausch 27
Statistical Correlation Analyses
Categorical Variables
●
{c1, …,cn} ~ { , } Pearson’s chi-square test
Numerical Variables
●
R ~ { , } Mann–Whitney U test
D. J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures.
crc Press, 2003.

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Results
Metric Meta
Build history +++++
Build type ++
Author ++
Change complexity +
Date and time +
File types ~
Integration Scenario ~
Meta statistics
●
Using Fisher’s method
●
Relative relation strength
Van Zwet, W. R., & Oosterhoff, J. (1967). On the combination of independent test
statistics. The Annals of Mathematical Statistics, 38(3), 659–680. article.

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PassedBuild outcome Failed
Failed Passed
Previous build result
Percentageofbuilds
Results
Metric Meta
Build history +++++
Build type ++
Author ++
Change complexity +
Date and time +
File types ~
Integration Scenario ~
b
b’

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PassedBuild outcome Failed
Failed Passed
Previous build result
Percentageofbuilds
Results
Metric Meta
Build history +++++
Build type ++
Author ++
Change complexity +
Date and time +
File types ~
Integration Scenario ~
b
b’

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Results
Metric Meta
Build history +++++
Build type ++
Author ++
Change complexity +
Date and time +
File types ~
Integration Scenario ~
Example
●
Documentation file (changelog)
●
577 builds – doc. change only
●
14% original failures
●
52% test failures
●
45% environment crash
●
3% dependency error
Similar behavior in all projects
Noise skews statistics

31. V
Build Failure Prediction

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Machine Learning
?
Classifier
Observations
?
?

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Experiment Design
Well-known algorithms
●
Naive Bayes
●
C4.5 Decision Trees
●
Random Forest
Feature sets
●
Process metrics
●
Change metrics
●
Combined
Prediction for
●
Binary (failed/passed)
●
Multi-class (error type)
Baseline
●
0-R classifier
frequency table
predicts the average

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Binary classification result
Average F1
-scores
Binary classification
●
F1-score, 1.0 = perfect
●
Ranging from
●
0.71 – 0.91
Algorithm 0-R NB C4.5 RF
All CM PM
0.3
0.4
0.5
0.6
0.7
0.8
0.9

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Multi-class classification results
Multi-class classification
●
●
RMSE
●
Error between
actual and predicted
Average RMSE
p=[ p1 ... pn]
Algorithm 0-R NB C4.5 RF
All CM PM
0.0
0.3
0.1
0.2

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Update Predictions
False positive elimination
●
Likelihood for error declines
●
Plausibility of prediction declines
tmax t50 t75

37. VI
Summary & Conclusion

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Research Questions - Answered
Which errors cause CI build failures?
●
Failing tests (41%)
●
Coding-rule violations (11%)
●
Compilation errors (10%)
●
A number of errors involving the CI environment

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Research Questions - Answered
What factors influence build outcomes?
●
Process Metrics
●
Build history failures persist
●
Build type merges vs. forward engineering
●
Change Metrics
●
Actually, not so much

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Research Questions - Answered
Can we predict build failures?
●
Yes!
●
We can update predictions during the execution
State Rate min max
Passed 87% 45% 99%
Failed 66% 27% 96%

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Contributions
In-depth analysis of CI workflow
●
Multiplicity of build errors
●
Factors influencing build failures
Topology mapping
Baseline for CI build failure prediction

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Future Work
Examine more influence factors
●
Developer behavior
●
Project management workflows
Improve learning methods
Incorporate prediction into CI tools
●
Improve feedback mechanism
●
Improve development productivity

43. The end?
Questions & Answers
Thomas Rausch
t.rausch@infosys.tuwien.ac.at

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Research Subjects
Project Name Description
Apache Storm Distributed Computation Framework
Butterknife Android Dependency Injection Library
Crate.IO Scalable SQL database
JabRef Graphical Java application for managing BibTeX databases
jcabi-github Object Oriented Wrapper of GitHub API
Hystrix Latency and fault tolerance library for distributed systems
Presto Distributed SQL query engine for big data
Openmicroscopy Microscopy data environment
RxAndroid RxJava bindings for Android
Sponge API Minecraft plugin API
Spring Boot Java Application Framework
Square OkHttp HTTP+HTTP/2 client for Android and Java
Square Retofit HTTP client for Android and Java
Wordpress-Android WordPress for Android

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Integration Scenarios
a
b
c
f
e d
g
a
b
c
e
d
f
a
b
c
e
d
a
b
c
d
Initialization Update

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Related Work
Build Failure Prediction
●
Hassan and Zhang 2006
●
Outdated assumptions
●
No CI workflow considerations
●
Kerzazi et al. 2014
●
Only statistical analysis, no prediction
●
Only binary build outcome
●
Wolf 2009, Schröter 2010
●
Socio-technical factors
●
Closed source enterprise software

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Data Source
{
"id": 22555277,
"commit_id": 6534711,
"number": "784",
"pull_request_number": "1912",
"pull_request_title": "Example PR",
"started_at": "2014-04-08T19:37:44Z",
"finished_at": "2014-04-08T19:52:56Z",
"duration": 2648,
"state": "failed"
"commit": { ... }
}
Travis-CI
●
Hosted CI service
●
RESTful API
●
Integrated with GitHub

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Runtime Evolution