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1
Which	Factors	Influence	
Practitioners’	Usage	of	Build	
Automation	Tools?	
Akond Rahman*,	Asif	Partho**,	David	Meder$,	and	Laurie	
Williams*
North	Carolina	State	University*,	Nested	Apps**,	Red	Hat$
Contact:	aarahman@ncsu.edu
2
Motivation
• Build	automation	is	not	widely	used,	and	can	
have	negative	consequences
• Example:	
https://www.bloomberg.com/news/articles/2012-08-02/knight-shows-how-to-lose-440-million-in-30-minutes
3
Research	Objective
The	goal	of	this	paper	is	to	aid	software	
practitioners	in	increasing	their	usage	of	build	
automation	tools	by	identifying	the	adoption	
factors	that	influence	usage of	these	tools.
4
Background	
Humble	and	Farley	defined	build	automation	as	the	
technology	which	automatically	compiles	and	tests	
software	changes,	packages	the	software	changes	into	a	
binary,	and	prepare	the	created	binary	for	deployment.	
https://continuousdelivery.com/
5
Background	(Contd.)	
• Build	(B) tools:	
– Example:	Ant	and	Maven.	
• Continuous	integration	(CI) tools:	
– Example:	Jenkins	and	Travis-CI.	
• Infrastructure	as	Code	(IaC) tools:
– Example:	Chef	and	Puppet.	
• Version	control	(VC) tools:	
– Example:	Git and	Subversion	(SVN).
6
Background	(Contd.)
• Education	- 6	factors
• Diffusion	of	Innovation	(DOI)	Theory
– Advantages - 4	factors
– Compatibility - 4	factors
– Complexity - 4	factors
– Observability - 4	factors
– Trialability - 4	factors
7
Our	Contributions
• A	list	of	adoption	factors	that	influence	usage	
of	B,CI,	IaC,	and	VC tools;	and	
• A	rank	of	the	identified	adoption	factors	that	
influence	usage	of	B,CI,	IaC,	and	VC tools
8
Research	Questions
• RQ-1:	Which	adoption	factors	influence	usage	
of	B,CI,	IaC,	and	VC tools?	
• RQ-2:	How	can	we	prioritize	the	identified	
influencing	adoption	factors	for	B,CI,	IaC,	and	
VC tools?
9
Methodology
Red	Hat,	
NestedApps,	and	
OSS	Projects
26	Adoption	Factors	
from	Six	Concepts
Logistic	Regression 268	Professionals
10
Empirical	Findings:	Summary	of	
Professional	Experience
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
B CI IaC VC
< 1 y 1 ~ 2 y 3 ~ 5 y 6 ~ 10 y > 10 y
11
Empirical	Findings:	Summary	of	Tool	
Usage
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
B CI IaC VC
< 1 y 1 ~ 2 y 3 ~ 5 y 6 ~ 10 y > 10 y
12
Empirical	Findings:	RQ1	(“Which	adoption	factors	
influence	usage	of	B,	CI,	IaC,	and	VC	tools?”)
• Build	tools	(B)
– Advantages
– Compatibility
– Complexity
– Education
– Observability
13
Empirical	Findings:	RQ1	(“Which	adoption	factors	
influence	usage	of	B,	CI,	IaC,	and	VC	tools?”)
• Continuous	integration	tools	(CI)
– Compatibility
– Complexity
– Education
– Observability
– Trialability
14
Empirical	Findings:	RQ1	(“Which	adoption	factors	
influence	usage	of	B,	CI,	IaC,	and	VC	tools?”)
• Infrastructure	as	code	tools	(IaC)
– Compatibility
– Education
– Observability
– Trialability
15
Empirical	Findings:	RQ1	(“Which	adoption	factors	
influence	usage	of	B,	CI,	IaC,	and	VC	tools?”)
• Version	control	tools	(VC)
– Compatibility
– Complexity
– Education
– Observability
– Trialability
16
Summary	of	RQ1
17
Empirical	Findings:	RQ2	(“How	can	we	prioritize	
the	identified	influencing	adoption	factors	for	B,	
CI,	IaC,	and	VC	tools?”)
• Summary:
– For	B tools,	the	highest	priority	adoption	factor	is	‘B	tools	
are	highly	configurable’	(Compatibility).	
– For	CI tools,	the	highest	priority	factor	is	‘CI	tools	are	
compatible	with	the	technologies	that	I	use’	
(Compatibility).	
– For	IaC tools,	the	highest	priority	factor	is	‘Use	of	IaC tools	
is	not	very	visible	in	my	organization’	(Observability).	
– For	VC tools,	the	factor	‘fits	well	with	the	way	I	work’	
(Compatibility)
– Overall,	Education-related	adoption	factors	have	lower	
priority.
18
Implications
• Select	build	automation	tools	that	fit	well	with	
the	usual	work	style	of	the	team	members	and	
that	can	be	easily	customized	to	the	needs	of	
the	team	members	
– Example:	A	team	that	uses	Ruby	might	adopt	tools	
that	are	Ruby-friendly	e.g.	Chef	as	a	IaC tool,	and	
Rake	as	a	B	tool.
19
Implications	(Contd.)
• Practitioner-led	demonstrations	of	build	
automation	tools	at	public	events
https://www.cloudbees.com/jenkinsworld/home
https://puppet.com/puppetconf
https://git-merge.com/	
https://www.apachecon.com/
20
Limitations
• Survey	response	rate:	9.9%
• Factors	used	in	the	survey:	26	adoption	factors
• Factor	selection	process:	Subjectivity
21
Conclusion
• Survey	of	268	software	professionals.	
• Compatibility of	build	automation	tools	can	
increase	usage.	
• Observability	of	build	automation	tool	usage	
can	increase	usage.

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Which Factors Influence Practitioners' Usage of Build Automation Tools?