Even though build automation tools help to reduce errors and rapid releases of software changes, use of build automation tools is not widespread amongst software practitioners. Software practitioners perceive build automation tools as complex, which can hinder the adoption of these tools. How well founded such perception is, can be determined by systematic exploration of adoption factors that influence usage of build automation tools. 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. We conducted a survey to empirically identify the adoption factors that influence usage of build automation tools. We obtained survey responses from 268 software professionals who work at NestedApps, Red Hat, as well as contribute to open source software. We observe that adoption factors related to complexity do not have the strongest influence on usage of build automation tools. Instead, we observe compatibility-related adoption factors, such as adjustment with existing tools, and adjustment with practitioner’s existing workflow, to have influence on usage of build automation tools with greater importance. Findings from our paper suggest that usage of build automation tools might increase if: build automation tools fit well with practitioners’ existing workflow and tool usage; and usage of build automation tools are made more visible among practitioners’ peers.

1. 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. 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. 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. 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. 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. 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. 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. 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. 9
Methodology
Red Hat,
NestedApps, and
OSS Projects
26 Adoption Factors
from Six Concepts
Logistic Regression 268 Professionals

10. 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. 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. 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. 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. 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. 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. 16
Summary of RQ1

17. 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. 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. 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. 20
Limitations
• Survey response rate: 9.9%
• Factors used in the survey: 26 adoption factors
• Factor selection process: Subjectivity

21. 21
Conclusion
• Survey of 268 software professionals.
• Compatibility of build automation tools can
increase usage.
• Observability of build automation tool usage
can increase usage.