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Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices
Open accessAbstract
Continuous practices, i.e., continuous integration, delivery, and deployment, are the software development industry practices that enable organizations to frequently and reliably release new features and products. With the increasing interest in the literature on continuous practices, it is important to systematically review and synthesize the approaches, tools, challenges, and practices reported for adopting and implementing continuous practices. This paper aimed at systematically reviewing the state of the art of continuous practices to classify approaches and tools, identify challenges and practices in this regard, and identify the gaps for future research. We used the systematic literature review method for reviewing the peer-reviewed papers on continuous practices published between 2004 and June 1, 2016. We applied the thematic analysis method for analyzing the data extracted from reviewing 69 papers selected using predefined criteria. We have identified 30 approaches and associated tools, which facilitate the implementation of continuous practices in the following ways: (1) reducing build and test time in continuous integration (CI); (2) increasing visibility and awareness on build and test results in CI; (3) supporting (semi-) automated continuous testing; (4) detecting violations, flaws, and faults in CI; (5) addressing security and scalability issues in deployment pipeline; and (6) improving dependability and reliability of deployment process. We have also determined a list of critical factors, such as testing (effort and time), team awareness and transparency, good design principles, customer, highly skilled and motivated team, application domain, and appropriate infrastructure that should be carefully considered when introducing continuous practices in a given organization. The majority of the reviewed papers were validation (34.7%) and evaluation (36.2%) research types. This paper also reveals that continuous practices have been successfully applied to both greenfield and maintenance projects. Continuous practices have become an important area of software engineering research and practice. While the reported approaches, tools, and practices are addressing a wide range of challenges, there are several challenges and gaps, which require future research work for improving the capturing and reporting of contextual information in the studies reporting different aspects of continuous practices; gaining a deep understanding of how software-intensive systems should be (re-) architected to support continuous practices; and addressing the lack of knowledge and tools for engineering processes of designing and running secure deployment pipelines.
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“Despite the popularity of DevOps, security is among the major concerns that hinder the adoption of DevOps in software enterprises [5].”
From Prioritization based taxonomy of DevOps security challenges using PROMETHEE (2020) · cites this paper as [5]
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“We have also identified lack of automated testing tool performance measures for security [2], [5], [20], [23], [24] as a critical challenge that have a negative impact on DevOps security.”
From Prioritization based taxonomy of DevOps security challenges using PROMETHEE (2020) · cites this paper as [5]
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Kostromin, R. O. (2020) · International Workshop on Information, Computation, and Control Systems for Distributed Environments (ICCS-DE) · doi:10.47350/iccs-de.2020.15 · Cited by 9
Zhang, J., Ferydouni, R., Montana, A., Bittman, D., & Alvaro, P. (2021) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3472883.3486986 · Cited by 9
What is There About DevOps Assessment? A Systematic Mapping
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Agent-Based DevOps of Software and Hardware Resources for Digital Twins of Infrastructural Objects
Kostromin, R. O., & Feoktistov, A. G. (2020) · International Conference on Future Networks and Distributed Systems (ICFNDS) · doi:10.1145/3440749.3442599 · Cited by 10
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Strandberg, P. E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08312 · Cited by 9
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Petrakis, K., Agorogiannis, E., Antonopoulos, G., Anagnostopoulos, T., Grigoropoulos, N., Veroni, E., Berne, A., Azaiez, S., Benomar, Z., Kakoulidis, H., et al. (2025) · Software · doi:10.3390/software4020010 · Cited by 8
Metadata
- Authors
- Shahin, M., Ali Babar, M., & Zhu, L.
- Affiliations
- The University of Adelaide (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- IEEE Access
- Published
- 2017
- Volume
- 5
- Pages
- 3909-3943
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2603119212
- Cited by
- 680
- References
- 47
- Citations by year
- 2026: 86 · 2025: 111 · 2024: 89 · 2023: 97 · 2022: 89 · 2021: 83 · 2020: 49 · 2019: 38 · 2018: 33 · 2017: 5
- FWCI
- 10.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- arXiv (Cornell University) (acceptedVersion)
- RMIT Research Repository (RMIT University Library) (acceptedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Data61, a business unit of CSIRO, Australia
- Australian Government Research Training Program Scholarship
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- continuous integration, continuous delivery, continuous deployment, continuous practice, deployment pipelines, scalability, barriers to adoption
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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What counts as a claim ›How we measure confidence ›How we check this ›
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