On continuous deployment maturity in customer projects

Virtanen, Antti; Kuusinen, Kati; Leppänen, Marko; Luoto, Antti; Kilamo, Terhi; Mikkonen, Tommi

doi:10.1145/3019612.3019777

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…More specifically, the study design corresponds to an embedded case study design [16] with multiple units of analysis. The main unit of analysis is the organization and its The primary research method use in the study was a survey based on a previously employed maturity test, the first Solita Test [15]. The replicated, extended survey was directed at Solita's project development teams.…”

Section: Methodsmentioning

confidence: 99%

“…Where large projects require significant effort to setup CD pipeline and practices, it may not be feasible to small projects due to other constraints such as staffing, time and budget constraints. In 2015, an in-house maturity model, the Solita Test [15], was developed to assess the CD maturity in Solita's development and data warehousing projects. The model evaluates the maturity of the project in five maturity categories ( Figure 1).…”

Section: Case Definitionmentioning

confidence: 99%

“…The goal is to release new features as soon as they are ready instead of making large releases of many features developed over several months. This practice, called continuous deployment, CD [5], requires a mature enough development and deployment infrastructure -so-called deployment pipeline [7] -from code to delivery and deployment, matching practices [10], as well as organizational maturity [15].…”

Section: Introductionmentioning

confidence: 99%

“…The company focuses on software development as a service, data intensive projects (including both data warehousing and business intelligence and data analysis), and data analytics for customer projects. We report results from a survey study on a continuous deployment maturity model survey conducted first in 2015 [15] and now as a follow-up in 2017 for over 40 software projects in total. The goal is to study how development processes have evolved over time.…”

Section: Introductionmentioning

confidence: 99%

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Revisiting continuous deployment maturity

Mäkinen

Lehtonen²,

Kilamo

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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Background: Achieving a steady stream of small releases and employing practices such as continuous deployment requires maturity in company processes. Maturity models provide one approach for companies to pinpoint areas of improvement by providing a position and hints to reflect on. Incorporating maturity models with agile software development and continuous deployment has its challenges, though. Aims: The focus of the study is in understanding the evolution of software processes towards continuous deployment in an industry organization over time when a maturity model is used as a yardstick in evaluation. Method: An embedded case study by design, the study utilizes and replicates a survey on the state of software projects in a large Finnish software company, Solita. The survey was initially conducted in 2015 with responses from 35 projects and now replicated in 2017 with responses from 43 projects. Both quantitative and qualitative approaches for survey responses are used in the analysis. Results: Maturity of software processes in the case company show improvement in deployment and in monitoring, albeit short of statistical significance. Technological advances in the application of cloud computing have likely spurred development in these areas. Capability in processes related to test automation and quality has not changed much in two years. Conclusions: Maintaining maturity in software processes requires constant attention as impressions on process quality can gradually diminish. Projects which are built on a compatible technology stack have a greater chance in achieving continuous deployment and thus being more mature. Customer preferences also make a difference in the ability to reach certain maturity levels.

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Section: Methodsmentioning

confidence: 99%

Section: Case Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Revisiting continuous deployment maturity

Mäkinen

Lehtonen²,

Kilamo

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Today, data warehouse (DWH), analytics and business intelligence (BI) stand for some of the most important information initiatives for companies [1], [2]. The continuous evolution of DWH implementation [3], the foundation for decision support systems [4]- [7], with new concepts such as data lakes [8], [9], big data [10]- [15], NoSQL technologies [16]- [19], and real-time streaming [20]- [23], is happening in an era characterized by persistently faster release cycles [24], [25] and constant product enhancements [26], [27]. DWH projects are mostly noted as large [28], time consuming [29], expensive [30]- [32], and change-sensitive [33] enterprise projects.…”

Section: Introductionmentioning

confidence: 99%