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|Paradigms and models|
|Methodologies and frameworks|
|Standards and Bodies of Knowledge|
DevOps is a set of software development practices that combines software development (Dev) and information technology operations (Ops) to shorten the systems development life cycle while delivering features, fixes, and updates frequently in close alignment with business objectives.
- 1 Definition
- 2 History
- 3 Toolchains
- 4 Relationship to other approaches
- 5 Goals
- 6 Criticism
- 7 Cultural change
- 8 Deployment
- 9 Adoption
- 10 See also
- 11 Notes
- 12 References
- 13 Further reading
From an academic perspective, Len Bass, Ingo Weber, and Liming Zhu—three computer science researchers from the CSIRO and the Software Engineering Institute—suggested defining DevOps as "a set of practices intended to reduce the time between committing a change to a system and the change being placed into normal production, while ensuring high quality".
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In 2009, the first conference named devopsdays was held in Ghent, Belgium. The conference was founded by Belgian consultant, project manager and agile practitioner Patrick Debois.[who?] The conference has now spread to other countries.
In 2012, the State of DevOps report was conceived and launched by Alanna Brown at Puppet. As of 2014, the annual State of DevOps report is published by Nicole Forsgren, Gene Kim, Jez Humble and others.  In 2014, they found that DevOps adoption was accelerating.
In 2015, Nicole Forsgren, Jez Humble and Gene Kim found DORA: DevOps Research and Assessment.
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As DevOps is intended to be a cross-functional mode of working, those that practice the methodology use different sets of tools—referred to as "toolchains"—rather than a single one. These toolchains are expected to fit into one or more of the following categories, reflective of key aspects of the development and delivery process:[unreliable source?][unreliable source?]
- Coding – code development and review, source code management tools, code merging
- Building – continuous integration tools, build status
- Testing – continuous testing tools that provide quick and timely feedback on business risks
- Packaging – artifact repository, application pre-deployment staging
- Releasing – change management, release approvals, release automation
- Configuring – infrastructure configuration and management, infrastructure as code tools
- Monitoring – applications performance monitoring, end-user experience
Some categories are more essential in a DevOps toolchain than others; especially continuous integration (e.g. Jenkins, Gitlab, Bitbucket pipelines) and infrastructure as code (e.g. Terraform, Ansible, Puppet).[unreliable source?][unreliable source?]
Relationship to other approaches
Agile and DevOps both often utilize practices such as automated build and test, continuous integration, and continuous delivery.[unreliable source?] Agile can be viewed as addressing communication gaps between customers and developers, while DevOps addresses gaps between developers and IT operations / infrastructure.[unreliable source?] Also, DevOps has focus on the deployment of developed software, whether it is developed via Agile or other methodologies.[unreliable source?]
ArchOps presents an extension for DevOps practice, starting from software architecture artifacts, instead of source code, for operation deployment.. ArchOps states that architectural models are first-class entities in software development, deployment, and operations.
While continuous delivery is focused on automating the processes in software delivery, DevOps also focuses on the organization change to support great collaboration between the many functions involved.
DevOps and continuous delivery share a common background in agile methods and lean thinking: small and frequent changes with focused value to the end customer.[unreliable source?] Lean management and continuous delivery are fundamental to delivering value faster, in a sustainable way. Continuous delivery focuses on making sure the software is always in a releasable state throughout its lifecycle. This makes it cheaper and less risky to deliver the software.
The application of continuous delivery and DevOps to data analytics has been termed DataOps. DataOps seeks to integrate data engineering, data integration, data quality, data security, and data privacy with operations.[unreliable source?] It applies principles from DevOps, Agile Development and the statistical process control, used in lean manufacturing, to improve the cycle time of extracting value from data analytics.[unreliable source?]
Site reliability engineering
In 2003, Google developed site reliability engineering (SRE), an approach for releasing new features continuously into large-scale high-availability systems while maintaining high-quality end user experience. While SRE predates the development of DevOps, they are generally viewed as being related to each other.[unreliable source?] Some aspects of DevOps have taken a similar approach.[unreliable source?]
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IT performance can be measured in terms of throughput and stability.  Throughput can be measured by deployment frequency and lead time for changes; stability can be measured by mean time to recover. The State of DevOps Reports found that investing in practices that increase these throughput and stability measures increase IT performance.
The goals of DevOps span the entire delivery pipeline. They include:
- Improved deployment frequency;
- Faster time to market;
- Lower failure rate of new releases;
- Shortened lead time between fixes;
- Faster mean time to recovery (in the event of a new release crashing or otherwise disabling the current system).
Simple processes become increasingly programmable and dynamic, using a DevOps approach.[unreliable source?] DevOps aims to maximize the predictability, efficiency, security, and maintainability of operational processes. Very often, automation supports this objective.
DevOps integration targets product delivery, continuous testing, quality testing, feature development, and maintenance releases in order to improve reliability and security and provide faster development and deployment cycles. Many of the ideas (and people) involved in DevOps came from the enterprise systems management and agile software development movements.[unreliable source?]
Practices that correlate with deployment frequency are:
- Continuous delivery
- Using version control for all production artifacts
Practices that correlate with lead time for change are:
- Using version control for all production artifacts
- Automated testing
Practices that correlate with mean time to recovery for change are:
- Using version control for all production artifacts
- Monitoring system and application health
Companies failed verification] have reported significant benefits, including: significantly shorter time to market, improved customer satisfaction, better product quality, more reliable releases, improved productivity and efficiency, and the increased ability to build the right product by fast experimentation.[
The 2014 State of DevOps Report found that "IT performance strongly correlates with well-known DevOps practices such as use of version control and continuous delivery."
There is a lack of evidence in academic literature on the effectiveness of DevOps.[e]
A similar absence exists in practice. F5 Networks, for example, found that only one in five surveyed IT professionals thought that DevOps had a strategic impact on their organization despite rise in usage. The same study found that only 17% of those surveyed identified DevOps as key, well below software as a service (42%), big data (41%), and public cloud infrastructure as a service (39%).
DevOps initiatives can create cultural changes in companies  by transforming the way operations, developers, and testers collaborate during the development and delivery processes. Getting these groups to work cohesively is a critical challenge in enterprise DevOps adoption. DevOps is as much about culture, as it is about the toolchain.
DevOps as a job title
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- Cattle not Pets: the paradigm of disposable server infrastructure.
- 10 deployments per day: the story of Flickr adopting DevOps.
Building a DevOps culture
Organizational culture is a strongest predictor of IT and organizational performance. Cultural practices such as information flow, collaboration, shared responsibilities, learning from failures and new ideas are central to DevOps. Team-building and other employee engagement activities are often used to create an environment that fosters this communication and cultural change within an organization. Team–building activities can include board games, trust activities, and employee engagement seminars.[unreliable source?]
The 2015 State of DevOps Report discovered that the top seven measures with the strongest correlation to organizational culture are: 1. Organizational investment in DevOps: 2. Team leaders' experience and effectiveness. 3. Continuous delivery. 4. The ability of different disciplines (development, operations, and infosec) to achieve win-win outcomes. 5. Organizational performance. 6. Deployment pain. 7. Lean management practices.
Companies with very frequent releases may require knowledge on DevOps. For example, the company that operates image hosting website Flickr developed a DevOps approach to support ten deployments a day. Daily deployment cycle would be much higher at organizations producing multi-focus or multi-function applications. Daily deployment is referred to as continuous deployment[unreliable source?] or continuous delivery [unreliable source?] and has been associated with the lean startup methodology.[unreliable source?] Professional associations and blogs posts have formed on the topic since 2009.[unreliable source?][unreliable source?]
Architecturally significant requirements
To practice DevOps effectively, software applications have to meet a set of architecturally significant requirements (ASRs), such as: deployability, modifiability, testability, and monitorability. These ASRs require a high priority and cannot be traded off lightly.
Although in principle it is possible to practice DevOps with any architectural style, the microservices architectural style is becoming the standard for building continuously deployed systems. Small size service allows the architecture of an individual service to emerge through continuous refactoring, hence reducing the need for a big upfront design , allows for releasing the software early and continuously.
DevOps automation can be achieved by repackaging platforms, systems, and applications into reusable building blocks through the use of technologies such as virtual machines and containerization.[unreliable source?]
Implementation of DevOps automation in the IT-organization is heavily dependent on tools, [unreliable source?]which are  to cover different areas of the systems development lifecycle (SDLC):
- Infrastructure as code — Ansible, Terraform, Puppet, Chef
- CI/CD — Jenkins, TeamCity, Shippable, Bamboo, Azure DevOps
- Test automation — Selenium, Cucumber, Apache JMeter
- Containerization — Docker, Rocket, Unik
- Orchestration — Kubernetes, Swarm, Mesos
- Software deployment — Elastic Beanstalk, Octopus, Vamp
- Measurement — NewRelic, Kibana, Datadog, DynaTrace
- ChatOps — Hubot, Lita, Cog
Some articles in the DevOps literature assume or recommend significant participation in DevOps initiatives from outside an organization's IT department, e.g.: "DevOps is just the agile principle, taken to the full enterprise."[unreliable source?]
A survey published in January 2016 by the SaaS cloud-computing company RightScale, DevOps adoption increased from 66 percent in 2015 to 74 percent in 2016. And among larger enterprise organizations, DevOps adoption is even higher – 81 percent.[unreliable source?]
Adoption of DevOps is being driven by many factors – including:
- Use of agile and other development processes and methods;
- Demand for an increased rate of production releases – from application and business unit stakeholders;
- Wide availability of virtualized[unreliable source?] and cloud infrastructure – from internal and external providers;
- Increased usage of data center automation[unreliable source?] and configuration management tools;
- Increased focus on test automation[unreliable source?] and continuous integration methods;
- A critical mass of publicly available best practices.
- Dyck et. al (2015) "To our knowledge, there is no uniform definition for the terms release engineering and DevOps. As a consequence, many people use their own definitions or rely on others, which results in confusion about those terms."
- Jabbari et. al (2016) "The research results of this study showed the need for a definition as individual studies do not consistently define DevOps."
- Erich et. al (2017) "We noticed that there are various gaps in the study of DevOps: There is no consensus of what concepts DevOps covers, nor how DevOps is defined."
- Erich et. al (2017) "We discovered that there exists little agreement about the characteristics of DevOps in the academic literature."
- Erich et. al (2017) "We noticed that there are various gaps in the study of DevOps: [...] There is a lack of evidence on the effectiveness of DevOps.
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