DevOps

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Software development
Core activities of software engineering Processes Requirements Design Construction Testing Debugging Deployment Maintenance
Paradigms and models Agile Cleanroom Incremental Prototyping Spiral V model Waterfall
Methodologies and frameworks ASD DevOps DAD DSDM FDD IID Kanban Lean SD LeSS MDD MSF PSP RAD RUP SAFe Scrum SEMAT TSP OpenUP UP XP
Supporting disciplines Configuration management Documentation Software quality assurance Project management User experience
Practices ATDD BDD CCO CI CD DDD PP SBE Stand-up TDD
Tools Compiler Debugger Profiler GUI designer Modeling IDE Build automation Release automation Infrastructure as code Testing
Standards and Bodies of Knowledge BABOK CMMI IEEE standards ISO 9001 ISO/IEC standards PMBOK SWEBOK ITIL IREB
Glossaries Artificial intelligence Computer science Electrical and electronics engineering
Outlines Outline of software development
v t

DevOps is a set of practices that combines software development (Dev) and IT operations (Ops). It aims to shorten the systems development life cycle and provide continuous delivery with high software quality.^[1] DevOps is complementary with Agile software development; several DevOps aspects came from the Agile methodology.

Definition[]

Other than it being a cross-functional combination of the terms and concepts for "development" and "operations," academics and practitioners have not developed a universal definition for the term "DevOps".^[a][b][c][d] Most often, DevOps is characterized by key principles: shared ownership, workflow automation, and rapid feedback.

From an academic perspective, Len Bass, , and —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".^[5]

However, the term is used in multiple contexts. At its most successful, DevOps is a combination of specific practices, culture change, and tools.^[6]

History[]

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In 1993 the Telecommunications Information Networking Architecture Consortium (TINA-C) defined a Model of a Service Lifecycle that combined software development with (telecom) service operations.^[7] Some say that DevOps emerged in part as a reaction to the "top-down" proscriptive approach of ITIL in the 1990s. DevOps, as a "bottom-up" approach, gained traction and persisted because it was created by software engineers for software engineers, and is a flexible practice rather than a rigid framework.

In 2014, Lisa Crispin and Janet Gregory wrote the book More Agile Testing, containing a chapter on testing and DevOps.^[8]

Taxonomy[]

The DevOps taxonomy was refined and made publicly available in 2021 through the release of a white paper titled, "The DevOps: A Concise Understanding to the DevOps Philosophy and Science".^[9] The DevOps taxonomy consists of Philosophy and Science, their respective branches (Epistemology and Ontology; Social and Applied) and sub-domains (Belief, Knowledge, and Truth; Sociology, Psychology, Economics, and Engineering) with provided instantiations (Agile, Quality Assurance, Lean; Culture Transformation, Faster Time to Market, and Automation).

Toolchains[]

As DevOps is intended to be a cross-functional mode of working, those who practice the methodology use different sets of tools—referred to as "toolchains"—rather than a single one.^[10] These toolchains are expected to fit into one or more of the following categories, reflective of key aspects of the development and delivery process.

1. Coding – code development and review, source code management tools, code merging.
2. Building – continuous integration tools, build status.
3. Testing – continuous testing tools that provide quick and timely feedback on business risks.
4. Packaging – artifact repository, application pre-deployment staging.
5. Releasing – change management, release approvals, release automation.
6. Configuring – infrastructure configuration and management, infrastructure as code tools.
7. Monitoring – applications performance monitoring, end-user experience.

Relationship to other approaches[]

Many of the ideas fundamental to DevOps practices are inspired by, or mirror, other well known practices such as Lean and Deming's Plan-Do-Check-Act cycle, through to The Toyota Way and the Agile approach of breaking down components and batch sizes.^[9]

Agile[]

The motivations for what has become modern DevOps and several standard DevOps practices such as automated build and test, continuous integration, and continuous delivery originated in the Agile world, which dates (informally) to the 1990s, and formally to 2001. Agile development teams using methods such as Extreme Programming couldn't "satisfy the customer through early and continuous delivery of valuable software"^[11] unless they subsumed the operations / infrastructure responsibilities associated with their applications, many of which they automated. Because Scrum emerged as the dominant Agile framework in the early 2000s and it omitted the engineering practices that were part of many Agile teams, the movement to automate operations / infrastructure functions splintered from Agile and expanded into what has become modern DevOps. Today, DevOps focuses on the deployment of developed software, whether it is developed via Agile or other methodologies.

ArchOps[]

ArchOps presents an extension for DevOps practice, starting from software architecture artifacts, instead of source code, for operation deployment.^[12] ArchOps states that architectural models are first-class entities in software development, deployment, and operations.

CI/CD[]

Automation is a core principle for achieving DevOps success and CI/CD is a critical component.^[13]

CI/CD comprises of continuous integration (CI) and continuous delivery (CD), or continuous deployment (CD). Used together, the three processes automate build, testing, and deployment so DevOps teams can ship code changes faster and more reliably. When referring to CI/CD, the “CD” being referenced is usually continuous delivery, not continuous deployment. Continuous delivery and other CI/CD processes are focused on automating software delivery tasks, while DevOps also focuses on the organizational change to support great collaboration between the many functions involved. Both share a common background in agile methods and lean thinking, prioritizing small and frequent changes with focused value to the end customer. This ensures two things: Software is always in a releasable state throughout its lifecycle, which makes it cheaper and less risky to deliver the software.

Plus, improved collaboration and communication between and within teams helps achieve faster time to market, with reduced risks.^[14]

DataOps[]

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. 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.

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.^[15] While SRE predates the development of DevOps, they are generally viewed as being related to each other.

DevSecOps, Shifting Security Left[]

DevSecOps is an augmentation of DevOps to allow for security practices to be integrated into the DevOps approach. The traditional centralized security team model must adopt a federated model allowing each delivery team the ability to factor in the correct security controls into their DevOps practices. Shifting security left is an approach to software security whereby security practices and testing are performed earlier in the development lifecycle.

BizOps[]

BizOps is contrasted with DevOps because of its more integrated approach. While DevOps is more focused on IT and software development, BizOps integrates technology into daily organizational decisions and business operations.

Cultural change[]

DevOps initiatives can create cultural changes in companies^[16] by transforming the way operations, developers, and testers collaborate during the development and delivery processes.^[1] Getting these groups to work cohesively is a critical challenge in enterprise DevOps adoption.^[17][18] DevOps is as much about culture, as it is about the toolchain.^[19]

Building a DevOps culture[]

Organizational culture is a strong 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. DevOps as a service approach allows developers and operations teams to take greater control of their applications and infrastructure without hindering speed. It also transfers the onus of owning a problem on to the development team, making them much more careful in their stride.

The 2015 State of DevOps Report discovered that the top seven measures with the strongest correlation to organizational culture are:

1. Organizational investment
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

Deployment[]

Companies with very frequent releases may require knowledge on DevOps.^{[citation needed]} For example, the company that operates image hosting website Flickr developed a DevOps approach to support ten deployments a day. Daily deployment cycles would be much higher at organizations producing multi-focus or multi-function applications.^{[citation needed]} Daily deployment is referred to as continuous deployment

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 monitor-ability.

Microservices[]

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,^[20].

DevOps automation[]

It also supports consistency, reliability, and efficiency within the organization, and is usually enabled by a shared code repository or version control. As DevOps researcher Ravi Teja Yarlagadda hypothesizes, "Through DevOps, there is an assumption that all functions can be carried out, controlled, and managed in a central place using a simple code."^[21]

Automation with version control[]

Many organizations use version control to power DevOps automation technologies like virtual machines, containerization (or OS-level virtualization), and CI/CD. The paper DevOps: development of a toolchain in the banking domain notes that with teams of developers working on the same project, "All developers need to make changes to the same codebase and sometimes edit even the same files. For efficient working, there has to be a system that helps engineers avoid conflicts and retain the codebase history,"^[22] with the Git version control system and the GitHub platform referenced as examples.

Adoption[]

DevOps practices and adoption[]

DevOps practices, and their dependencies include a dependency network which connects potential benefits to an ordered chain of practices. Using this network organizations can choose a path that enables fulfillment of their goals.

Adoption of DevOps is being driven by many factors – including:

1. Use of agile and other development processes and methods;
2. Demand for an increased rate of production releases – from application and business unit stakeholders;
3. Wide availability of virtualized and cloud infrastructure – from internal and external providers;
4. Increased usage of data center automation and configuration management tools;
5. Increased focus on test automation and continuous integration methods;
6. A critical mass of publicly available best practices.

See also[]

• Twelve-factor app
• Infrastructure as code
• Lean software development
• Value Stream

Notes[]

References[]

Further reading[]

• Davis, Jennifer; Daniels, Ryn (30 May 2016). Effective DevOps : building a culture of collaboration, affinity, and tooling at scale. Sebastopol, CA: O'Reilly. ISBN 9781491926437. OCLC 951434424.
• Kim, Gene; Debois, Patrick; Willis, John; Humble, Jez; Allspaw, John (7 October 2015). The DevOps handbook : how to create world-class agility, reliability, and security in technology organizations (First ed.). Portland, OR. ISBN 9781942788003. OCLC 907166314.
• Forsgren, Nicole; Humble, Jez; Kim, Gene (27 March 2018). Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations (First ed.). IT Revolution Press. ISBN 9781942788331.