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. DevOps is complementary with Agile software development; several DevOps aspects came from the Agile methodology.
Other than it being a cross-functional combination of the terms and concepts for "development" and "operations," academics and practitioners have not developed a unique definition for the term "DevOps".[a][b][c][d] The idea behind this practice is to make delivery teams responsible for the production issues and fixes, whether legacy or new. In traditional practices, delivery would only be responsible for the changes put in by them, within the warranty period.
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".
This section may lend undue weight to certain ideas, incidents, or controversies. (December 2018)
Many of the ideas fundamental to DevOps practices are inspired by, or mirror, 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. 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. 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 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 was published by Nicole Forsgren, Gene Kim, Jez Humble and others. In 2014, they found that DevOps adoption was accelerating. Also in 2014, Lisa Crispin and Janet Gregory wrote the book More Agile Testing, containing a chapter on testing and DevOps.
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. 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?]
Forsgren et al. found that IT performance is strongly correlated with DevOps practices like source code management and continuous delivery. The authors later summarised their 4 year experience working with Puppet on the State of DevOps Report (SODR) in a book called Accelerate. The authors analysed 23,000 data points from a variety of companies of various different sizes (from start-up to enterprises), for-profit and not-for-profit and both those with legacy systems and those born digital. Their research identified four key metrics that are indicators of software delivery performance, which lead to higher rates of profitability, market share and customer satisfaction for their respective companies. The authors identified that “highest performers are twice as likely to meet or exceed their organizational performance goals.”
- Change Lead Time or Cycle Time - Time to implement, test, and deliver code for a feature (measured from first commit to deployment)
- Deployment Frequency - Number of deployments in a given duration of time
- Change Failure Rate (CFR) - Percentage of failed changes over all changes (regardless of success)
- Mean Time to Recovery (MTTR) - Time it takes to restore service after production failure
Relationship to other approachesEdit
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" 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 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.
TestOps is often considered a subset of DevOps, focusing on accelerating the practice of software testing within agile development methodologies. It includes the operations of test planning, managing test data, controlling changes to tests, organizing tests, managing test status, and gaining insights from testing activities to inform status and process improvements. 
While continuous delivery is focused on automating the processes in software delivery, DevOps also focuses on the organizational 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?]
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?]
This section needs expansion. You can help by adding to it. (June 2018)
Toyota production system, lean thinking, kaizenEdit
Toyota production system, also known under the acronym TPS, was the inspiration for lean thinking with its focus on continuous improvement, kaizen, flow and small batches. The Andon cord principle to create fast feedback, swarm and solve problems stems from TPS.
DevSecOps, Shifting Security LeftEdit
DevSecOps is an augmentation of DevOps to allow for security practices to be integrated into the DevOps approach. The traditional centralised 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 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.
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 a lead time for change are:
- Using version control for all production artifacts
- Automated testing
Practices that correlate with a 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 the use of version control and continuous delivery."
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 titleEdit
- Cattle not Pets: the paradigm of disposable server infrastructure.
- 10 deployments per day: the story of Flickr adopting DevOps.
Building a DevOps cultureEdit
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. Psychological safety is a fundamental enabler of DevOps cultures, and is reflected in Gene Kim's "Five Ideals" of 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?] 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 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 cycles 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 requirementsEdit
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
- Test automation
- Software deployment
- Software measurement
DevOps practices and adoptionEdit
Jabbari et al. identified DevOps practices and their dependencies. They developed a benefits 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.
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?]
In 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.
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