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Release engineering, frequently abbreviated as RE or as the clipped compound Releng, is a sub-discipline in software engineering concerned with the compilation, assembly, and delivery of source code into finished products or other software components. Associated with the software release life cycle, it was said by Boris Debic of Google Inc. that release engineering is to software engineering as manufacturing is to an industrial process:
Release engineering is the difference between manufacturing software in small teams or startups and manufacturing software in an industrial way that is repeatable, gives predictable results, and scales well. These industrial style practices not only contribute to the growth of a company but also are key factors in enabling growth.
Application lifecycle management (ALM) is the product lifecycle management of computer programs. It encompasses requirements management, software architecture, computer programming, software testing, software maintenance, change management, continuous integration, project management, and release management.
Build automation is the process of automating the creation of a software build and the associated processes including: compiling computer source code into binary code, packaging binary code, and running automated tests.
AnthillPro is a software tool originally developed and released as one of the first continuous integration servers. AnthillPro automates the process of building code into software projects and testing it to verify that project quality has been maintained. Software developers are able to identify bugs and errors earlier by using AnthillPro to track, collate, and test changes in real time to a collectively maintained body of computer code.
Parasoft is an independent software vendor specializing in automated software testing and application security with headquarters in Monrovia, California. It was founded in 1987 by four graduates of the California Institute of Technology who planned to commercialize the parallel computing software tools they had been working on for the Caltech Cosmic Cube, which was the first working hypercube computer built.
Release management is the process of managing, planning, scheduling and controlling a software build through different stages and environments; it includes testing and deploying software releases.
DevOps is a methodology in the software development and IT industry. Used as a set of practices and tools, DevOps integrates and automates the work of software development (Dev) and IT operations (Ops) as a means for improving and shortening the systems development life cycle.
Continuous testing is the process of executing automated tests as part of the software delivery pipeline to obtain immediate feedback on the business risks associated with a software release candidate. Continuous testing was originally proposed as a way of reducing waiting time for feedback to developers by introducing development environment-triggered tests as well as more traditional developer/tester-triggered tests.
Continuous delivery (CD) is a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time and, following a pipeline through a "production-like environment", without doing so manually. It aims at building, testing, and releasing software with greater speed and frequency. The approach helps reduce the cost, time, and risk of delivering changes by allowing for more incremental updates to applications in production. A straightforward and repeatable deployment process is important for continuous delivery.
BuildMaster is an application release automation tool, designed by the software development team Inedo. It combines build management and ARA capabilities to manage and automate processes primarily related to continuous integration, database change scripts, and production deployments, overall releasing applications reliably. The tool is browser-based and able to be used "out-of-the-box". Its feature set and scope puts it in line with the DevOps movement, and is marketed as "more than a release automatigs together the people, processes, and practices that allow teams to deliver software rapidly, reliably, and responsibly.” It's a tool that embodies incremental DevOps adoption.
Dynatrace, Inc. is a global technology company listed on the NYSE that provides a software intelligence platform based on artificial intelligence (AI) and automation. Dynatrace technologies are used to monitor and optimize application performance, software development and security practices, IT infrastructure, and user experience for businesses and government agencies throughout the world.
XebiaLabs is an independent software company specializing in DevOps and continuous delivery for large enterprise organizations. The offers a DevOps Platform, for application-release automation (ARO). These components include release orchestration, deployment automation, and DevOps intelligence.
DBmaestro is a computer software company with sales headquartered in Boston, and development in Israel. It markets its services for DevOps: collaboration between development and IT operations teams.
Infrastructure as code (IaC) is the process of managing and provisioning computer data centers through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. The IT infrastructure managed by this process comprises both physical equipment, such as bare-metal servers, as well as virtual machines, and associated configuration resources. The definitions may be in a version control system. The code in the definition files may use either scripts or declarative definitions, rather than maintaining the code through manual processes, but IaC more often employs declarative approaches.
A DevOps toolchain is a set or combination of tools that aid in the delivery, development, and management of software applications throughout the systems development life cycle, as coordinated by an organisation that uses DevOps practices.
Continuous configuration automation (CCA) is the methodology or process of automating the deployment and configuration of settings and software for both physical and virtual data center equipment.
StackEngine was founded in Austin, Texas in 2014 to build enterprise-grade container management and automation products to help organizations simply deploy, manage, and scale resilient applications. It was designed as a Docker management software product to provide an integrated DevOps solution for end-to-end container application delivery and operations. StackEngine was acquired by Oracle in December 2015.
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.
MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous development practice of DevOps in the software field. Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation, orchestration, and deployment, to health, diagnostics, governance, and business metrics. According to Gartner, MLOps is a subset of ModelOps. MLOps is focused on the operationalization of ML models, while ModelOps covers the operationalization of all types of AI models.
ModelOps, as defined by Gartner, "is focused primarily on the governance and lifecycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models". "ModelOps lies at the heart of any enterprise AI strategy". It orchestrates the model lifecycles of all models in production across the entire enterprise, from putting a model into production, then evaluating and updating the resulting application according to a set of governance rules, including both technical and business KPI's. It grants business domain experts the capability to evaluate AI models in production, independent of data scientists.
References
- ↑ "Gartner IT Glossary – Application Release Automation (ARA)". Gartner. Retrieved 25 September 2014.
- ↑ Application Release Automation Market Forecast (Report). Forrester. 2015.
- ↑ Garner Market Trends: DevOps – Not a Market, but Tool-Centric Philosophy That supports a Continuous Delivery Value Chain (Report). Gartner. 18 February 2015.
- ↑ Humble, Jez; Farley, David (2011). Continuous Delivery: reliable software releases through build, test, and deployment automation. Pearson Education Inc. p. 255-257. ISBN 978-0-321-60191-9.
- ↑ Market Overview: Application Release Automation Tools (Report). Forrester. 2015.
- ↑ Market Guide for Application Release Automation Solutions (Report). Gartner. 20 July 2015.
- ↑ Fletcher, Colin (1 August 2016). Critical Capabilities for Application Release Automation Solutions (Report). Gartner.