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Software release life cycle
Software release life cycle
Software release life cycle
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The software release life cycle is the process of developing, testing, and distributing a software product (e.g., an operating system). It typically consists of several stages, such as pre-alpha, alpha, beta, and release candidate, before the final version, or "gold", is released to the public.

An example of a basic software release life cycle

Pre-alpha refers to the early stages of development, when the software is still being designed and built. Alpha testing is the first phase of formal testing, during which the software is tested internally using white-box techniques. Beta testing is the next phase, in which the software is tested by a larger group of users, typically outside of the organization that developed it. The beta phase is focused on reducing impacts on users and may include usability testing.

After beta testing, the software may go through one or more release candidate phases, in which it is refined and tested further, before the final version is released.

Some software, particularly in the internet and technology industries, is released in a perpetual beta state, meaning that it is continuously being updated and improved, and is never considered to be a fully completed product. This approach allows for a more agile development process and enables the software to be released and used by users earlier in the development cycle.

Stages of development

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Pre-alpha

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Pre-alpha refers to all activities performed during the software project before formal testing. These activities can include requirements analysis, software design, software development, and unit testing. In typical open source development, there are several types of pre-alpha versions. Milestone versions include specific sets of functions and are released as soon as the feature is complete.[citation needed]

Alpha

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The alpha phase of the release life cycle is the first phase of software testing (alpha is the first letter of the Greek alphabet, used as the number 1). In this phase, developers generally test the software using white-box techniques. Additional validation is then performed using black-box or gray-box techniques, by another testing team. Moving to black-box testing inside the organization is known as alpha release.[1][2]

Alpha software is not thoroughly tested by the developer before it is released to customers. Alpha software may contain serious errors, and any resulting instability could cause crashes or data loss.[3] Alpha software may not contain all of the features that are planned for the final version.[4] In general, external availability of alpha software is uncommon for proprietary software, while open source software often has publicly available alpha versions. The alpha phase usually ends with a feature freeze, indicating that no more features will be added to the software. At this time, the software is said to be feature-complete. A beta test is carried out following acceptance testing at the supplier's site (the alpha test) and immediately before the general release of the software as a product.[5]

Feature-complete

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A feature-complete (FC) version of a piece of software has all of its planned or primary features implemented but is not yet final due to bugs, performance or stability issues.[6] This occurs at the end of alpha testing in development.

Usually, feature-complete software still has to undergo beta testing and bug fixing, as well as performance or stability enhancement before it can go to release candidate, and finally gold status.

Beta

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"Beta test" redirects here. For the 2016 film, see Beta Test (film). For the 2021 film, see The Beta Test.
See also: Software testing § Beta testing

Beta, named after the second letter of the Greek alphabet, is the software development phase following alpha. A beta phase generally begins when the software is feature-complete but likely to contain several known or unknown bugs.[7] Software in the beta phase will generally have many more bugs in it than completed software and speed or performance issues, and may still cause crashes or data loss. The focus of beta testing is reducing impacts on users, often incorporating usability testing. The process of delivering a beta version to the users is called beta release and is typically the first time that the software is available outside of the organization that developed it. Software beta releases can be either open or closed, depending on whether they are openly available or only available to a limited audience. Beta version software is often useful for demonstrations and previews within an organization and to prospective customers. Some developers refer to this stage as a preview, preview release, prototype, technical preview or technology preview (TP),[8] or early access.

Beta testers are people who actively report issues with beta software. They are usually customers or representatives of prospective customers of the organization that develops the software. Beta testers tend to volunteer their services free of charge but often receive versions of the product they test, discounts on the release version, or other incentives.[9][10]

Perpetual beta

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Some software is kept in so-called perpetual beta, where new features are continually added to the software without establishing a final "stable" release. As the Internet has facilitated the rapid and inexpensive distribution of software, companies have begun to take a looser approach to the use of the word beta.[11]

Open and closed beta

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Developers may release either a closed beta, or an open beta; closed beta versions are released to a restricted group of individuals for a user test by invitation, while open beta testers are from a larger group, or anyone interested. Private beta could be suitable for the software that is capable of delivering value but is not ready to be used by everyone either due to scaling issues, lack of documentation or still missing vital features. The testers report any bugs that they find, and sometimes suggest additional features they think should be available in the final version.

Open betas serve the dual purpose of demonstrating a product to potential consumers, and testing among a wide user base is likely to bring to light obscure errors that a much smaller testing team might not find.[citation needed]

Release candidate

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Microsoft Windows 2000 Server Release Candidate 2 media

A release candidate (RC), also known as gamma testing or "going silver", is a beta version with the potential to be a stable product, which is ready to release unless significant bugs emerge. In this stage of product stabilization, all product features have been designed, coded, and tested through one or more beta cycles with no known showstopper-class bugs. A release is called code complete when the development team agrees that no entirely new source code will be added to this release. There could still be source code changes to fix defects, changes to documentation and data files, and peripheral code for test cases or utilities.[citation needed]

Stable release

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Also called production release, the stable release is the last release candidate (RC) which has passed all stages of verification and tests. Any known remaining bugs are considered acceptable. This release goes to production.

Some software products (e.g. Linux distributions like Debian) also have long-term support (LTS) releases which are based on full releases that have already been tried and tested and receive only security updates.[citation needed]

Release

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Once released, the software is generally known as a "stable release". The formal term often depends on the method of release: physical media, online release, or a web application.[12]

Usually the released software is assigned an official version name or version number. (Pre-release software may or may not have a separate internal project code name or internal version number).

Release to manufacturing (RTM)

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Satya Nadella of Microsoft with the gold master disc of Gears of War 4

The term "release to manufacturing" (RTM), also known as "going gold", is a term used when a software product is ready to be delivered. This build may be digitally signed, allowing the end user to verify the integrity and authenticity of the software purchase. The RTM build is known as the "gold master" or GM[13] is sent for mass duplication or disc replication if applicable. The terminology is taken from the audio record-making industry, specifically the process of mastering. RTM precedes general availability (GA) when the product is released to the public. A golden master build (GM) is typically the final build of a piece of software in the beta stages for developers. Typically, for iOS, it is the final build before a major release, however, there have been a few exceptions.

RTM is typically used in certain retail mass-production software contexts—as opposed to a specialized software production or project in a commercial or government production and distribution—where the software is sold as part of a bundle in a related computer hardware sale and typically where the software and related hardware is ultimately to be available and sold on mass/public basis at retail stores to indicate that the software has met a defined quality level and is ready for mass retail distribution. RTM could also mean in other contexts that the software has been delivered or released to a client or customer for installation or distribution to the related hardware end user computers or machines. The term does not define the delivery mechanism or volume; it only states that the quality is sufficient for mass distribution. The deliverable from the engineering organization is frequently in the form of a golden master media used for duplication or to produce the image for the web.

General availability (GA)

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Milestones in a product life cycle: general availability (GA), end of life announcement (EOLA), last order date (LOD), and end-of-life (EOL)

General availability (GA) is the marketing stage at which all necessary commercialization activities have been completed and a software product is available for purchase, depending, however, on language, region, and electronic vs. media availability.[14] Commercialization activities could include security and compliance tests, as well as localization and worldwide availability. The time between RTM and GA can take from days to months before a generally available release can be declared, due to the time needed to complete all commercialization activities required by GA. At this stage, the software has "gone live".

Release to the Web (RTW)

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Release to the Web (RTW) or Web release is a means of software delivery that utilizes the Internet for distribution. No physical media are produced in this type of release mechanism by the manufacturer. Web releases have become more common as Internet usage has grown.[citation needed]

Support

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During its supported lifetime, the software is sometimes subjected to service releases, patches or service packs, sometimes also called "interim releases" or "maintenance releases" (MR). For example, Microsoft released three major service packs for the 32-bit editions of Windows XP and two service packs for the 64-bit editions.[15] Such service releases contain a collection of updates, fixes, and enhancements, delivered in the form of a single installable package. They may also implement new features. Some software is released with the expectation of regular support. Classes of software that generally involve protracted support as the norm include anti-virus suites and massively multiplayer online games. Continuing with this Windows XP example, Microsoft did offer paid updates for five more years after the end of extended support. This means that support ended on April 8, 2019.[16]

End-of-life

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See also: End-of-life product and Abandonware

When software is no longer sold or supported, the product is said to have reached end-of-life, to be discontinued, retired, deprecated, abandoned, or obsolete, but user loyalty may continue its existence for some time, even long after its platform is obsolete—e.g., the Common Desktop Environment[17] and Sinclair ZX Spectrum.[18]

After the end-of-life date, the developer will usually not implement any new features, fix existing defects, bugs, or vulnerabilities (whether known before that date or not), or provide any support for the product. If the developer wishes, they may release the source code, so that the platform may be maintained by volunteers.

History

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Usage of the "alpha/beta" test terminology originated at IBM.[citation needed] Similar terminologies for IBM's software development were used by people involved with IBM from at least the 1950s (and probably earlier). "A" test was the verification of a new product before the public announcement. The "B" test was the verification before releasing the product to be manufactured. The "C" test was the final test before the general availability of the product. As software became a significant part of IBM's offerings, the alpha test terminology was used to denote the pre-announcement test and the beta test was used to show product readiness for general availability. Martin Belsky, a manager on some of IBM's earlier software projects claimed to have invented the terminology. IBM dropped the alpha/beta terminology during the 1960s, but by then it had received fairly wide notice. The usage of "beta test" to refer to testing done by customers was not done in IBM. Rather, IBM used the term "field test".

Major public betas developed afterward, with early customers having purchased a "pioneer edition" of the WordVision word processor for the IBM PC for $49.95. In 1984, Stephen Manes wrote that "in a brilliant marketing coup, Bruce and James Program Publishers managed to get people to pay for the privilege of testing the product."[19] In September 2000, a boxed version of Apple's Mac OS X Public Beta operating system was released.[20] Between September 2005 and May 2006, Microsoft released community technology previews (CTPs) for Windows Vista.[21] From 2009 to 2011, Minecraft was in public beta.

In February 2005, ZDNet published an article about the phenomenon of a beta version often staying for years and being used as if it were at the production level.[22] It noted that Gmail and Google News, for example, had been in beta for a long time although widely used; Google News left beta in January 2006, followed by Google Apps (now named Google Workspace), including Gmail, in July 2009.[12] Since the introduction of Windows 8, Microsoft has called pre-release software a preview rather than beta. All pre-release builds released through the Windows Insider Program launched in 2014 are termed "Insider Preview builds". "Beta" may also indicate something more like a release candidate, or as a form of time-limited demo, or marketing technique.[23]

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Software release life cycle

View on Grokipedia
from Grokipedia
The software release life cycle (SRLC) is a structured sequence of phases that guides a software product from initial conception through development, testing, and deployment to eventual , ensuring progressive refinement, , and readiness for user adoption. This process typically encompasses key milestones such as pre-alpha, alpha, beta, release candidate, general availability, and production stages, allowing teams to identify and address issues iteratively while incorporating stakeholder feedback. Central to modern , the SRLC integrates with broader methodologies like Agile and to facilitate and delivery (CI/CD), enabling faster releases with reduced risk through automated testing and deployment pipelines. In the pre-alpha stage, focus lies on core planning, requirements gathering, and basic coding, producing an unstable prototype for internal review. The alpha stage involves initial internal testing to detect major bugs and validate functionality, followed by the beta stage, where limited external users provide real-world feedback on and . Subsequent phases, including release candidate for final stability checks and general availability for public launch, culminate in production release, where the software enters live environments with ongoing monitoring and updates. This lifecycle is essential for mitigating risks such as security vulnerabilities and deployment failures, particularly in an era of rapid innovation where global software spending exceeds $1 trillion annually. Best practices emphasize tools like , feature flags for controlled rollouts, and comprehensive to support post-release maintenance, ultimately enhancing software reliability and user satisfaction.

Planning and Design

Requirements Gathering

Requirements gathering is the foundational phase of the software release life cycle, where the needs of users, stakeholders, and the business are systematically identified, analyzed, and documented to establish the project's scope and objectives. This phase ensures that the software addresses real-world problems by capturing both explicit and implicit expectations, preventing misalignment in subsequent development stages. Effective requirements gathering involves collaboration between developers, customers, and end-users to build a shared understanding of the software's purpose and constraints. Key activities in requirements gathering include stakeholder interviews to elicit detailed needs, market analysis to assess competitive landscapes and user trends, use case definition to outline system interactions, and feature prioritization using established techniques. Stakeholder interviews facilitate direct communication, allowing analysts to probe for functional expectations and uncover hidden assumptions through structured questioning. Market analysis involves reviewing industry reports and competitor products to identify gaps and opportunities, ensuring the software remains viable in its target environment. Use cases are developed to describe scenarios of system usage, providing concrete examples of how users will interact with the software. For prioritization, the MoSCoW method—originating from the Dynamic Systems Development Method (DSDM)—categorizes requirements into Must-have (essential for delivery), Should-have (important but not vital), Could-have (desirable if time permits), and Won't-have (out of scope for the current release), helping teams focus on high-value features. The primary outputs of this phase are a comprehensive requirements specification document, user stories, and an initial project roadmap. The requirements specification document, often following standards like ISO/IEC/IEEE 29148:2018, delineates functional requirements (specific behaviors and operations the software shall perform) and non-functional requirements (qualities such as performance, security, and usability). User stories capture requirements in a narrative format from the end-user perspective, typically structured as "As a [user], I want [feature] so that [benefit]," to promote agility and clarity. The initial project roadmap outlines high-level milestones and dependencies, serving as a guide for resource allocation and timeline estimation. Tools such as Jira and support by enabling collaborative tracking and documentation. Jira facilitates issue creation for individual requirements, linking them to epics and sprints, while provides a centralized space for drafting specifications and attaching supporting artifacts. Best practices emphasize , where each is uniquely identified and linked to its origin and downstream elements like and testing, ensuring verifiability throughout the life cycle. Requirements should be unambiguous, complete, and ranked by priority to avoid conflicts and facilitate validation. Common challenges include —uncontrolled expansion of project scope due to evolving stakeholder demands—and ambiguous requirements that lead to misinterpretation. often arises from poor initial scoping or inadequate change controls, resulting in delays and cost overruns. To mitigate these, teams implement formal processes to evaluate additions against impact on time and budget, educating stakeholders on the consequences of modifications. For ambiguous requirements, prototyping serves as a validation tool, allowing early feedback to refine specifications without full implementation. These strategies help maintain focus and align the gathered requirements with the architectural design phase that follows.

Architectural Design

The architectural design phase in the software release life cycle transforms the gathered requirements into a technical blueprint that defines the system's structure, components, and interactions. This phase establishes the high-level framework for the software, ensuring it meets functional and non-functional needs while providing a foundation for subsequent development. Drawing directly from requirements, architects create models that outline how the system will be built, emphasizing modularity, interoperability, and maintainability to facilitate efficient implementation and evolution. Core elements of architectural design include high-level diagrams such as UML class diagrams for modeling static structures like classes and relationships, and data flow diagrams for visualizing how information moves through processes and entities. Technology selection involves choosing appropriate frameworks, languages, and tools aligned with project constraints, such as opting for scalable databases or cloud-native services. Modularity principles guide decisions between architectures like monoliths, which integrate all components into a single unit for simplicity in smaller applications, and , which decompose the system into independent, loosely coupled services to enhance and deployment flexibility—microservices often reduce interdependencies but introduce in . Key considerations encompass scalability to handle growing loads through techniques like horizontal scaling, security following guidelines such as implementing secure design principles to mitigate risks like injection attacks, performance metrics including response times and throughput targets, and seamless integration with legacy or third-party systems via APIs or . Adherence to standards like ISO/IEC/IEEE 42010 ensures consistent descriptions, specifying viewpoints, models, and rationales for stakeholders. Best practices involve conducting design reviews with multidisciplinary teams to validate assumptions and identify gaps early, developing proof-of-concept prototypes to test architectural viability without full implementation, and incorporating iterative feedback loops to refine designs based on stakeholder input. These practices promote robust, adaptable architectures. Common risks include over-engineering, where excessive complexity anticipates unlikely scenarios, leading to unnecessary development overhead, and incompatibility between components or systems, potentially causing integration failures. These are mitigated through iterative feedback loops that incorporate prototyping and reviews to balance thoroughness with practicality, ensuring the architecture remains aligned with requirements and feasible for development.

Development Phases

Pre-alpha Development

The pre-alpha development phase represents the earliest stage of hands-on in the release life cycle, where developers translate architectural designs into executable code to establish the core technical foundation. This phase emphasizes and iterative coding to explore and validate the feasibility of key technical elements, without concern for completeness, stability, or end-user interaction. It directly builds on outputs from the architectural design stage, such as system blueprints and component specifications, to guide initial efforts. Key activities during pre-alpha development include the initial implementation of foundational code, such as writing core modules, algorithms, and data structures essential to the software's functionality. Developers set up systems, often using for branching strategies that allow parallel experimentation and merging of code changes, to manage evolving prototypes effectively. Basic is introduced early to isolate and verify individual code units, ensuring that small components behave as expected amid frequent modifications. These practices support a high-velocity workflow focused on experimentation rather than refinement. Milestones in this phase are typically proof-of-concept builds, which demonstrate the viability of critical technical approaches—like novel algorithms or data handling mechanisms—through minimal viable prototypes that lack any polish or integration. These builds serve as internal checkpoints to confirm that the underlying technology aligns with project goals, often involving rough compilations or scripts to showcase functionality in a controlled environment. Unlike subsequent phases, pre-alpha outputs are not intended for broader review, remaining highly unstable and prone to frequent rewrites. Common tools facilitate this exploratory work, including integrated development environments (IDEs) like , which provide robust code editing, debugging, and refactoring capabilities to accelerate solo or small-team coding sessions. (CI) tools, such as Jenkins or GitHub Actions, automate initial builds and basic checks, enabling developers to iterate quickly by catching syntax errors or integration issues early without manual overhead. The emphasis on rapid iteration distinguishes pre-alpha from later development stages, prioritizing technical proof over polished deliverables or external validation.

Alpha Release

The alpha release represents the initial stage of formal testing in the software release life cycle, where the software achieves feature-complete status—meaning all intended functionalities have been implemented, albeit in an unpolished and potentially unstable form. This phase focuses on internal validation to uncover major defects and ensure basic operability before progressing to broader testing. Internal teams, including developers and (QA) personnel, conduct the testing in a controlled environment, emphasizing functionality rather than or experience refinements. High defect density is anticipated at this point, as the software is not yet optimized for external use. Testing during the alpha release encompasses to verify interactions between modules, to assess performance under stress, and systematic bug triage to prioritize and resolve issues. Tools such as for automated browser testing and Jira or for issue tracking facilitate this process, enabling efficient identification and documentation of defects. The scope prioritizes core features, edge cases, and end-to-end workflows, often incorporating both white-box techniques (examining internal code structures) by developers and black-box methods (focusing on external behavior) by QA teams. This internal-only approach allows for iterative hotfixes without external exposure, following preliminary stability checks from pre-alpha builds. Alpha releases are typically milestone-driven, with versions labeled sequentially (e.g., v0.1 or alpha-1) to track progress, culminating in exit criteria such as resolution of critical bugs, achievement of minimum test coverage thresholds (e.g., 80% for key functions), and meeting performance benchmarks. Best practices include implementing freezes to halt new feature additions and stabilize the build, alongside comprehensive of known issues in a or bug log for future reference. The phase generally lasts from a few weeks to several months, depending on project complexity and defect volume, allowing sufficient time for refinement without delaying the overall cycle.

Testing Phases

Beta Release

The beta release phase represents an external testing stage in the software release life cycle, where a near-complete version of the software is distributed to a limited group of end-users to validate , identify edge cases, and gather real-world feedback, building on the stability achieved during alpha testing for broader validation. This phase focuses on user acceptance testing (UAT) to ensure the product aligns with user needs and expectations in diverse environments. Beta releases come in several types, including closed beta, which restricts access to invited users such as loyal customers or stakeholders for targeted, confidential feedback; open beta, which allows public sign-up to collect broader insights on and ; and perpetual beta, an ongoing model for web applications where features are continuously added without a fixed stable release, as seen in services like and . Key activities during this phase include UAT conducted by external testers, feedback collection through surveys, bug reporting tools, and analytics platforms like to track user interactions, followed by iterative bug fixes and minor adjustments based on the input received. Milestones in the beta phase typically involve versioning such as "v1.0-beta," where the software achieves near feature parity with the final release but may include placeholders for unfinished elements or known limitations to manage scope. Best practices emphasize securing non-disclosure agreements (NDAs) for closed betas to protect intellectual property, implementing crash reporting tools like Sentry for automated error tracking, and planning durations of 2-6 weeks per cycle, often extending to 1-2 months total depending on feedback volume and complexity. Challenges in beta releases include managing user expectations to avoid frustration with incomplete features and ensuring data privacy compliance, particularly under regulations like the General Data Protection Regulation (GDPR), which requires explicit consent for collecting personal data from testers and anonymization to prevent breaches. To address these, teams often provide clear guidelines, incentivize participation, and prioritize critical issues while maintaining secure data handling protocols.

Release Candidate

A release candidate (RC) is a pre-release software version that represents the culmination of development efforts, where all planned features are fully implemented, the is polished, and only critical bugs are slated for resolution before final release. This phase focuses on validating the software's stability and readiness for production, with the build treated as largely frozen to minimize changes. If significant issues emerge during testing, multiple RCs may be iterated, such as v1.0-rc1 followed by v1.0-rc2, allowing targeted fixes without reopening broader development. Testing in the RC phase is rigorous and multifaceted, encompassing comprehensive to verify that recent fixes do not introduce new defects in existing functionality, security audits such as penetration testing to uncover vulnerabilities, and compatibility checks across diverse hardware, operating systems, and network environments. These activities ensure the software performs reliably under conditions approximating real-world usage. Penetration testing, in particular, simulates adversarial attacks to assess defenses against exploits like injection or authentication bypasses. Key milestones include stakeholder sign-off, where product managers, teams, and end-users review the RC against predefined criteria for functionality, performance, and usability. Deployment to staging environments—near-identical replicas of production setups—facilitates this validation by allowing tests in a controlled yet realistic context, identifying deployment-specific issues like configuration mismatches or scalability limits. Successful completion of these steps confirms the RC's viability for progression to release. Best practices emphasize automation to enhance efficiency and repeatability, such as integrating Jenkins pipelines for orchestrating regression, integration, and load tests within workflows. Versioning adheres to standards like semantic versioning (SemVer), which structures identifiers as MAJOR.MINOR.PATCH with pre-release tags (e.g., 1.0.0-rc.1) to clearly signal the build's status and precedence relative to the final version. These approaches reduce and accelerate feedback loops. The RC phase carries risks of discovering last-minute defects that could necessitate delays or rollbacks, potentially impacting timelines; mitigation involves confining cycles to short durations, often one to four weeks, to balance thoroughness with speed. The RC incorporates final refinements drawn from beta testing feedback to address any overlooked usability or gaps.

Release Deployment

Release to Manufacturing

Release to Manufacturing (RTM) represents the culmination of the , where the product achieves a , distribution-ready state for production and initial deployment, particularly in scenarios involving or hardware integration. This stage ensures the software meets all , , and compliance criteria before mass duplication, allowing it to be bundled with devices or packaged for retail sale. In practice, RTM is essential for boxed software products and embedded systems, where the final build undergoes rigorous validation to prevent defects in replicated copies. The core process at RTM involves finalizing the build through packaging, such as generating ISO images for optical disc mastering, followed by comprehensive quality assurance to support duplication. Teams coordinate with manufacturing partners to align on supply chain logistics, including the preparation of physical components like storage media and packaging materials. Licensing mechanisms, such as product key serialization, are integrated to enable unique activations and enforce usage terms, ensuring traceability and compliance in distribution. This phase typically follows the approval of a release candidate, confirming no outstanding critical issues remain. Key milestones include the RTM date, which officially denotes the completion of development and triggers production timelines, often incorporating a brief —typically weeks—for final validations before broader rollout. Best practices emphasize creating a verified master copy of the software, performing checks via checksums to confirm replication fidelity, and conducting legal reviews to validate distribution rights and protections. These steps mitigate risks like or unauthorized use, with additional safeguards such as virus scans on all artifacts outlined in the bill of materials. Historically, RTM originated during the era of distribution in the late , when software was pressed onto magnetic tapes or compact discs for enterprise and markets, a practice that persists today in specialized contexts like enterprise installations and device .

General Availability

General Availability (GA) represents the final stage in the software release life cycle where the product is officially launched to the public, fully vetted, and ready for widespread use after completing prior phases such as release candidate testing. At this point, the software is considered , feature-complete, and supported by comprehensive , enabling end-users to access it through standard distribution channels like app stores, vendor websites, or . This milestone ensures the product meets predefined quality thresholds, including resolution of major bugs and full documentation availability, marking the transition from internal validation to customer adoption. The launch typically involves coordinated announcements via press releases and marketing campaigns to generate awareness and drive initial uptake. For instance, companies issue detailed press releases highlighting key features, , and availability details, often distributed through platforms like or company pages to reach media and stakeholders. Marketing efforts may include targeted campaigns on , newsletters, and partnerships with distributors, ensuring the software is listed in major app stores or enterprise catalogs immediately upon GA declaration. These strategies aim to maximize visibility while aligning with the product's positioning as a mature offering. Prior to GA, the software must satisfy stringent criteria, including the establishment of support systems such as helpdesks, knowledge bases, and channels to handle inquiries and issues. This ensures rapid response times, often within hours for high-priority problems, and includes monitoring tools for real-time performance tracking. The GA date frequently signifies a major version release, such as 1.0, serving as a key milestone that organizations use to benchmark success through metrics like user adoption rates, with good rates typically reaching 70-80% in enterprise environments. Post-GA, teams track these indicators to evaluate and iterate on feedback. Best practices for GA rollout emphasize risk mitigation through progressive deployment strategies, such as canary releases, where the update is initially exposed to a small user subset—typically 5-10%—to detect anomalies before full rollout, thereby minimizing . Compliance with accessibility standards, like WCAG 2.1 Level AA, is also integral, ensuring the software is perceivable, operable, understandable, and robust for users with disabilities, which broadens market reach and avoids legal pitfalls. In enterprise contexts, GA often incorporates service level agreements (SLAs) guaranteeing uptime of at least 99.9%, with remedies like service credits for breaches, to foster trust in mission-critical applications. These practices, succeeding release to manufacturing for broader distribution, underscore a commitment to reliability and user-centric deployment.

Release to the Web

Release to the Web (RTW) represents the final deployment stage for web and cloud-based software, where the application is made instantly accessible online without relying on or traditional distribution channels. This model emphasizes direct uploading to hosting servers or infrastructures, ensuring global availability from the moment of launch. It is particularly suited to web applications, progressive web apps, and SaaS platforms that prioritize rapid iteration and user-centric delivery. The core process begins with uploading compiled code, static assets, and configurations to target servers, often leveraging cloud services like for storage. Content Delivery Networks (CDNs), such as AWS CloudFront, are then configured to cache and distribute content from edge locations worldwide, reducing latency and improving for end-users. Automated deployment pipelines integrate tools like Docker for containerizing applications—packaging them with dependencies for consistency—and for orchestrating container management, enabling zero-downtime rollouts across clusters. These steps ensure the software transitions smoothly from testing environments to production, with CI/CD tools like AWS CodePipeline automating the workflow. Key advantages of RTW include zero-touch updates, where backend changes propagate automatically to users without manual intervention or downloads, streamlining maintenance for SaaS products. It also facilitates , allowing developers to expose new features to select user segments via routing logic in CDNs or load balancers, enabling data-driven refinements before full rollout. This approach is especially common in SaaS ecosystems, where enhances competitiveness by accelerating feedback loops. Milestones in RTW center on the designated release date, when the application goes live, coupled with immediate activation of monitoring systems. Tools like provide real-time on key performance indicators such as response times, error rates, and user traffic, correlating these metrics directly to the deployment event for proactive issue detection. Best practices emphasize reliability and visibility: blue-green deployments maintain two identical production environments, routing traffic to the "green" (new) version only after validation, thus avoiding service interruptions during updates. Comprehensive rollback plans involve scripted reversions to previous versions via container versioning in , ensuring swift recovery from anomalies. Additionally, SEO optimization during deployment includes generating or updating sitemaps, implementing structured data, and ensuring fast load times through CDN caching to boost post-launch rankings and organic discoverability. Since the 2010s , RTW has emerged as the dominant release model for digital software, driven by providers like AWS and Azure that lowered infrastructure costs through pay-as-you-go pricing and eliminated expenses tied to physical duplication and shipping. This evolution has led to significant cost reductions in . For web-centric applications, RTW aligns seamlessly with general availability, emphasizing instantaneous digital mechanics over broader launch orchestration.

Maintenance and Support

Ongoing Maintenance

Ongoing maintenance encompasses the activities performed after the initial release to ensure the software remains functional, secure, and aligned with evolving user needs and environments. This phase typically begins immediately following general availability or release to the web, extending for several years until the software approaches end-of-support. It involves systematic updates to address defects, enhance performance, and mitigate risks, often consuming 60-80% of the total software lifecycle costs according to industry standards. The primary types of maintenance include corrective maintenance, which focuses on fixing bugs and errors discovered post-release through minor updates and patches; perfective maintenance, which introduces enhancements and new features via major version increments to improve functionality; and hotfixes, which are urgent corrective interventions for critical issues that could compromise system stability or security. Additionally, adaptive maintenance adjusts the software to new hardware, operating systems, or regulatory requirements, while preventive maintenance proactively addresses potential vulnerabilities to avert future problems. These categories are defined in the ISO/IEC/IEEE 14764 standard for maintenance. Key processes in ongoing maintenance revolve around patch management, which systematically identifies, prioritizes, acquires, tests, installs, and verifies updates to correct security flaws and functional issues across enterprise systems. Vulnerability monitoring relies on tracking entries in the (CVE) database to detect and respond to known threats promptly. User notifications are facilitated through mechanisms like automatic updates in applications and operating systems, ensuring seamless delivery without manual intervention and minimizing exposure to unpatched risks. These processes are outlined in NIST guidelines for enterprise patch management. Best practices emphasize structured approaches such as release trains, where updates are bundled and deployed on a predictable schedule to coordinate feature releases and fixes across teams, as implemented in frameworks like Scaled Agile. Maintaining is crucial, ensuring that new updates do not disrupt existing applications or data, thereby preserving user trust and system integrity. User impact assessments evaluate potential disruptions from updates, including performance effects and compatibility risks, to prioritize changes and inform rollout strategies. These practices help balance timely enhancements with minimal operational interference. The duration of ongoing maintenance often spans multiple years, aligned with the software's support lifecycle, during which metrics like mean time to resolution (MTTR) track the average time to address support tickets and restore functionality, aiming for reductions through efficient processes. Challenges include balancing innovation—such as integrating new features—against stability to avoid regressions, particularly in open-source projects where community contributions can introduce variability compared to the controlled environments of . In open-source models, decentralized decision-making accelerates fixes but complicates coordination, while proprietary systems prioritize vetted updates at the risk of slower responses.

End-of-Life

The end-of-life (EOL) phase of the software release life cycle the planned termination of all support, following years of ongoing to ensure stability and . During this stage, organizations shift focus from active development to facilitating user transitions, minimizing disruptions while addressing residual risks. This phase typically spans several months to years, emphasizing clear communication and to guide users toward newer alternatives. The EOL process unfolds in distinct phases, beginning with an announcement that provides advance notice—often 6 to 12 months or more—to allow users time for preparation. This notice details the timeline for support cessation and encourages migration planning. Following the announcement, an extended support phase may occur, where limited services such as critical security updates are available on a paid basis, typically lasting up to five years beyond mainstream support. Full retirement then follows, at which point no further updates, patches, or technical assistance are provided, rendering the software obsolete. Key activities during EOL include archiving source code and documentation in secure repositories to preserve institutional knowledge, often for compliance or potential revival efforts. Developers also create data migration tools to transfer user data to successor systems seamlessly, reducing downtime and data loss risks. Additionally, vendors issue security advisories warning end-users about vulnerabilities in unsupported versions and recommending immediate upgrades. Best practices for managing EOL involve establishing clear policies, such as Microsoft's Fixed Lifecycle Policy, which guarantees a minimum of 10 years of total support—five years of mainstream and five of extended—enabling predictable planning. Organizations should communicate timelines transparently, offer migration resources, and consider alternatives like open-sourcing the code to enable community-driven maintenance, thereby extending usability without vendor involvement. Regular audits of software inventories help identify approaching EOL dates early. The impacts of reaching EOL are significant, particularly heightened security vulnerabilities as unpatched software becomes a prime target for exploits, potentially leading to data breaches. Legally, EOL terminates warranties and support contracts, exposing users to compliance risks under regulations like GDPR, which can result in fines up to 4% of global annual revenue for non-compliance due to insecure systems. These factors underscore the need for proactive retirement strategies to mitigate operational and financial liabilities. A notable example is Microsoft , which reached EOL on January 14, 2020, after 10 years of support, ceasing all free security updates and leaving users reliant on paid Extended Security Updates for continued protection.

Methodologies and Practices

Waterfall Model

The represents a linear, sequential for managing the software release life cycle, where each phase must be completed before the next begins, ensuring a structured progression from initial planning to ongoing support. Originating in the as an adaptation of processes for large-scale systems, it emphasizes comprehensive documentation and predefined stages to minimize risks in complex projects. The model consists of distinct phases executed in strict order: , where user needs and specifications are gathered and documented; system , divided into high-level architecture and detailed component blueprints; implementation, involving coding based on the design; verification and testing, to validate functionality and detect defects; deployment, releasing the software to users; and maintenance, addressing post-release issues. Critical gates, typically requiring management sign-off on deliverables, separate these phases to enforce accountability and prevent progression without approval. This approach offers predictable timelines and clear milestones, facilitating accurate cost estimation and progress tracking through tools like Gantt charts for scheduling. Thorough at each gate supports and compliance, making it particularly suitable for regulated industries such as , where safety-critical requirements demand rigorous upfront planning and audit trails. However, its rigidity poses significant drawbacks, including inflexibility to evolving requirements, which can lead to costly rework if changes arise after early phases, and late defect discovery during testing, potentially delaying the entire project. In terms of cycle length, projects often span months to years due to the sequential nature, contrasting with iterative models that enable faster feedback loops and adjustments. Best practices include maintaining detailed records throughout to support gate reviews and using Gantt charts to visualize dependencies and timelines, ensuring alignment with project objectives. Unlike agile methods, which accommodate requirements changes through iterative cycles, Waterfall assumes stable specifications from the outset, prioritizing completeness over adaptability.

Agile and DevOps Approaches

Agile methodologies emphasize iterative development, collaboration, and adaptability in the software release life cycle, enabling teams to deliver functional increments frequently rather than in a single large release. The foundational document, the for , outlines four core values: individuals and interactions over processes and tools, working software over comprehensive documentation, customer collaboration over contract negotiation, and responding to change over following a plan. These values, established in 2001, guide practices that prioritize delivering value to customers through short development cycles. A key framework within Agile is Scrum, which structures work into fixed-length iterations called sprints, typically lasting 2 to 4 weeks, during which teams complete a potentially shippable product increment. Scrum defines specific roles, including the product owner who prioritizes the , the Scrum Master who facilitates the process and removes impediments, and the development team responsible for delivering the increment. Daily stand-up meetings, limited to 15 minutes, allow team members to synchronize activities, discuss progress, and identify blockers, fostering transparency and rapid issue resolution. DevOps extends Agile by integrating development and operations teams to automate and streamline the release process, promoting a culture of shared responsibility for software delivery. Central to DevOps are continuous integration/continuous delivery (CI/CD) pipelines, which automate building, testing, and deployment of code changes to ensure reliability and speed. Tools like Actions enable these pipelines by allowing workflows to be defined in repository configuration files, automating tasks such as testing and deployment upon code commits. Infrastructure as code (IaC) practices, exemplified by Terraform, treat infrastructure provisioning as version-controlled code, enabling reproducible environments and reducing manual configuration errors. In terms of release implications, Agile and distinguish between , where code is always in a deployable state but requires manual approval for production release, and , which automates the full release to production upon passing tests, minimizing human intervention. Feature flags, also known as feature toggles, support safe rollouts by allowing teams to enable or disable new features dynamically without redeploying code, thus isolating risks and enabling quick rollbacks if issues arise. Best practices in these approaches include tracking , a metric representing the amount of work completed per sprint, to forecast future progress and adjust planning accordingly. Retrospectives, held at the end of each sprint, involve the team reflecting on what went well and areas for improvement to continuously refine processes. Automation tools like Jenkins serve as open-source servers for orchestrating workflows, supporting plugins for diverse build, test, and deployment needs across projects. The adoption of Agile and has accelerated since around 2010, driven by the need for faster software delivery in dynamic markets, with elite organizations achieving deployments 182 times more frequently than low performers as of the 2024 DORA report. These methods yield benefits such as reduced time-to-market through iterative releases and early feedback loops, alongside improved via automated testing and monitoring, as evidenced by metrics from elite DevOps teams showing change failure rates of 0-5% and recovery times under one hour.

Historical Development

Origins in Traditional Models

The software release life cycle originated in the mid-20th century amid the rise of mainframe , where development processes were manual, labor-intensive, and geared toward large-scale systems for enterprise and scientific use. In the and early , software was often bundled with hardware and released through sequential stages of design, coding, and testing, primarily using assembly languages and punch cards for input. A pivotal example was IBM's System/360 family, announced in 1964 after development beginning in 1962, which involved over 1,000 programmers creating the OS/360 operating system—initially 1 million lines of code that expanded to 10 million—facing significant delays due to complexity and production issues like high failure rates in components. These releases emphasized compatibility and reliability for mainframes, with manual distribution via magnetic tapes and on-site installation by technicians, reflecting the era's focus on stability in mission-critical environments. Influences from traditional engineering disciplines shaped these early models, adapting structured approaches from civil and hardware engineering to manage the growing scale of software projects. In 1970, Winston W. Royce's paper "Managing the Development of Large Software Systems" proposed a linear process involving system requirements analysis, , preliminary and detailed design, coding and , integration and testing, and finally installation and checkout, drawing parallels to hardware manufacturing pipelines to address inefficiencies in large projects. This framework prioritized comprehensive documentation and upfront planning for documented, predictable outcomes, influencing subsequent practices in handling complex, multi-year developments like those at . Key events in the , including the "" highlighted at the 1968 NATO Conference on Software Engineering, exposed challenges such as cost overruns, unreliable code, and delivery delays in projects like NASA's space systems, prompting a shift toward techniques to impose discipline on coding. The conference report noted that software production lagged behind hardware advances, leading to formalized methods for . The terms "alpha" and "beta" originated from hardware testing conventions and were adopted for software releases in the 1960s, notably at , with alpha denoting internal functionality checks and beta external validation to gather limited user feedback before final shipment. By the , these terms became common across industries, including gaming. Milestones in commercial software underscored an emphasis on stability over rapid iteration, as seen with , the first electronic released in October 1979 for the after development starting in 1978, designed to provide stable functionality for business applications. Pre-internet, distribution relied on such as 8-inch floppy disks and magnetic tapes, mailed or sold through retailers, ensuring controlled releases but limiting accessibility compared to later digital methods.

Evolution to Modern Practices

The open-source movement in the 1990s marked a pivotal shift in software release practices, enabling collaborative, frequent updates that contrasted with proprietary models. released the initial in 1991 under an , fostering a community-driven development process where contributors worldwide submitted patches and participated in iterative releases. By the mid-1990s, this model supported regular kernel updates, with the community evolving from a loose, small-scale effort to structured merge windows for biannual stable releases, influencing broader adoption of and in release cycles. The 2001 Agile Manifesto further challenged traditional approaches by emphasizing iterative development, customer collaboration, and responsiveness to change, which shortened release cycles from monolithic projects to incremental deliveries. This declaration, signed by 17 software leaders, promoted practices like sprints and continuous feedback, laying the groundwork for more adaptive . In the , the rise of web applications exemplified these shifts through concepts like perpetual beta, as seen in Google's launch in 2004, which remained in beta for over five years to enable ongoing feature rollouts and user-driven improvements without fixed version boundaries. Around 2009, emerged as a cultural and technical movement, with tools like —introduced in 2005 but widely adopted post-2009—automating and bridging development and operations for faster, reliable releases. The 2010s saw accelerate these trends, with (AWS) launching in 2006 and providing scalable infrastructure that enabled / (CI/CD) pipelines, allowing teams to deploy code multiple times daily without hardware constraints. The from 2020 onward further hastened remote collaboration in , boosting adoption of distributed tools and zero-trust models to secure release processes amid widespread hybrid work. By 2025, current practices incorporate AI-assisted releases, such as , which surpassed 20 million users by mid-2025 and speeds up code generation to facilitate more frequent, error-reduced deployments. Shift-left integrates vulnerability scanning early in the software development lifecycle (SDLC), reducing remediation costs by addressing issues before production. Sustainable practices like green coding also gain prominence, focusing on energy-efficient algorithms and resource optimization to minimize the environmental impact of software operations throughout the release cycle. These evolutions have transformed release frequencies, with companies like achieving over 4,000 deployments per day through and automated pipelines, enabling rapid iteration while maintaining and reducing downtime risks.

References

  1. https://cpoclub.com/product-development/software-release-life-cycle/
  2. https://www.topdevelopers.co/blog/software-release-life-cycle/
  3. https://www.ibm.com/think/topics/devops
  4. https://rtslabs.com/software-release-life-cycle-guide
  5. https://www.harness.io/blog/lifecycle-software-releases-explained
  6. https://ieeexplore.ieee.org/document/720574
  7. http://www.math.uaa.alaska.edu/~afkjm/cs401/IEEE830.pdf
  8. https://dl.acm.org/doi/10.1145/3613372.3613410
  9. https://dl.acm.org/doi/10.1145/336512.336523
  10. https://www.agilebusiness.org/dsdm-project-framework/moscow-prioririsation.html
  11. https://standards.ieee.org/ieee/29148/6937/
  12. https://support.atlassian.com/jira/kb/using-jira-for-requirements-management/
  13. https://ieeexplore.ieee.org/document/9133081/
  14. https://ieeexplore.ieee.org/document/8994842
  15. https://www.tutorialspoint.com/software_architecture_design/data_flow_architecture.htm
  16. https://owasp.org/www-project-secure-by-design-framework/
  17. https://www.iso.org/standard/74393.html
  18. https://www.infoq.com/articles/avoid-architecture-pitfalls/
  19. https://www.techtarget.com/searchsoftwarequality/definition/release
  20. https://launchdarkly.com/blog/guide-what-is-a-software-release/
  21. https://www.browserstack.com/guide/alpha-testing
  22. https://www.gocodeo.com/post/alpha-testing-best-practices
  23. https://www.lambdatest.com/learning-hub/alpha-testing
  24. https://www.faqprime.com/en/alpha-testing/
  25. https://testsigma.com/blog/beta-testing/
  26. https://www.frugaltesting.com/blog/ultimate-guide-to-beta-testing-strategies-types-and-best-practices
  27. https://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html?page=4
  28. https://www.applause.com/blog/beta-testing-examples/
  29. https://www.centercode.com/blog/managing-tester-data-gdpr-4-questions
  30. https://semver.org/
  31. https://axify.io/blog/software-release-lifecycle
  32. https://owasp.org/www-project-web-security-testing-guide/v41/3-The_OWASP_Testing_Framework/1-Penetration_Testing_Methodologies
  33. https://learn.microsoft.com/en-us/dynamics365/guidance/implementation-guide/application-lifecycle-management-product
  34. https://www.techtarget.com/searchsoftwarequality/definition/staging-environment
  35. https://www.computerhope.com/jargon/r/rtm.htm
  36. https://learn.microsoft.com/en-us/previous-versions/windows/desktop/cc307420(v=msdn.10)
  37. https://swi.cs.vsb.cz/RUPLarge/core.base_rup/tasks/release_to_manufacturing_122CA83E.html
  38. https://blog.codinghorror.com/alpha-beta-and-sometimes-gamma/
  39. https://www.launchnotes.com/glossary/general-availability-ga-in-product-management-and-operations
  40. https://app.uxcel.com/glossary/general-availability
  41. https://www.prnewswire.com/news-releases/finops-open-cost-and-usage-specification-focus-1-0-is-generally-available-leading-clouds-launch-native-support-302176745.html
  42. https://www.businesswire.com/news/home/20251015105389/en/StackAdapt-Launches-General-Availability-of-Martech-Suite-Unifying-Email-Programmatic-and-First-Party-Data-in-One-AI-Powered-Platform
  43. https://fiixsoftware.com/maintenance-metrics/what-is-system-adoption-rate/
  44. https://openssf.org/press-release/2022/10/25/sigstore-announces-general-availability-at-sigstorecon/
  45. https://sre.google/workbook/canarying-releases/
  46. https://www.w3.org/TR/WCAG21/
  47. https://www.microsoft.com/licensing/docs/view/Service-Level-Agreements-SLA-for-Online-Services
  48. https://www.wgu.edu/blog/breaking-down-web-application-deployment-process2311.html
  49. https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/deploy-a-react-based-single-page-application-to-amazon-s3-and-cloudfront.html
  50. https://aws.amazon.com/blogs/apn/automating-secure-and-scalable-website-deployment-on-aws-with-amazon-cloudfront-and-aws-cdk/
  51. https://kubernetes.io/docs/concepts/workloads/controllers/deployment/
  52. https://aws.plainenglish.io/automate-static-web-application-deployment-on-aws-cloud-92c006ce032c
  53. https://docs.broadcom.com/doc/application-release-automation-with-zero-touch-deployment
  54. https://www.bettercloud.com/what-is-zero-touch-saas-management/
  55. https://vwo.com/saas-ab-testing/
  56. https://unbounce.com/a-b-testing/benefits-of-ab-testing/
  57. https://docs.newrelic.com/docs/apm/apm-ui-pages/events/record-deployments/
  58. https://newrelic.com/blog/how-to-relic/how-to-leverage-apm-insights-for-smarter-release-management
  59. https://docs.aws.amazon.com/whitepapers/latest/blue-green-deployments/introduction.html
  60. https://www.blazemeter.com/blog/blue-green-deployment-testing
  61. https://blog.inedo.com/devops/blue-green-deployment-best-practices
  62. https://cubecreative.design/blog/partners/web-development-seo-best-practices-developers-1755
  63. https://www.datamation.com/cloud/how-cloud-computing-changed-the-world/
  64. https://itif.org/publications/2021/06/01/secrets-cloud-computings-first-stage-action-agenda-government-and-industry/
  65. https://news.microsoft.com/download/archived/presskits/cloud/docs/The-Economics-of-the-Cloud.pdf
  66. https://csrc.nist.gov/pubs/sp/800/40/r4/final
  67. https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-40r4.pdf
  68. https://learn.microsoft.com/en-us/azure/devops/boards/plans/practices-that-scale
  69. https://learn.microsoft.com/en-us/dotnet/framework/migration-guide/version-compatibility
  70. https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=901613
  71. https://dl.acm.org/doi/full/10.1145/3652508
  72. https://dl.acm.org/doi/10.1016/j.jss.2021.111152
  73. https://learn.microsoft.com/en-us/lifecycle/overview/product-end-of-support-overview
  74. https://www.flexera.com/blog/it-visibility/understanding-end-of-life-eol-and-end-of-support-eos-a-comprehensive-guide/
  75. https://learn.microsoft.com/en-us/lifecycle/faq/fixed-policy
  76. https://learn.microsoft.com/en-us/lifecycle/policies/fixed
  77. https://www.archondatastore.com/blog/mainframe-decommissioning
  78. https://www.absolute.com/platform/security-information/vulnerability-archive
  79. https://www.ninjaone.com/blog/end-of-life-software-management/
  80. https://www.herodevs.com/whitepaper/best-practices-for-managing-eol-open-source-software
  81. https://www.svris.com/blog/understanding-the-cyber-risks-of-end-of-life-software/
  82. https://tuxcare.com/blog/eol-software/
  83. https://www.fyin.com/blog/the-dangers-of-end-of-life-software-what-you-need-to-know/
  84. https://learn.microsoft.com/en-us/lifecycle/products/windows-7
  85. https://www.umsl.edu/~hugheyd/is6840/waterfall.html
  86. https://www.cs.odu.edu/~zeil/cs333/latest/Public/waterfall/index.html
  87. https://www.forbes.com/advisor/business/what-is-waterfall-methodology/
  88. https://www.cs.toronto.edu/~sme/CSC444F/slides/L04-Lifecycles.pdf
  89. https://online.fit.edu/degrees/graduate/business/mba/it-project-management/understanding-waterfall-and-agile-marketing/
  90. https://agilemanifesto.org/
  91. https://scrumguides.org/scrum-guide.html
  92. https://www.atlassian.com/devops/what-is-devops
  93. https://www.redhat.com/en/topics/devops/what-is-ci-cd
  94. https://developer.hashicorp.com/terraform
  95. https://www.atlassian.com/continuous-delivery/principles/continuous-integration-vs-delivery-vs-deployment
  96. https://martinfowler.com/articles/feature-toggles.html
  97. https://www.jenkins.io/
  98. https://dora.dev/research/2024/dora-report/
  99. https://www.vm.ibm.com/history/360rev.pdf
  100. https://www.praxisframework.org/files/royce1970.pdf
  101. https://www.scrummanager.com/files/nato1968e.pdf
  102. https://archive.computerhistory.org/resources/access/text/2017/06/102738286-05-01-acc.pdf
  103. https://www.computerhistory.org/timeline/networking-the-web/
  104. https://www.kernel.org/doc/html/v4.14/process/2.Process.html
  105. https://agilemanifesto.org/history.html
  106. https://www.nytimes.com/2009/07/08/technology/companies/08google.html
  107. https://www.puppet.com/resources/history-of-devops-reports
  108. https://www.sei.cmu.edu/blog/devops-case-study-amazon-aws/
  109. https://www.microsoft.com/en-us/security/blog/2020/08/19/microsoft-shows-pandemic-accelerating-transformation-cyber-security/
  110. https://techcrunch.com/2025/07/30/github-copilot-crosses-20-million-all-time-users/
  111. https://www.checkpoint.com/cyber-hub/cloud-security/what-is-shift-left-security/
  112. https://www.ibm.com/think/topics/green-coding
  113. https://www.infoq.com/news/2013/06/netflix/
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