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Acceptance testing
Acceptance testing
Acceptance testing
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Acceptance testing of an aircraft catapult
Six of the primary mirrors of the James Webb Space Telescope being prepared for acceptance testing

In engineering and its various subdisciplines, acceptance testing is a test conducted to determine if the requirements of a specification or contract are met. It may involve chemical tests, physical tests, or performance tests.[1]

In systems engineering, it may involve black-box testing performed on a system (for example: a piece of software, lots of manufactured mechanical parts, or batches of chemical products) prior to its delivery.[2]

In software testing, the ISTQB defines acceptance testing as:

Formal testing with respect to user needs, requirements, and business processes conducted to determine whether a system satisfies the acceptance criteria [3] and to enable the user, customers or other authorized entity to determine whether to accept the system.

— Standard Glossary of Terms used in Software Testing[4]: 2 

The final test in the QA lifecycle, user acceptance testing, is conducted just before the final release to assess whether the product or application can handle real-world scenarios. By replicating user behavior, it checks if the system satisfies business requirements and rejects changes if certain criteria are not met.[5]

Some forms of acceptance testing are, user acceptance testing (UAT), end-user testing, operational acceptance testing (OAT), acceptance test-driven development (ATDD) and field (acceptance) testing. Acceptance criteria are the criteria that a system or component must satisfy in order to be accepted by a user, customer, or other authorized entity.[6]

Overview

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Testing is a set of activities conducted to facilitate the discovery and/or evaluation of properties of one or more items under test.[7] Each test, known as a test case, exercises a set of predefined test activities, developed to drive the execution of the test item to meet test objectives; including correct implementation, error identification, quality verification, and other valued details.[7] The test environment is usually designed to be identical, or as close as possible, to the anticipated production environment. It includes all facilities, hardware, software, firmware, procedures, and/or documentation intended for or used to perform the testing of software.[7]

UAT and OAT test cases are ideally derived in collaboration with business customers, business analysts, testers, and developers. These tests must include both business logic tests as well as operational environment conditions. The business customers (product owners) are the primary stakeholders of these tests. As the test conditions successfully achieve their acceptance criteria, the stakeholders are reassured the development is progressing in the right direction.[8]

Process

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The acceptance test suite may need to be performed multiple times, as all of the test cases may not be executed within a single test iteration.[9]

The acceptance test suite is run using predefined acceptance test procedures to direct the testers on which data to use, the step-by-step processes to follow, and the expected result following execution. The actual results are retained for comparison with the expected results.[9] If the actual results match the expected results for each test case, the test case is said to pass. If the quantity of non-passing test cases does not breach the project's predetermined threshold, the test suite is said to pass. If it does, the system may either be rejected or accepted on conditions previously agreed between the sponsor and the manufacturer.

The anticipated result of a successful test execution:

  • test cases are executed, using predetermined data
  • actual results are recorded
  • actual and expected results are compared, and
  • test results are determined.

The objective is to provide confidence that the developed product meets both the functional and non-functional requirements. The purpose of conducting acceptance testing is that once completed, and provided the acceptance criteria are met, it is expected the sponsors will sign off on the product development/enhancement as satisfying the defined requirements (previously agreed between business and product provider/developer).

User acceptance testing

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User acceptance testing (UAT) consists of a process of verifying that a solution works for the user.[10] It is not system testing (ensuring software does not crash and meets documented requirements) but rather ensures that the solution will work for the user (i.e. tests that the user accepts the solution); software vendors often refer to this as "Beta testing".

This testing should be undertaken by the intended end user, or a subject-matter expert (SME), preferably the owner or client of the solution under test and provide a summary of the findings for confirmation to proceed after trial or review. In software development, UAT as one of the final stages of a project often occurs before a client or customer accepts the new system. Users of the system perform tests in line with what would occur in real-life scenarios.[11]

The materials given to the tester must be similar to the materials that the end user will have. Testers should be given real-life scenarios such as the three most common or difficult tasks that the users they represent will undertake.[12]

The UAT acts as a final verification of the required business functionality and proper functioning of the system, emulating real-world conditions on behalf of the paying client or a specific large customer. If the software works as required and without issues during normal use, one can reasonably extrapolate the same level of stability in production.[13]

User tests, usually performed by clients or by end-users, do not normally focus on identifying simple cosmetic problems such as spelling errors, nor on showstopper defects, such as software crashes; testers and developers identify and fix these issues during earlier unit testing, integration testing, and system testing phases.

UAT should be executed against test scenarios.[14][15] Test scenarios usually differ from System or Functional test cases in that they represent a "player" or "user" journey. The broad nature of the test scenario ensures that the focus is on the journey and not on technical or system-specific details, staying away from "click-by-click" test steps to allow for a variance in users' behavior. Test scenarios can be broken down into logical "days", which are usually where the actor (player/customer/operator) or system (backoffice, front end) changes.[16]

In industry, a common UAT is a factory acceptance test (FAT). This test takes place before the installation of the equipment. Most of the time testers not only check that the equipment meets the specification but also that it is fully functional. A FAT usually includes a check of completeness, a verification against contractual requirements, a proof of functionality (either by simulation or a conventional function test), and a final inspection.[17] The results of these tests give clients confidence in how the system will perform in production. There may also be legal or contractual requirements for acceptance of the system.

Operational acceptance testing

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Main article: Operational acceptance testing

Operational acceptance testing (OAT) is used to conduct operational readiness (pre-release) of a product, service or system as part of a quality management system. OAT is a common type of non-functional software testing, used mainly in software development and software maintenance projects. This type of testing focuses on the operational readiness of the system to be supported, and/or to become part of the production environment.[18]

Acceptance testing in extreme programming

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Main article: Extreme programming

Acceptance testing is a term used in agile software development methodologies, particularly extreme programming, referring to the functional testing of a user story by the software development team during the implementation phase.[19]

The customer specifies scenarios to test when a user story has been correctly implemented. A story can have one or many acceptance tests, whatever it takes to ensure the functionality works. Acceptance tests are black-box system tests. Each acceptance test represents some expected result from the system. Customers are responsible for verifying the correctness of the acceptance tests and reviewing test scores to decide which failed tests are of highest priority. Acceptance tests are also used as regression tests prior to a production release. A user story is not considered complete until it has passed its acceptance tests. This means that new acceptance tests must be created for each iteration, or the development team will report zero progress.[20]

Types of acceptance testing

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Typical types of acceptance testing include the following

User acceptance testing
This may include factory acceptance testing (FAT), i.e. the testing done by a vendor before the product or system is moved to its destination site, after which site acceptance testing (SAT) may be performed by the users at the site.[21]
Operational acceptance testing
Also known as operational readiness testing, this refers to the checking done to a system to ensure that processes and procedures are in place to allow the system to be used and maintained. This may include checks done to back-up facilities, procedures for disaster recovery, training for end users, maintenance procedures, and security procedures.[22]
Contract and regulation acceptance testing
In contract acceptance testing, a system is tested against acceptance criteria as documented in a contract, before the system is accepted. In regulation acceptance testing, a system is tested to ensure it meets governmental, legal and safety standards.[23]
Factory acceptance testing
Acceptance testing conducted at the site at which the product is developed and performed by employees of the supplier organization, to determine whether a component or system satisfies the requirements, normally including hardware as well as software.[24]
Alpha and beta testing
Alpha testing takes place at developers' sites and involves testing of the operational system by internal staff, before it is released to external customers. Beta testing takes place at customers' sites and involves testing by a group of customers who use the system at their own locations and provide feedback, before the system is released to other customers. The latter is often called "field testing".[25]

Acceptance criteria

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According to the Project Management Institute, acceptance criteria is a "set of conditions that is required to be met before deliverables are accepted."[26] Requirements found in acceptance criteria for a given component of the system are usually very detailed.[27]

List of acceptance-testing frameworks

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See also

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References

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Sources

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  • A guide to the project management body of knowledge (PMBOK guide) (7th ed.). Newtown Square, PA: Project Management Institute. 2021. ISBN 978-1-62825-664-2.

Further reading

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[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Acceptance testing

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from Grokipedia
Acceptance testing is a formal testing process conducted to determine whether a satisfies its acceptance criteria, user needs, requirements, and business processes, thereby enabling stakeholders to decide whether to accept the system. It serves as the final verification phase before system release, ensuring that the software aligns with business goals, user expectations, and contractual obligations. This testing typically occurs in an operational or production-like environment and involves end-users, customers, or designated representatives evaluating the system's functionality, , , and compliance with specified standards. Key purposes include demonstrating that the software meets customer requirements, uncovering residual defects, and confirming overall system readiness for deployment. Acceptance testing encompasses various types, such as user acceptance testing (UAT), where end-users validate that the system meets business requirements from an end-user perspective; integration acceptance testing (IAT), which focuses on verifying integrated components or internal acceptance before full UAT; operational acceptance testing (OAT), which assesses , , and features; contract acceptance testing (CAT), focused on contractual terms; regulatory acceptance testing (RAT), ensuring compliance with laws and regulations; and alpha and beta testing, involving internal and external previews for feedback. These approaches emphasize collaboration between product owners, business analysts, and testers to derive acceptance criteria and design tests from business models and non-functional requirements like and . In standards, acceptance testing is integrated into broader processes, often following integration and , to provide assurance of and before live operation. It relies on documented test plans, cases, and results to support objective , with tools and experience-based practices enhancing efficiency in agile and traditional development contexts.

Fundamentals

Definition and Purpose

Acceptance testing is the final phase of , conducted to evaluate whether a meets predefined requirements, user needs, and criteria prior to deployment or operational use. This phase involves assessing the software as a complete to verify its readiness for production, often through simulated real-world scenarios that align with stakeholder expectations. As an incremental process throughout development or maintenance, it approves or rejects the based on established benchmarks, ensuring alignment with contractual or operational specifications. The primary purpose of acceptance testing is to confirm the software's functionality, , , and compliance with external standards from an end-user viewpoint, thereby mitigating risks associated with deployment. Unlike , which verifies individual components in isolation by developers, or , which examines interactions between modules, acceptance testing adopts an external, holistic perspective to validate overall system behavior against user-centric requirements. This focus helps identify discrepancies between expected and actual outcomes, ensuring the software delivers value and avoids costly post-release fixes. It plays a key role in catching defects missed in earlier testing phases, reducing overall project risks. Key concepts in acceptance testing include its black-box approach, where testers evaluate inputs and outputs without knowledge of internal code or structure, emphasizing observable behavior over implementation details. Stakeholders such as customers, end-users, buyers, and acceptance managers play central roles, collaborating to define and apply criteria for acceptance or rejection, typically categorized into functionality, , interface , overall , , and , each with quantifiable measures. Originating in the demonstration-oriented era of software testing during the late 1950s, when validation shifted from mere to proving system adequacy, acceptance testing was initially formalized through standards like IEEE 829 in 1983 and has since evolved with the ISO/IEC/IEEE 29119 series (2013–2024), which provides the current international framework for test documentation, planning, execution, and reporting across testing phases, including recent updates such as part 5 on (2024) and guidance for AI systems testing (2025).

Role in Software Development Lifecycle

Acceptance testing is positioned as the culminating phase of the software development lifecycle (SDLC), occurring after unit, integration, and system testing but before production deployment. This placement ensures that the software has been rigorously validated against technical specifications prior to end-user evaluation, serving as a critical gatekeeper that determines readiness for go-live by confirming alignment with business needs and user expectations. Within the SDLC, acceptance testing integrates closely with requirements gathering to maintain from initial specifications through to validation, ensuring that the delivered product adheres to defined criteria and mitigates risks such as by clarifying and confirming stakeholder expectations early in the process. It also supports post-deployment maintenance by providing a baseline for ongoing validation against evolving requirements, helping to identify potential operational issues that could lead to deployment failures or extended support needs. The benefits of acceptance testing extend to enhanced , greater stakeholder satisfaction, and improved cost efficiency, as it uncovers and functional gaps that earlier phases might overlook, thereby preventing expensive rework in production. Effective acceptance testing presupposes the completion of preceding testing phases, with all defects from unit, integration, and resolved to a predefined threshold. It further relies on strong to requirements documents, such as through a matrix, which links test cases directly to original specifications to ensure comprehensive coverage and verifiability.

Types of Acceptance Testing

User Acceptance Testing

User Acceptance Testing (UAT) is a type of acceptance testing performed by the intended users or their representatives to determine whether a satisfies the specified user requirements, business processes, and expectations in a simulated operational environment. This testing phase focuses on validating that the software aligns with end-user needs rather than internal technical specifications, often serving as the final validation before deployment. Key activities in UAT include scenario-based testing derived from use cases, where users execute predefined scripts to simulate real-world interactions; defects encountered during these scenarios; and providing formal sign-off upon successful validation. These activities typically involve non-technical users, such as business stakeholders or end-users, who assess functionality from a practical perspective without deep involvement in code-level details. Unlike other testing types, such as system or , UAT emphasizes subjective and over objective technical metrics like or performance benchmarks. It relies on user-derived scripts from business use cases to evaluate fit-for-purpose outcomes, prioritizing qualitative feedback on and intuitiveness. Best practices for UAT include setting up a dedicated staging environment that mirrors production to ensure realistic testing conditions, and providing training or guidance to participants to familiarize them with test scripts and tools. This approach is particularly prevalent in regulated industries like , where it supports compliance with standards such as those from FINRA for settlement systems, and healthcare, for example in validation of electronic systems for clinical outcome assessments as outlined in best practice recommendations. Success in UAT is measured through metrics such as pass/fail ratios of test cases, which indicate the percentage of scenarios meeting acceptance criteria, and user feedback surveys assessing satisfaction with and functionality. These quantitative and qualitative indicators help quantify overall readiness, with positive survey scores signaling effective user validation.

Operational Acceptance Testing

Operational Acceptance Testing (OAT) is a form of acceptance testing that evaluates the operational readiness of a or service by verifying non-functional requirements related to reliability, recoverability, , and supportability. This testing confirms that the system can be effectively operated and supported in a production environment without causing disruptions, focusing on backend infrastructure and IT operations rather than user interactions. According to the (ISTQB), OAT determines whether the organization responsible for operating the system—typically IT operations and systems administration staff—can accept it for live deployment. Key components of OAT encompass testing critical operational elements such as procedures, disaster recovery mechanisms, protocols, and monitoring and tools. These are assessed under simulated production conditions to replicate real-world stresses, including high loads and failure scenarios, ensuring the system maintains integrity during routine maintenance and unexpected events. In the context of ITIL 4's Service Validation and Testing practice, OAT integrates with broader service transition activities to validate that releases meet operational quality criteria before handover. Procedures for OAT typically include load and testing to evaluate under expected volumes, failover simulations to confirm and quick recovery, and validation of maintenance processes like patching and . These activities are led by IT operations teams, using tools and environments that mirror production to identify potential issues in supportability and resource utilization. For instance, backup testing verifies and restoration times, while disaster recovery drills assess the ability to resume operations within predefined recovery time objectives. The importance of lies in its role in mitigating risks of post-deployment and operational failures, which can be costly for enterprise systems handling critical data or services. By adhering to standards like ITIL 4 (released in with ongoing updates), organizations ensure robust operational handover, reducing incident rates and enhancing service continuity. In high-stakes environments, such as financial or healthcare systems, OAT supports improved metrics through thorough pre-release validation. Outcomes of OAT include the creation of operational checklists, detailed handover documentation, and acceptance sign-off from operations teams, facilitating a smooth transition to live support. These deliverables provide support staff with clear guidelines for ongoing maintenance, monitoring thresholds, and escalation procedures, ensuring long-term system stability.

Contract and Regulatory Acceptance Testing

Contract and Regulatory Acceptance Testing (CRAT) verifies that a meets the specific terms outlined in service-level agreements (SLAs), contractual obligations, or mandatory regulatory standards, ensuring legal and compliance adherence before deployment. This form of testing focuses on external enforceable requirements rather than internal operational fitness, distinguishing it from other acceptance variants by emphasizing verifiable fulfillment of predefined legal criteria. For instance, it confirms that the system adheres to contractual performance benchmarks, such as uptime guarantees or data handling protocols, and regulatory mandates like data privacy protections under the General Data Protection Regulation (GDPR). Key elements of CRAT include comprehensive for data privacy, detailed audit trails for , and validation of performance metrics explicitly stated in contracts or regulations. These audits often involve third-party reviewers, such as independent auditors or notified bodies, to objectively assess compliance and mitigate liability risks. In regulatory contexts, testing ensures safeguards like access controls and align with standards; for example, under GDPR, acceptance testing must incorporate data protection impact assessments, using anonymized test data to avoid processing real without necessity. Similarly, HIPAA Security Rule compliance requires testing audit controls and contingency plans to protect electronic protected health information (ePHI), with addressable specifications evaluated for appropriateness. Performance benchmarks might include response times or error rates tied to penalty clauses in contracts, ensuring the system avoids financial repercussions for non-compliance. The process entails formal planning with quantifiable acceptance criteria, execution through structured test cases, and culminating in official sign-offs by stakeholders, often including legal representatives. This is prevalent in sectors like and , where failure to comply can trigger penalties or contract termination; for example, post-2002 Sarbanes-Oxley Act () implementations require software systems supporting financial reporting to undergo acceptance testing for internal controls and auditability to prevent discrepancies in reported data. In payment processing, PCI-DSS compliance testing validates software against security standards for cardholder data, involving validated solutions lists maintained by the PCI Security Standards Council. Challenges arise from evolving regulations, such as the 2024 EU AI Act updates, which mandate risk assessments, pre-market conformity testing, and post-market monitoring for high-risk AI systems, including real-world testing plans and mitigation in datasets to ensure protection.

Alpha and Beta Testing

Alpha testing represents an internal phase of acceptance testing conducted within the developer's controlled environment, typically by teams or internal users simulating end-user actions to identify major functional and issues before external release. This process focuses on verifying that the software meets basic operational requirements in a lab-like setting, allowing developers to address defects such as crashes, interface inconsistencies, or performance bottlenecks without exposing the product to real-world variables. Beta testing, in contrast, involves external validation by a limited group of real users in their natural environments, aiming to collect diverse feedback on , compatibility, and remaining bugs that may not surface in controlled conditions. Participants, often selected from early adopters or target audiences, interact with the software as they would in daily use, providing insights into real-world scenarios like hardware variations or network issues. Feedback is commonly gathered through dedicated portals, surveys, or direct reports, enabling iterative improvements prior to full deployment. The primary differences lie in scope and execution: alpha testing is developer-led and confined to an in-house lab to catch foundational flaws, whereas beta testing is user-driven and field-based to validate broader applicability and gather subjective user experiences. Alpha occurs earlier, emphasizing technical stability, while beta follows to assess user satisfaction and edge cases. These practices originated from hardware testing conventions in the mid-20th century, such as IBM's use in the for product cycle checkpoints, but gained prominence in during the 1980s as personal expanded, with structured alpha and beta phases becoming standard for pre-release validation. Key metrics for both include the volume and severity of bug reports, defect resolution rates, and user satisfaction scores derived from feedback surveys, which inform the transition to comprehensive user acceptance testing upon successful completion. For instance, a high defect burn-down rate during alpha signals readiness for beta, while beta satisfaction scores from feedback often indicate progression to full release.

The Acceptance Testing Process

Planning and Preparation

Planning and preparation for acceptance testing involve defining the scope, assembling the necessary team, and developing detailed test plans and scripts to ensure alignment with project requirements. The scope is determined by reviewing and prioritizing requirements from earlier phases of the lifecycle, focusing on business objectives and user needs to avoid . According to the ISTQB Foundation Level Acceptance Testing , this step establishes the objectives and approach for testing, ensuring that only relevant functionalities are covered. Hands-on expertise in User Acceptance Testing (UAT) and Integration Acceptance Testing (IAT) planning is critical. This includes creating comprehensive test plans and realistic test scenarios. UAT scenarios validate that the system meets business requirements from an end-user perspective, while IAT scenarios focus on verifying that integrated components and interfaces function correctly together as an internal acceptance step before full UAT. Team assembly includes stakeholders such as end-users, business analysts, testers, and subject matter experts to foster collaboration; business analysts and testers work together to clarify requirements and identify potential gaps. The emphasizes this collaborative effort to enhance the quality of . Test plans outline the strategy, resources, schedule, and entry/exit criteria, while scripts detail specific test cases derived from acceptance criteria, often using traceable links to requirements for verification. Key preparation elements include conducting a to prioritize testing efforts based on potential impacts to processes, followed by creating representative test that simulates real-world scenarios without compromising sensitive . The ISTQB recommends risk-based testing to focus on high-impact areas, such as critical user workflows. Environment configuration is crucial, involving setups that mirror production conditions, including hardware, software, network configurations, and volumes to ensure realistic validation; for instance, deploying virtualized servers or cloud-based replicas to replicate operational loads. Test creation typically involves anonymized or synthetic datasets to support scenario-based testing, as outlined in standard practices for ensuring and compliance. Prerequisites for this phase include fully traceable requirements documented from prior SDLC stages, such as and , to enable bidirectional mapping between tests and specifications. Tools for planning often include test management software like Jira for tracking requirements and defects, and TestRail for organizing test cases and scripts, facilitating team collaboration and progress monitoring. Budget considerations encompass costs for user involvement, such as training sessions or compensated participation from business users, which can represent a significant portion of testing expenses due to their domain expertise. The ISTQB syllabus implies for these activities to maintain project viability.

Execution and Evaluation

Execution in acceptance testing involves hands-on running of predefined test cases to verify that the software meets the specified acceptance criteria. For User Acceptance Testing (UAT), this typically includes coordinating with business users who actively participate in executing scripted scenarios to simulate real-user interactions and validate business requirements. Integration Acceptance Testing (IAT) focuses on hands-on verification of integrated components and interfaces, often performed by internal teams before full UAT. Operational Acceptance Testing (OAT) employs simulated production setups to assess , recovery, and procedures. Defect management is a critical hands-on activity during execution. Defects are logged using specialized tools such as JIRA or Application Lifecycle Management (ALM) systems, prioritized based on severity and business impact, tracked throughout the resolution process, and verified through retesting after fixes. Defects are classified by severity—critical (system crash or ), major (core functionality impaired), minor (non-critical UI issues), or low (cosmetic flaws)—to prioritize resolution. This process enables iterative retesting, ensuring that resolved defects do not reoccur and that the system progressively aligns with requirements. Stakeholders, including product owners and quality assurance teams, play key roles: testers handle the hands-on execution, while reviewers assess business impacts and approve retests. Post-2020, remote execution has become prevalent, leveraging cloud platforms like AWS or Azure for distributed testing environments, which supports global teams and reduces on-site dependencies amid hybrid work trends. The execution phase duration varies depending on project complexity and test volume. Evaluation follows execution through pass/fail judgments against acceptance criteria, where tests passing indicate compliance and failures trigger defect analysis. Quantitative metrics, such as defect density (number of defects per thousand lines of code or function points), provide an objective measure of , with lower densities signaling higher reliability. Severity classification guides these assessments, ensuring critical issues block release until resolved, while test summary reports aggregate results for stakeholder review.

Reporting and Closure

In the reporting phase of acceptance testing, teams generate comprehensive test summaries that outline the overall execution results, coverage achieved, and alignment with predefined criteria. These summaries often include defect reports detailing identified issues, their severity, and status, along with root cause analysis to uncover underlying factors such as requirement ambiguities or integration flaws, enabling preventive measures in future cycles. Metrics dashboards are also compiled to visualize key performance indicators, such as pass/fail rates and test completion percentages, providing stakeholders with actionable insights into the testing outcomes. Closure activities formalize the end of the acceptance testing process through stakeholder sign-off, where key parties review reports and approve or reject the deliverables based on results. Lessons learned sessions are conducted to capture insights on process efficiencies, challenges encountered, and recommendations for improvement, fostering continuous enhancement in testing practices. Artifacts, including test scripts, logs, and reports, are then archived in a centralized repository to ensure traceability and compliance with organizational standards. These steps culminate in a go/no-go decision for deployment, evaluating whether the system meets readiness thresholds to proceed to production. The primary outcomes of reporting and closure include issuing a formal certificate upon successful validation, signifying that the software fulfills contractual or operational requirements, or documenting rejection with detailed remediation plans outlining necessary fixes and retesting timelines. This process integrates seamlessly with protocols, where acceptance outcomes inform controlled transitions, risk assessments, and updates to production environments to minimize disruptions. Modern approaches have shifted toward digital reporting via integrated dashboards, such as those in Azure DevOps, which provide capabilities for real-time test , automated defect tracking, and collaborative visualizations, addressing limitations of traditional paper-based methods like delayed feedback and manual aggregation.

Acceptance Criteria

Defining Effective Criteria

Effective acceptance criteria serve as the foundational standards that determine whether a meets stakeholder expectations during acceptance testing. These criteria must be clearly articulated to ensure unambiguous evaluation of the product's readiness for deployment or use. According to the ISTQB Certified Tester Acceptance Testing syllabus, well-written acceptance criteria are precise, measurable, and concise, focusing on the "what" of the requirements rather than the "how" of . Criteria derived from user stories, business requirements, or regulatory needs provide a direct link to the project's objectives. For instance, functional aspects might include achieving a specified test coverage level, such as 95% of user scenarios, while non-functional aspects could specify thresholds like response times under 2 seconds under load. The ISTQB emphasizes that criteria should encompass both functional requirements and non-functional characteristics, such as and , aligned with standards like ISO/IEC 25010. The development process for these criteria involves collaborative workshops and reviews with stakeholders, including business analysts, testers, and end-users, to foster shared understanding and alignment. This iterative approach, often using techniques like joint application design sessions, ensures criteria are realistic and comprehensive. matrices are essential tools in this process, mapping criteria back to requirements to verify coverage and forward to test cases for validation. Common pitfalls in defining criteria include vagueness, which can lead to interpretation disputes, , or failed tests requiring extensive rework. Such issues are best addressed by employing matrices to maintain bidirectional links between requirements and tests, enabling early detection of gaps. The ISTQB guidelines recommend black-box test design techniques, such as , to derive criteria that support robust evaluation without implementation details.

Examples and Templates

Practical examples of acceptance criteria illustrate how abstract principles translate into verifiable conditions for software features, ensuring alignment between user needs and system performance. These examples often draw from common domains like e-commerce and mobile applications to demonstrate measurable outcomes. In an login scenario, acceptance criteria might specify: "The user can log in with valid credentials in under 3 seconds." This ensures both functionality and meet user expectations under typical load. Similarly, for a mobile app's offline mode, criteria could include: "The app handles offline conditions by queuing user actions locally and synchronizing them upon reconnection without ." This criterion verifies resilience in variable network environments. Templates provide reusable structures to standardize acceptance criteria, facilitating collaboration in (BDD) and user acceptance testing (UAT). format, using syntax, is a widely adopted template for BDD scenarios that can be automated with tools like . For instance, a template for the e-commerce login might read: Feature: User Authentication Scenario: Successful login with valid credentials
Given the user is on the page
When the user enters valid username and password and clicks submit
Then the user is redirected to the dashboard within 3 seconds This structure promotes readable, executable specifications. For UAT sign-off, checklists serve as practical templates to confirm completion and stakeholder approval. A standard UAT checklist template includes items such as: verifying all test cases pass against defined criteria, documenting any defects and resolutions, obtaining sign-off from business stakeholders, and confirming the system meets exit criteria. These checklists ensure systematic closure of testing phases. Acceptance criteria vary by context, with business-oriented criteria focusing on user value and outcomes, while technical criteria emphasize attributes like and . Business criteria for an e-commerce checkout might state: "The user can complete a purchase and receive a confirmation within 1 minute." In contrast, technical criteria could require: "The processes transactions with 99.9% uptime and encrypts using AES-256." This distinction allows tailored verification for different stakeholders. A sample traceability table links requirements to acceptance tests, ensuring comprehensive coverage. Below is an example in table format:
Requirement IDDescriptionAcceptance CriterionTest Case IDStatus
REQ-001User functionalityLogin succeeds in <3s, 100% rateTC-001Pass
REQ-002Offline action queuingActions queue and sync without lossTC-002Pass
REQ-003Purchase confirmation sent within 1minTC-003Fail
This matrix tracks bidirectional from requirements to tests, aiding in impact analysis during changes. Recent advancements incorporate AI-assisted generation of criteria to address incompleteness in manual definitions, particularly since 2024. Tools leveraging large models (LLMs), such as those integrated with for generating scenarios from requirements, automate the creation of test cases. For example, one industrial study found that 95% of generated acceptance test scenarios were considered helpful by users. Generative AI models trained on software specifications can produce customized criteria for features like user , allowing refinement by teams.

Integration with Development Methodologies

In Traditional Models

In the , originally outlined by in 1970, acceptance testing serves as a late-stage phase occurring after system design, implementation, and , where the fully developed software is evaluated against predefined, fixed requirements to verify compliance with user needs and contractual obligations. This sequential approach structures the software development life cycle (SDLC) into distinct phases—requirements , design, coding, testing, and deployment—with acceptance testing typically integrated into or following the overall testing phase to ensure the system meets operational specifications before handover. Fixed requirements, documented upfront, guide the testing process, minimizing ambiguity but assuming stability in project scope from inception. Adaptations in traditional models emphasize comprehensive throughout the SDLC to support acceptance testing, including detailed test plans, matrices linking requirements to test cases, and formal acceptance criteria established during the requirements phase. Sequential from development to independent testing teams is standard, often involving specialists who conduct user acceptance testing (UAT) in a controlled environment simulating production. According to NIST guidelines for software projects, this handover includes buyer-provided resources like test and facilities to facilitate rigorous of functionality, , and . This methodology ensures thoroughness by allowing exhaustive validation against documented specifications, reducing risks in regulated environments such as large-scale government systems like defense networks, where structured acceptance testing has historically confirmed system reliability before deployment. However, it risks late discoveries of defects or requirement misalignments, as changes post-testing can necessitate costly rework across prior phases, potentially delaying projects by months. Dominant from the 1980s through the early 2000s in industries requiring predictability, such as and IT, the waterfall approach provided a stable framework for acceptance testing amid the era's emphasis on upfront planning over flexibility.

In Agile and Extreme Programming

In Agile methodologies, acceptance testing is integrated continuously throughout development sprints, rather than as a terminal phase, to ensure that increments of functionality align with user needs from the outset. This iterative approach emphasizes collaboration among cross-functional teams, including developers, testers, and product owners, to validate software against evolving requirements in short cycles. A key practice is Acceptance Test-Driven Development (ATDD), where acceptance tests are collaboratively authored prior to implementation, deriving directly from user stories to clarify expectations and drive feature development. In (XP), acceptance testing forms a of the methodology, with an on-site customer actively participating to define and validate tests that reflect . Automated acceptance tests serve as a comprehensive regression suite, executed frequently to maintain system integrity amid rapid iterations, and are often paired with practices like to enhance code quality and test reliability. This customer involvement, as outlined in foundational XP principles, ensures tests embody real-world usage scenarios, with practices evolving through the to incorporate more robust and integration strategies. Supporting these practices, (BDD) extends ATDD by focusing on behavioral specifications written in ubiquitous language, fostering shared understanding across teams and automating acceptance tests as executable examples. Tools like SpecFlow facilitate in .NET environments by translating Gherkin-based feature files into automated tests, enabling seamless integration with development workflows. Within pipelines, acceptance testing has been embedded in (CI/CD) processes since around 2015, automating test execution on every commit to catch issues early and support deployment readiness. The adoption of these approaches yields faster feedback loops, allowing teams to detect and address defects immediately after each sprint, thereby reducing rework and accelerating time-to-market. This alignment with dynamically changing requirements enhances overall and stakeholder satisfaction, as validated by empirical studies showing improved defect detection rates in iterative environments.

Tools and Frameworks

Overview of Acceptance Testing Frameworks

Acceptance testing frameworks are software tools specifically designed to facilitate the scripting, execution, and reporting of acceptance tests, enabling teams to verify that a fulfills predefined requirements. These frameworks emphasize to promote repeatability, reduce manual effort, and integrate seamlessly into development pipelines, often supporting both user acceptance testing (UAT) and scenarios. By automating test cases written in domain-specific languages or programming code, they help bridge the gap between technical implementation and non-technical stakeholder expectations. Several prominent open-source frameworks have become staples in acceptance testing due to their robustness and community support. Selenium, first developed in 2004 by Jason Huggins at as an internal tool for , remains a for browser-based UAT across multiple languages like , Python, and C#. Appium, originating in 2012 from and inspired by Selenium's WebDriver protocol, extends to native, hybrid, and mobile s on and Android platforms. Cucumber, created in 2008 by Aslak Hellesøy in Ruby to support (BDD), allows tests to be written in readable Gherkin syntax, fostering collaboration between developers, testers, and business analysts, and now supports languages like and . Playwright, released by in 2020, targets modern s with reliable end-to-end testing across , , and browsers, addressing limitations in older tools like flakiness in dynamic environments. Cypress, with roots in a 2014 project by Cypress.io, emerged as a JavaScript-based framework around 2017 for fast, real-time E2E testing directly in the browser, emphasizing developer-friendly . Robot Framework, initiated in 2005 by Pekka Klärck during his master's thesis at , is a keyword-driven tool ideal for acceptance test-driven development (ATDD), supporting extensible libraries for web, API, and desktop testing in Python. Key features of these frameworks include cross-platform compatibility, allowing tests to run on various operating systems and devices without major modifications, and native integration with (CI) tools such as Jenkins, GitHub Actions, or Azure DevOps for automated execution in pipelines. For instance, and offer WebDriver standards for browser control, while and provide reporting mechanisms that generate human-readable outputs like logs or artifacts for stakeholder review. Modern frameworks like and further enhance reliability through built-in waiting mechanisms and parallel test execution, reducing maintenance overhead in agile environments. Selecting an appropriate framework depends primarily on the application type under test. Web-centric applications benefit from , , or due to their strong browser automation capabilities; mobile apps require Appium's cross-platform mobile support; and BDD-oriented projects favor or for their emphasis on executable specifications. Desktop or API-focused acceptance testing might lean toward 's extensibility or specialized extensions in , ensuring alignment with the system's architecture and testing goals.

Selection and Implementation

When selecting an acceptance testing framework, key criteria include scalability, ease of maintenance, cost, and compatibility with development environments. Scalability is essential for handling large-scale test suites, where modular or hybrid frameworks like those built on Selenium or Playwright support parallel execution and integration with CI/CD pipelines to manage growing application complexity. Ease of maintenance favors frameworks employing patterns such as the Page Object Model (POM), which promote reusability and reduce script updates when application interfaces evolve. Cost considerations often pit open-source options, such as Selenium, which incur no licensing fees but may involve hidden expenses in training and infrastructure, against commercial tools like OpenText ALM (formerly HP ALM and Micro Focus ALM) and Atlassian JIRA, which demand substantial subscription fees but provide enterprise-grade features and support for test management, defect tracking, prioritization, and resolution in UAT and IAT processes. Compatibility ensures seamless operation across browsers, operating systems, and tools; for instance, Selenium's multi-language support makes it adaptable to diverse web and mobile environments, while commercial alternatives like BrowserStack offer built-in cloud compatibility for cross-device testing. Implementing an acceptance testing framework begins with setup, such as integrating with Jenkins for automated execution. This involves installing Jenkins plugins (e.g., for and reporting), configuring a via a Jenkinsfile to handle stages like workspace cleanup, Git checkout, virtual environment setup, prerequisite installations (including WebDriver for browsers), and test execution using frameworks like . Script development follows, leveraging WebDriver to create modular tests with robust locators (e.g., or CSS selectors) and explicit waits to synchronize with dynamic elements, often structured via POM for readability and reusability. Maintenance addresses test flakiness—common in due to timing issues—through regular updates to reflect UI changes, adoption of fluent waits, and integration with for continuous validation, ensuring long-term reliability. A notable case study is Netflix's adoption of SafeTest, a custom end-to-end testing framework introduced in 2024 to enhance front-end acceptance testing for its web applications. Building on prior tools like , SafeTest injects test hooks into application bootstrapping for precise control over complex scenarios, including and overrides, allowing scalable execution across React-based UIs without production impacts; this shift addressed limitations in off-the-shelf frameworks for enterprise-scale streaming services. Another example involves enterprises migrating to hybrid setups, where open-source bases like are extended with commercial integrations for robust acceptance validation in microservices architectures. Recent trends highlight a shift toward low-code tools to empower non-technical users in acceptance testing, bridging gaps in traditional coding-intensive approaches. Katalon Studio's 2023 updates, including version 9 with core library enhancements for performance and AI-powered features like TrueTest for test prioritization, enable record-and-playback scripting and scenarios, reducing dependency on developers. This evolution supports broader team involvement, with low-code platforms like Katalon integrating seamlessly into Agile workflows to accelerate acceptance cycles without extensive programming expertise.

Challenges and Best Practices

Common Challenges

One prevalent challenge in acceptance testing arises from unclear or ambiguous requirements, which often leads to scope creep as stakeholders introduce additional expectations during testing phases. This ambiguity can result in expanded test coverage beyond initial plans, complicating validation and extending timelines. For instance, acceptance criteria that lack specificity may cause teams to reinterpret functionalities, fostering disagreements and rework. Resource constraints, particularly the limited availability of end-users or subject matter experts, further exacerbate delays in user acceptance testing (UAT). Users frequently face competing priorities, reducing participation and leading to incomplete test execution, while training gaps hinder their ability to effectively evaluate system and alignment with business needs. In (OAT), similar issues manifest as failures in assessing under real-world loads, where insufficient resources prevent simulation of production-like volumes, revealing performance bottlenecks only post-deployment. Environment discrepancies between testing setups and production systems commonly produce false positives, where tests flag non-existent issues due to mismatched configurations, , or network conditions, eroding tester confidence and wasting effort on unnecessary fixes. In modern contexts, integrating acceptance testing with architectures amplifies these problems, as the distributed nature of services introduces complexities in end-to-end validation, such as inconsistent service interactions and dependency management. Post-2020, remote testing has introduced additional hurdles, with a 238% increase in VPN-targeted attacks between 2020 and 2022 complicating secure access to testing environments and raising privacy risks during distributed UAT sessions. These challenges collectively contribute to significant project impacts, including delays and escalated costs; industry analyses indicate that software projects have success rates around 30%.

Strategies for Success

Early stakeholder involvement is a foundational for successful testing, as it facilitates the collaborative definition of precise acceptance criteria that align with objectives and user expectations from the outset. This approach minimizes ambiguities and rework by incorporating feedback from product owners, business analysts, and end-users during requirements gathering, thereby enhancing test coverage and . Complementing this, automation of ensures consistent validation of core functionalities across iterations, reducing manual effort and enabling rapid feedback loops in dynamic development environments. Continuous for testing teams on evolving tools and methodologies further sustains proficiency, fostering a of that adapts to project complexities. Risk-based prioritization of tests represents another critical technique, where efforts are directed toward high-impact areas such as critical user paths or compliance requirements, optimizing and test efficiency. In agile contexts, adopting —integrating acceptance criteria validation earlier in the sprint cycle—has demonstrated effectiveness, as seen in teams that reported reduced defect densities through proactive requirement reviews and . Success in these strategies can be measured via key metrics, including defect escape rate, which tracks the proportion of issues surfacing post-release relative to those detected during testing (ideally targeting below 5% for mature processes), and on-time completion rates, assessing the percentage of test cycles finished within planned timelines to gauge . Emerging strategies leverage AI-driven test generation to address limitations in traditional manual processes, automating the creation of acceptance test cases from requirements or UI interactions for greater and reduced . Tools like Testim.io, which uses advanced AI features, exemplify this by using to stabilize tests against application changes, thereby improving efficiency in end-to-end validation. These innovations help mitigate incompletenesses in coverage by dynamically generating and prioritizing tests based on usage patterns. Implementing such strategies yields tangible outcomes, including improved (ROI) through cost efficiencies and accelerated delivery. For instance, organizations adopting comprehensive , encompassing acceptance testing, have achieved 20-30% cost savings and up to 50% faster release cycles in case studies, underscoring the value of integrated quality practices.

References

  1. https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nbsspecialpublication500-136.pdf
  2. https://istqb.org/certifications/certified-tester-acceptance-testing/
  3. https://standards.ieee.org/ieee/29119-5/11042/
  4. https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication500-180.pdf
  5. https://www.cs.odu.edu/~zeil/cs330/web/website/Lectures/testingcpp/pages/testforms.html
  6. https://www.testingreferences.com/testinghistory.php
  7. https://www.splunk.com/en_us/blog/learn/user-acceptance-testing-uat.html
  8. https://marker.io/blog/user-acceptance-testing
  9. https://teachingagile.com/sdlc/testing
  10. https://www.pmi.org/learning/library/requirement-traceability-tool-quality-results-8873
  11. https://moldstud.com/articles/p-understanding-the-importance-of-user-acceptance-testing-uat-for-successful-software-deployment
  12. https://ops.fhwa.dot.gov/seits/sections/section3/3_3_10.html
  13. https://www.browserstack.com/guide/acceptance-testing
  14. https://www.perforce.com/resources/alm/requirements-traceability-matrix
  15. https://www.projectmanager.com/blog/requirements-traceability-matrix
  16. https://glossary.istqb.org/en_US/term/user-acceptance-testing
  17. https://www.practitest.com/resource-center/article/what-is-uat-testing/
  18. https://tryqa.com/user-acceptance-testing-uat-checklist-best-practices-approach-example-templates/
  19. https://bugbug.io/blog/software-testing/qa-vs-uat/
  20. https://www.globalapptesting.com/blog/qa-vs-uat-testing
  21. https://www.finra.org/rules-guidance/technical-notice/t1-settlement-uat-042524
  22. https://pmc.ncbi.nlm.nih.gov/articles/PMC8964567/
  23. https://coaxsoft.com/blog/how-to-conduct-user-acceptance-testing
  24. https://disbug.io/en/blog/complete-guide-user-acceptance-testing-examples/
  25. https://glossary.istqb.org/en_US/term/operational-acceptance-testing
  26. https://wiki.en.it-processmaps.com/index.php/Service_Validation_and_Testing
  27. https://arxiv.org/abs/2501.01224
  28. https://ieeexplore.ieee.org/document/4122048
  29. https://www.edps.europa.eu/sites/default/files/publication/it_governance_management_en.pdf
  30. https://www.hhs.gov/sites/default/files/ocr/privacy/hipaa/administrative/securityrule/security101.pdf
  31. https://dl.acm.org/doi/pdf/10.1145/1064830.1064845
  32. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L_202401689
  33. https://glossary.istqb.org/en_US/term/alpha-testing
  34. https://nvlpubs.nist.gov/nistpubs/Legacy/IR/nbsir82-2482.pdf
  35. https://glossary.istqb.org/en_US/term/beta-testing
  36. https://www.centercode.com/guides/the-ultimate-guide-to-beta-testing
  37. https://www.functionize.com/automated-testing/beta-testing
  38. https://blog.codinghorror.com/alpha-beta-and-sometimes-gamma/
  39. https://english.stackexchange.com/questions/40013/whats-the-origin-of-beta-to-describe-a-user-testing-phase-of-computer-devel
  40. https://abstracta.us/blog/acceptance-testing/alpha-testing-vs-beta-testing/
  41. https://www.bcs.org/media/6358/swt-acceptance-testing-syllabus.pdf
  42. https://www.gasq.org/files/content/gasq/downloads/certification/ISTQB/ISTQB%20Acceptance%20Testing%20-%20Syllabus%20-%202019%20-%20GA.pdf
  43. https://www2.stardust-testing.com/en/a-quick-guide-to-operational-acceptance-testing
  44. https://www.atlassian.com/software/jira
  45. https://www.microfocus.com/en-us/products/alm-application-lifecycle-management/overview
  46. https://blog.qatestlab.com/2015/03/10/software-bugs-severity-levels/
  47. https://contextai.us/industry/how-cloud-based-testing-solutions-are-revolutionizing-remote-workflows/
  48. https://www.virtuosoqa.com/testing-guides/what-is-acceptance-testing
  49. https://www.browserstack.com/guide/what-is-defect-density
  50. https://istqb.org/?sdm_process_download=1&download_id=3345
  51. https://www.browserstack.com/test-reporting-and-analytics/what-is-test-reporting
  52. https://www.testdevlab.com/blog/what-is-root-cause-analysis
  53. https://learn.microsoft.com/en-us/azure/devops/report/dashboards/overview?view=azure-devops
  54. https://www.geeksforgeeks.org/software-testing/test-closure-in-software-testing/
  55. https://istqbfoundation.wordpress.com/2017/09/18/test-closure-activities/
  56. https://fullscale.io/blog/acceptance-testing/
  57. https://services.sjinnovation.com/what-is-uat
  58. https://www.idmworks.com/insight/uat-key-strategies/
  59. https://www.myndbend.com/how-organizations-implement-it-change-requests-and-move-through-post-implementation/
  60. https://learn.microsoft.com/en-us/azure/devops/test/user-acceptance-testing?view=azure-devops
  61. https://aqua-cloud.io/acceptance-criteria-in-testing/
  62. https://developer.android.com/topic/architecture/data-layer/offline-first
  63. https://cucumber.io/docs/gherkin/reference/
  64. https://www.testrail.com/blog/user-acceptance-testing/
  65. https://www.altexsoft.com/blog/acceptance-criteria-purposes-formats-and-best-practices/
  66. https://www.geeksforgeeks.org/software-testing/requirement-traceability-matrix/
  67. https://arxiv.org/html/2504.07244v1
  68. https://link.springer.com/chapter/10.1007/978-3-031-61221-3_20
  69. https://www.praxisframework.org/files/royce1970.pdf
  70. https://www.geeksforgeeks.org/software-engineering/waterfall-model/
  71. https://www.lucidchart.com/blog/pros-and-cons-of-waterfall-methodology
  72. https://agilealliance.org/glossary/atdd/
  73. https://ieeexplore.ieee.org/document/6149535/
  74. https://www.amazon.com/Extreme-Programming-Explained-Embrace-Change/dp/0201616416
  75. https://ptgmedia.pearsoncmg.com/images/9780321278654/samplepages/9780321278654.pdf
  76. https://agilealliance.org/glossary/bdd/
  77. https://dannorth.net/blog/introducing-bdd/
  78. https://www.globalapptesting.com/blog/testing-in-agile
  79. https://www.testingmind.com/a-brief-history-of-the-selenium-testing-framework/
  80. https://appium.io/docs/en/2.0/intro/history/
  81. https://jasonlemauk.com/exploring-the-origin-of-cucumber-how-why-and-who/
  82. https://applitools.com/blog/playwright-vs-selenium/
  83. https://canvasbusinessmodel.com/blogs/brief-history/cypress-io-brief-history
  84. https://www.eficode.com/blog/en/blog/robot-framework
  85. https://saucelabs.com/resources/blog/top-test-automation-frameworks-in-2023
  86. https://www.globalapptesting.com/blog/automation-testing-framework
  87. https://testlio.com/blog/test-automation-frameworks/
  88. https://www.accelq.com/blog/test-automation-tools/
  89. https://www.opentext.com/products/alm-quality-center
  90. https://www.testdevlab.com/blog/when-to-conduct-acceptance-testing
  91. https://2itesting.com/insights/testing-insights/user-acceptance-testing-is-broken
  92. https://www.diva-portal.org/smash/get/diva2:1988152/FULLTEXT01.pdf
  93. https://www.sciencedirect.com/science/article/abs/pii/S0164121224002760
  94. https://brainhub.eu/library/reasons-for-it-project-failure
  95. https://testlio.com/blog/what-is-acceptance-testing/
  96. https://www.testrail.com/blog/shift-left-testing/
  97. https://www.istqb.org/
  98. https://www.testrail.com/blog/qa-metrics-matter/
  99. https://www.testim.io/ai/
  100. https://www.testim.io/
  101. https://www.itconvergence.com/blog/boosting-roi-in-test-automation-optimization-ci-cd-and-test-reuse-strategies/
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