In software testing, a test environment refers to the configuration of hardware, software, network setups, and other components needed to run and manage test cases. It mirrors the production environment where the application will eventually deploy; consequently, it enables testers to simulate real-world conditions and identify defects before the product reaches end users. By creating a controlled testing environment, software teams can ensure more accurate and reliable test results, making the process of identifying issues more efficient. You may also be interested in What is Test Data? Why is it Important?

Test Environment Setup: Key Areas

Setting up an effective test environment is crucial for ensuring that software testing is accurate and reliable. Here are the key areas that need attention:

1. Hardware Configuration: The servers, computers, or cloud infrastructure used for running tests. This ensures that your hardware can handle the load and meet the requirements of the software being tested.
2. Software and Tools: Installation of the necessary operating systems, applications, and testing tools such as test management software, bug tracking tools, and automation frameworks.
3. Network Setup: Configuring the network conditions that mimic the actual operating environment, including bandwidth, latency, and firewall settings.
4. Database Setup: Set up a clean, replicated database to simulate various scenarios during testing. This approach ensures that data integrity remains intact throughout the testing process.
5. Configuration Management: Managing different configurations across the environment ensures that tests are conducted in diverse setups, covering all potential production conditions.

Process of Software Test Environment Setup

The process of setting up a test environment involves the following steps:

1. Requirement Gathering: Understand the software requirements and determine the resources needed for testing.
2. Environment Design: Design the architecture of the test environment to ensure it simulates the production environment accurately.
3. Provisioning Resources: Set up the hardware, software, databases, and network configurations based on the test requirements.
4. Installation and Configuration: Install necessary software, tools, and frameworks. Ensure that all components work cohesively.
5. Test Execution: Perform testing based on the requirements and document results.
6. Environment Validation: Run a series of preliminary tests to confirm that the environment is stable and all components are functioning correctly.

Test Environment Management

Test Environment Management (TEM) involves managing the resources, configurations, and setups needed to perform software testing efficiently. Effective TEM ensures that the right resources are available at the right time, that configurations are consistent, and that testing remains stable and predictable.

Key responsibilities of test environment management include:

• Provisioning and Scheduling: Make sure the necessary resources are available as needed for various testing activities.
• Configuration Management : Handling changes and ensuring the team maintains correct environment setups.
• Monitoring and Reporting: Keeping track of environment performance and identifying any issues that may affect the testing process.

Test Environment Checklist

A well-defined checklist helps ensure that no component is overlooked during the test environment setup. Here’s an example of a test environment checklist:

1. Hardware and Software Requirements: Confirm that the hardware and software meet the necessary specifications for testing.
2. Network Configuration: Test the network setup to ensure connectivity and performance match production environments.
3. Testing Tools: Install and configure all tools for test management, automation, and defect tracking.
4. Database Setup: Verify that the database is correctly configured and data is loaded as required.
5. Backup and Recovery Procedures: Implement necessary backups for recovery purposes in case of test failures.
6. Access Control: Ensure proper configuration of user permissions, especially when testing sensitive environments.

Challenges in Setting Up Test Environment Management

Setting up and managing a test environment comes with several challenges:

1. Resource Availability: Limited availability of required hardware and software resources may delay testing activities.
2. Complexity in Replicating Production Environments: Accurately replicating the production environment can be difficult, especially with complex infrastructures.
3. Configuration Management: Ensuring that the environment remains stable and consistent across multiple test runs can be challenging.
4. Integration Issues: Configuring and integrating multiple tools and components can lead to compatibility issues that hinder testing efficiency.
5. Cost Management: Maintaining test environments, especially in cloud-based or high-demand testing scenarios, can be costly.

Best Practices for Setting Up Test Environment Management

To overcome the challenges mentioned above, here are some best practices for effective test environment management:

1. Automation: Automate the provisioning, configuration, and management of test environments to reduce human error and improve consistency.
2. Environment Versioning: Keep track of different environment versions to ensure compatibility with various software releases.
3. Cloud-Based Testing: Use cloud environments for scalable and flexible testing, especially for complex setups.
4. Collaboration Between Teams: Ensure good communication between development, testing, and operations teams to align on environment requirements.
5. Environment Virtualization: Utilize virtualization tools to replicate various production setups efficiently, making it easier to test across different configurations.

What is Test Bed in Software Testing?

The goal of a test bed is to integrate all components needed for testing and ensure that they function together properly before testers begin the actual testing. This setup allows testers to simulate production environments and catch potential issues early.

For example, when testing a web application, a test bed would include the browser versions, network conditions, and device configurations necessary to replicate how real users might interact with the application. Overall, a test bed plays a crucial role in maintaining consistency and reliability during testing by ensuring that tests run in conditions that closely mirror the actual user environment. It helps replicate real-world scenarios, allowing testers to accurately evaluate the software’s performance under expected conditions.

Conclusion

A test environment provides a controlled setting for conducting software testing. It replicates real-world conditions while preventing any impact on the production environment. It involves configuring hardware, software, networks, and other resources needed for testing. A well-defined test environment is essential for accurate and reliable test results.

Key areas of test environment setup include the configuration of hardware (e.g., servers, devices), software (e.g., operating systems, databases), and network configurations (e.g., routers, firewalls). Proper setup also includes test data preparation and configuring tools for test execution.

The process of setting up a test environment involves planning, infrastructure setup, data preparation, tool configuration, and verification. Test Environment Management (TEM) ensures that test environments are maintained, monitored, and optimized for testing needs. Challenges include resource limitations, maintaining environment consistency, managing test data, and ensuring compatibility of tools. Best practices for setting up test environments include automation, version control, and collaboration with DevOps teams. Additionally, a test bed is a specific subset of a test environment that includes all the resources needed to conduct particular tests, such as performance or security testing.

Both Selenium and Playwright provide powerful automation solutions for testing web applications, but they cater to different needs and use cases. Selenium has a long history of reliability and supports multiple browsers, programming languages, and test types. Playwright, on the other hand, targets modern web applications and delivers enhanced performance, stability, and advanced testing capabilities. Understanding the key differences between these two tools will help you make an informed decision based on your project requirements, team skill set, and testing goals.

Pros and Cons of Selenium Vs Playwright

Selenium

Playwright

15 Key Differences Between Selenium and Playwright

Conclusion

When comparing Selenium vs Playwright, the right choice depends on your specific automation needs:

• Selenium remains a great option for teams that require extensive browser support and need to work with multiple programming languages. Its large community and robust ecosystem make it a reliable choice for legacy applications or teams working with a wide range of browsers.
• Playwright, on the other hand, is a superior choice for modern web applications, offering faster test execution, simpler setup, and better handling of dynamic content. If your project focuses on modern web standards and cross-browser testing, Playwright’s performance and advanced features make it the better option.

For teams looking to optimize automation testing with modern tools, Playwright is a clear winner. However, Selenium continues to be the go-to tool for broader support and long-term reliability.

1. UiPath

UiPath is one of the most popular RPA platforms worldwide, known for its ease of use and scalability. It is widely used for automating repetitive tasks in various industries, from finance to healthcare. UiPath offers powerful AI-driven automation and is especially suitable for both small businesses and large enterprises.

Key Features

• Drag-and-Drop Designer: Enables users to build automation workflows without needing to write any code, making it accessible for non-technical users.
• AI Fabric Integration: Integrates AI and machine learning models into automation workflows for more intelligent decision-making and task execution.
• Orchestrator: A powerful tool for managing, monitoring, and controlling RPA bots at scale.
• Task Mining: Uses AI to discover automation opportunities by analyzing existing workflows, identifying inefficiencies.
• Cloud Automation: Flexible cloud deployment options, with hybrid models for on-premise and cloud infrastructures.
• Comprehensive Analytics: Advanced real-time analytics to track the performance of bots, improve processes, and optimize workflows.

Pros

• User-friendly interface ideal for both beginners and experts.
• Scalable solution for businesses of all sizes.
• Extensive integration capabilities with over 500 applications.
• Strong support and resources from a large community.

Cons

• High licensing costs for small and medium-sized businesses.
• More complex features may require technical expertise.
• Performance can slow down in large-scale deployments.

Download UiPath ( O ne of the best RPA Tools List)

2. Automation Anywhere

Automation Anywhere is an AI-powered RPA solution that provides a robust set of tools for businesses to automate complex processes across various industries. The platform is designed to be scalable, flexible, and user-friendly, offering both cloud-based and on-premise deployment.

Key Features

• IQ Bot: Uses AI and machine learning to automate tasks involving unstructured data such as invoices, contracts, and emails.
• Bot Store: A marketplace offering pre-built bots to automate common business processes, saving time on development.
• Automation Anywhere IQ: Provides intelligence to your automation by analyzing workflows and suggesting improvements.
• Centralized Control Room: For seamless bot deployment, monitoring, and management in one place.
• Cloud and On-Premise Options: Flexibility in deployment for businesses with specific infrastructure needs.
• Advanced Analytics: Provides detailed performance metrics and insights to optimize bot productivity and business impact.

Pros

• AI-driven automation for handling complex, cognitive tasks.
• Extensive bot library in the Bot Store.
• User-friendly interface, allowing for quick deployment.
• Powerful analytics and reporting for continuous process optimization.

Cons

• Expensive for small businesses or startups.
• Steep learning curve for new users unfamiliar with automation.
• Limited support for non-cloud enterprise deployments.

Download Automation Anywhere

3. Blue Prism

Blue Prism is an enterprise-grade RPA tools designed to automate complex business processes at scale. Known for its secure, scalable solutions, Blue Prism is favored by large corporations and industries that require high levels of security and compliance.

Key Features

• Advanced Security and Governance: Ensures robust compliance and security features for industries like healthcare, finance, and government.
• Digital Workforce: Provides a digital workforce that can handle end-to-end automation across systems and applications.
• Control Room: A centralized system for managing bots and workflows in real-time, ensuring smooth and scalable automation.
• Multi-Platform Automation: Supports automation across Windows, Linux, and Virtual Machines, giving businesses more flexibility in deployment.
• Process Studio: A visual interface that allows developers to design automated processes with object-oriented programming.
• Comprehensive Reporting and Auditing: Provides insights and detailed logs for compliance, performance monitoring, and audit trails.

Pros

• Highly secure with strong compliance features, ideal for regulated industries.
• Robust architecture for large-scale, enterprise automation.
• Scalable across a wide range of industries.
• Customizable and flexible for complex business processes.

Cons

• Steep learning curve for users unfamiliar with RPA.
• Expensive licensing costs can be prohibitive for small businesses.
• Initial setup and integration may take time.

Download Blue Prism

4. Pega Systems

Pega Systems integrates Robotic Process Automation (RPA) with Business Process Management (BPM). The platform enables businesses to automate customer-facing processes and streamline internal workflows, with no-code functionality for rapid deployment.

Key Features

• No-Code Interface: Ideal for business users, not requiring any coding expertise to create automation workflows.
• BPM Integration: Combines RPA with business process management for end-to-end process automation.
• AI and Machine Learning: Uses AI to handle cognitive tasks like decision-making and data interpretation.
• Real-Time Analytics and Reporting: Gain insights into process efficiency and optimization areas.
• Cloud and On-Premise Flexibility: Offers both deployment models to cater to different organizational needs.
• Process Mining: Provides tools for discovering and improving current business processes before implementing automation.

Pros

• No-code platform ideal for non-technical users.
• Combines RPA and BPM for holistic process automation.
• Strong AI-powered decision-making capabilities.
• Scalable for large enterprises.

Cons

• Costly for small businesses or organizations with limited budgets.
• Complexity in complex workflows, requiring deeper understanding of processes.
• Overkill for businesses with simpler RPA needs.

Download Pega Systems

5. WorkFusion

WorkFusion combines RPA, machine learning, and cognitive automation into a single platform. It is particularly suited for industries with high volumes of unstructured data, such as financial services, insurance, and healthcare.

Key Features

• AI-Driven RPA: Integrates machine learning models for self-learning bots that improve over time.
• Document Processing: Automates the extraction and processing of data from documents and forms.
• Cloud & On-Premise Deployment: Offers both deployment models for maximum flexibility.
• End-to-End Automation: Automates both front-end (customer-facing) and back-end (operations) processes.
• Robust Reporting & Analytics: Provides visibility into process performance and bot productivity.
• Real-Time Monitoring: Tracks bots’ actions and performance in real-time, with alerts and troubleshooting capabilities.

Pros

• Strong integration of AI and ML for intelligent automation.
• Ideal for industries with complex data and documents.
• Flexible deployment options for cloud or on-premise needs.
• Provides a unified platform for both RPA and cognitive automation.

Cons

• Expensive for smaller businesses or startups.
• Complex setup for advanced features.
• Can be difficult for organizations with simple automation needs.

Download WorkFusion

6. Kofax

Kofax offers a comprehensive document automation and RPA solution designed to streamline business processes, particularly in industries that rely heavily on document management, such as finance and healthcare.

Key Features

• Document Capture: Automates data extraction from documents, including invoices, contracts, and forms.
• OCR (Optical Character Recognition): Converts scanned or handwritten documents into editable and structured data.
• Cloud and On-Premise: Deployment options for flexibility and scalability.
• Low-Code Development: Quickly design and deploy automation processes without needing deep coding skills.
• AI-Powered Automation: Integrates AI to handle unstructured data and improve automation decision-making.
• Analytics and Reporting: Provides detailed insights into bot performance and operational metrics.

Pros

• Strong document automation capabilities with OCR.
• Low-code development interface simplifies bot creation.
• Scalable for businesses of all sizes.
• Ideal for document-intensive industries like banking, healthcare, and insurance.

Cons

• Not as versatile for non-document-related automation tasks.
• Can be cost-prohibitive for smaller businesses.
• Requires technical expertise for complex automation.

Download Kofax

7. NICE Systems

NICE Systems is a leading provider of customer service automation and RPA solutions, particularly suitable for organizations that require robust capabilities for automating customer interactions and operational tasks.

Key Features

• Customer Service Automation: Automates routine customer service tasks such as responding to queries and processing requests.
• AI and NLP Integration: Uses AI and Natural Language Processing for more intelligent and human-like interactions.
• Cloud-Ready: Fully optimized for cloud deployment to handle high-volume transactions.
• Process Mining: Provides insights into inefficiencies in existing processes to identify automation opportunities.
• Real-Time Reporting: Offers dashboards to monitor the performance of bots and track business KPIs.

Pros

• AI and NLP capabilities enhance customer interaction automation.
• Cloud-first platform that’s scalable and flexible.
• Advanced analytics and reporting for real-time insights.

Cons

• Complexity for small businesses or simple automation needs.
• Higher pricing can be a barrier for SMBs.
• Requires expert-level skills for complex tasks.

Download NICE Systems

8. Microsoft Power Automate

Microsoft Power Automate empowers businesses with seamless automation, especially those already using Microsoft products.

Key Features:

• AI Builder: Automates document processing and image recognition.
• Process Advisor: Identifies bottlenecks in workflows.
• Wide Connectivity: Integrates with 300+ third-party applications.
• Desktop Flows: Automates tasks on legacy systems.

Pros:

• Affordable for small businesses.
• Ideal for Microsoft ecosystem users.
• Beginner-friendly interface.

Cons:

• Limited advanced features compared to competitors.
• Heavily tied to Microsoft products.

Download Link: Download Microsoft Power Automate

9. Nintex RPA

Key Features:

Nintex is known for its low-code platform and user-friendly approach, making it accessible to non-technical users.

• Low-Code Development: Speeds up automation design.
• Workflow Automation: Automates complex, multi-step workflows.
• Cloud-Based Platform: Simplifies deployment and scaling.
• Document Generation: Automates the creation of business documents.

Pros:

• Easy to use for non-technical users.
• Strong focus on workflow automation.
• Affordable pricing options.

Cons:

• Limited customization for advanced users.
• Not ideal for large-scale enterprises.

Download Link: Download Nintex RPA

10. Kryon

Kryon focuses on simplifying automation with its Process Discovery feature, making it ideal for beginners.

Key Features:

• Process Discovery: Automatically identifies processes for automation.
• Hybrid Automation: Combines attended and unattended bots.
• Cloud Integration: Scalable and easy to deploy.
• Intuitive Design: User-friendly interface for automation creation.

Pros:

• Simplifies automation with discovery tools.
• Easy for beginners to adopt.
• Cost-effective for small to medium businesses.

Cons:

• Limited advanced features.
• Less suited for complex workflows.

Download Link: Download Kryon

Conclusion

From top rated list of RPA Tools choosing the right RPA tool is a crucial step toward achieving greater efficiency and operational excellence. Whether you’re a small business looking to automate basic tasks or a large enterprise needing complex, AI-driven automation, the tools listed above offer scalable and flexible solutions for various use cases.

Want to Learn RPA from Scratch?

If you’re new to RPA or looking to strengthen your foundation, we’ve got you covered! Check out our comprehensive RPA Tutorial Series , where we break down RPA concepts, tools, and practical implementation techniques in a beginner-friendly way.

Embark on your RPA journey today and build the skills to transform your career or business!

Welcome to the Comprehensive Robotic Process Automation Tutorial Series!

This series is designed to guide you step-by-step through the essentials of Robotic Process Automation (RPA), from understanding the fundamentals to successfully implementing RPA in your organization.

Article 1: Introduction to RPA: What It Is and Why It Matters

• Summary: This introductory article will provide an overview of Robotic Process Automation (RPA), explaining what it is, how it works, and why it’s gaining traction across industries. It will also highlight the key benefits of RPA, including cost savings, improved efficiency, and error reduction.
  • What is RPA?
  • How RPA is transforming industries
  • Key benefits: increased productivity, accuracy, and scalability
  • Use cases in different industries (Finance, HR, IT, etc.)

Article 2: Key Components of an RPA System

• Summary: In this article, we’ll dive into the core components of an RPA system. Readers will learn about the various tools and technologies involved in RPA, such as RPA bots, control room, orchestrator, and the development environment.
  • RPA Bots: Types (Attended vs. Unattended)
  • Control Room/Orchestrator: Managing bots and monitoring processes
  • Development Studio: Designing and building RPA workflows
  • Robotic Workflows: The process of automating tasks

Article 3: Understanding RPA vs. AI vs. Traditional Automation

• Summary: This article will compare and contrast RPA, Artificial Intelligence (AI), and traditional forms of automation. It will explain how RPA fits into the broader landscape of digital transformation and how it differs from or complements AI and machine learning.
  • Differences between RPA, AI, and traditional automation
  • How RPA handles repetitive tasks vs. AI’s capability for cognitive tasks
  • Combining RPA with AI for intelligent automation (e.g., document processing, chatbots)
  • Use cases that benefit from combining AI and RPA

Article 4: Choosing the Right RPA Tool for Your Organization

• Summary: This article will provide insights into choosing the best RPA tool for your business needs. It will discuss popular RPA platforms like UiPath , Automation Anywhere , and Blue Prism , and factors to consider when selecting a tool.
  • Overview of popular RPA tools
  • Key considerations when selecting an RPA platform (ease of use, scalability, pricing)
  • Evaluating vendors based on features like support, integration, and AI capabilities
  • Hands-on comparison of popular tools (feature-by-feature)

Article 5: Designing and Mapping Your First RPA Process

• Summary: This article will guide readers through the process of designing and mapping a basic RPA workflow. It will explain how to identify suitable tasks for automation, map the process flow, and set clear objectives for automation.
  • Identifying repetitive and rule-based tasks for automation
  • Mapping the process flow (using tools like flowcharts or BPMN)
  • Setting clear KPIs and success metrics
  • Designing the first automation workflow

Article 6: Building Your First RPA Bot

• Summary: In this article, we’ll walk through the steps of building a simple RPA bot using an RPA tool (e.g., UiPath or Automation Anywhere). This hands-on guide will cover bot creation, testing, and debugging.
  • Step-by-step guide to creating a bot
  • Selecting activities and actions (e.g., click, input, read data)
  • Running the bot in the development environment
  • Debugging common errors and troubleshooting tips

Article 7: Orchestrating and Managing RPA Bots in Production

• Summary: Once an RPA bot is built, it needs to be deployed, managed, and monitored in production. This article will focus on the Orchestrator and Control Room, where bots are scheduled, executed, and tracked.
  • Deploying bots to the Control Room/Orchestrator
  • Scheduling bots for execution
  • Monitoring bot performance and troubleshooting errors
  • Scaling bot deployments across multiple machines
  • Best practices for bot governance and security

Article 8: Best Practices for RPA Implementation and Deployment

• Summary: This article will cover best practices for successfully implementing RPA in an organization. It will provide advice on project planning, change management, and post-deployment optimization to ensure a smooth RPA rollout.
  • Steps to start an RPA implementation project
  • Involving key stakeholders (IT, business users, process owners)
  • Managing change and overcoming resistance
  • Post-deployment: optimizing and maintaining bots
  • Measuring ROI and continuous improvement

Article 9: Integrating RPA with Existing Business Systems

• Summary: RPA often needs to integrate with existing systems, applications, and data sources. This article will explore how RPA integrates with legacy systems, CRMs, ERPs, and cloud-based solutions, including challenges and solutions.
  • RPA integration with legacy systems (e.g., mainframe, custom software)
  • Integrating RPA with CRMs (e.g., Salesforce), ERPs (e.g., SAP)
  • Using APIs for seamless integration with third-party apps
  • Data handling: moving data between systems and ensuring data accuracy
  • Real-life examples of successful system integrations

Article 10: The Future of RPA: Trends, Opportunities, and Challenges

• Summary: The final article will look ahead to the future of RPA and how it’s evolving. It will explore emerging trends such as Intelligent Process Automation (IPA), AI-powered RPA, and robotic desktop automation. It will also touch on the challenges organizations face as they scale RPA.
  • Emerging trends in RPA: AI, Machine Learning, and NLP
  • Intelligent Process Automation (IPA) and its role in the future of work
  • RPA in the context of Digital Transformation
  • Addressing challenges like scalability, governance, and security
  • Predictions for RPA’s role in the workforce

This series will provide you with the knowledge and skills necessary to successfully implement RPA in your organization, whether you’re just starting or scaling your automation efforts. Enjoy the learning journey with Robotic Process Automation Tutorial!

This document attempts to address those concerns and presents a recommended testing approach from OpKey for Oracle E-Business Suite for multiple generations of the application. We have drawn from our experience of working with over 100+ global enterprises helping them test seamlessly their Oracle EBS and Oracle Cloud Applications.

Areas of Potential Break?

Integration Issues while testing Connections:

System started throwing error that ‘connection URL was of a specific format?

Why this issue occurs? The issue occurs because of protocol error. To resolve the issue, the connection URL should be of a certain format http(Oracle Host name>:<port>)

System has multiple connections going for it and changing all of that at a live production environment is a scenario nobody would want to get into.

Security Validation Issues

Security assignments experience access compliance breach and unexpected mismatches to policies making it an inevitable validation required to be covered at all the stages during the migration

Process-to-Integration Failure:

When creating an integration that includes a Process and an Integration, you cannot expose a WSDL on the Integrations side that is the same WSDL exposed on the Processes start message side. Instead, either use a different WSDL on the Integrations side or expose a REST interface.

Multi-Order Process Failure:

Online order creation system not being thoroughly tested for multiple orders. E2E cycles are required to be tested for multiple orders with multiple personalized items.

Releases/Patches not thoroughly tested

When it comes to managing your Oracle EBS releases, change is constant. And to survive – you need to take a proactive approach to change delivery. With the right tools to standardize and accelerate the way you deliver change – is the key to ensuring your organization’s competitive edge.

The Proven Continuous Testing Methodology for Oracle EBS Test Automation

OpKey is the Continuous testing platform for ERP applications designed keeping modern enterprises in mind. Powered by a patent-pending Artificial-Test-Intelligence (TM) engine, OpKey significantly accelerates test creation by 3X-5X compared to other Automation tools. With OpKey, agile teams now have a test Automation platform to create reliable Automation tests significantly faster and maintain those painlessly.Let us look at some of the other features of OpKey that help accelerate Oracle EBS Test automation

Extensive Technological Support

OpKey Surge provides unparalleled ERP Automated Testing experience with fully automated 500 + pre-built components and 1000+ test cases across Oracle EBS multiple module packages like Financials, SCM, Procurement, Manufacturing and HCM. Also offering the end to end visibility into change impact across the entire application ecosystem. OpKey Surge’s Continuous testing platform enables companies to automate testing for multiple generations of commonly used Oracle EBS applications like R12.1 and R12.2 packages and user interfaces, moreover test cases can be easily updated when a business process changes using OpKey’s Oracle EBS intuitive Test Recorder Engine.

Flexible Scripting Extensibility

Using OpKeyEBS’test builder, readable scriptless test cases are created in real time as the user exercises the application. This allows teams to take full advantage of test automation without additional effort. Test generator relieves "maintenance load" that undermines most test automation initiatives.

End to end Test Automation Across Ecosystem

OpKey’s continuous testing platform solves end to end testing challenges where it can get easily be integrated with different versions of ERP’s and third-party applications connected to EBS applications like Oracle Cloud, Salesforce, SAP or even other third party apps. Largest ERP support in the marketwith over 14+ ERP applications including SAP, Oracle, SFDC, Workday and Microsoft Business Central, etc. OpKey’s in-built Change Analysis and Impact Assessment across ecosystems to provide one seamless experience to testing teams.

OpKey AI-based engine enables enterprises to accelerate innovation, ensure continuous quality and delivery, and lower risk across the most complex Oracle EBS landscapes, massively reducing the time and effort needed for EBS automated testing. OpKey, with its special features like Patch ReleaseSupport, and End to End Change Management simplifies and accelerates testing for Oracle EBS applications achieving upto 80% test coverage for critical business processes.

1. Improve deployment frequency: The use of DevOps tools helps to create a CICD pipeline where the building of the application code, running of the automated test cases, and the deployment takes place in the sequence without any human intervention. Each of the steps in the workflow provides the transparency through the logs and progress bar.
2. Achieve faster time to market: Use of the automation tools and integration of these tools with other testing and deployment tools together orchestrate and speed up the product development that ultimately reduces the time to deliver the software to the market.
3. The lower failure rate of new releases: The modern automation tools have equipped to provide the complete insight of the application health, and visual logs representation depicting the code deployment or build failure or success. It reduces the rate of product failure of the new releases as tools are very good in tracking the flaws in the release over releases.
4. Shorten the lead time between fixes: The use of automation tools and the integration of these tools to form a product delivery pipeline reduces the lead time between the product fixes.
5. Improve mean time to recovery: DevOps culture and automation tools together help to improve mean time to recovery from failures.

There is an endless list of tools that can get used together to orchestrate the continuous integration, continuous deployment, and continuous delivery but, anyone needs to make to wise choice depending on the tools suitability, tools knowledge, and the scope of the organization projects utility. All tools are not compatible with each other. Therefore, the selection of the compatible tools is very important before any organization can think to implement the DevOps solution.

Key DevOps tools list and practices are as follows.

1) Source Code Repository:

A source code repository is a centralized repository where developers continuously check-in and check out code. The source code repository should be capable to manage the various code versions that are checked in so that developers do not overwrite each other’s work. It should maintain the author’s details, date, and time details for each version of the code. It should be capable to compare the code versions and outline the changes made in the code. It forms a major component of continuous integration as this is where the code is present that can be checked out for build and integration. The following are the popular source code repository tools.

• Git
• Subversion
• Cloudforce
• Bitbucket
• TFS

2) Build Server:

It is an automation tool where the code gets compiled after being checked out of the source code repository into the executable code base. The following are the popular build tools.

• Jenkins
• SonarQube
• Artifactory

3) Configuration Management:

It defines the server or an environment configuration. The following are the popular configuration management tools.

• Puppet
• Chef

4) Virtual Infrastructure:

AWS (Amazon Web Services), and Microsoft Azure are the best examples of virtual infrastructures. They are provided by the cloud vendors which sell infrastructure or platform as a service (PaaS). They have APIs that allow to programmatically create new machines with the help of the configuration management tools such as Chef and Puppet. They are also available in the form of private clouds such as VMware, and vCloud. Such virtual infrastructures enable us to execute a cloud on top of the hardware in the data center. Virtual infrastructures can be easily combined with automation tools which can empower the organizations practicing DevOps with the capability to configure a server without any manual interventions. We can easily test our brand-new code automatically by sending it to our combined cloud infrastructure followed by building the environment and run all of the tests without any human intervention.

5) Test Automation:

DevOps testing is completely focused on the automated testing within the build pipeline to ensure that the deployable build has passed the necessary tests, and it is ready to be deployed. Test automation makes us very confident about the recent build to get deployed on production without any human intervention. The following are popular tools.

• Selenium
• Water

6) Pipeline Orchestration:

It can be visualized as a manufacturing assembly line where the code checked-in by the developer gets build by the build tool and tested with the help of the automation tool. Further, the tested code gets deployed into production via deployment tool and made available to the limited set of users initially followed by the full set of developers based on the feedback from the initial set of users.

7) Unified Enterprise Software Development and Delivery:

VersionOne® ContinuumTM is an enterprise continuous delivery solution that can automate, orchestrate, and visualize the flow of change throughout the software delivery cycle. We have already discussed DevOps Continuum in detail in the previous chapter.

Conclusion

In this article, we discussed the various DevOps tools list that are used by organizations to implement the DevOps model.

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DevOps Culture

DevOps culture demands the following qualities among team members.

1. Increased collaboration among various team members. It acts as an opportunity where team members of various teams, such as the development team, testing team, operation teams, etc. work together with a single focus to make the product successful.
2. Discouraging the team members who work in silos. The team members who are working in silo adds very little value to the team as they build a virtual barrier in communication with other team members. Every successful project demands a good deal of communication as well as collaboration among team members as it is the key to build any project successfully.
3. Encouraging the shared responsibility of building any product. When we talk about collaboration then the shared responsibility is imperative. One cannot blame any team member if any module is not functioning well, instead, the team members should come forward to help other team members and make the product working as expected.
4. Emphasizing on forming the autonomous teams. Autonomy provides confidence to the team member through which they can work freely and deliver their best to the team to build the product quickly without compromising the quality.
5. Improving the quality of work through collaboration and early feedback. As the team is more focused on their work, the better will be the product quality. This equation is directly proportional and the collaboration factor adds more weights to the actual equation.
6. Valuing the feedback of every responsible team member towards product development. It is very important to act early on any feedback if it is making any impact on the product. Early we discover the defect or flaw; more time we can save in the product delivery to the end-user at later part of the project.
7. Increasing automation of the workflows and chains to save the time which was wasted due to delay in receiving feedback, dependency on a particular member in the team, etc.

The above points clearly state that the DevOps culture has many values that resemble the agile values as DevOps is an extension of agile methodology. Agile methodology is a more all-inclusive way of delivering products. In Agile development practices, teams measure the progress in terms of a working product. It has roles such as Product owners, team members (developers, testers, and UX people), and scrum master who work closely together with the same goals.

DevOps is an extension to Agile where it adds the operations’ as a team member with those responsibilities into the agile team. The DevOps progress is measured in terms of working software in the customer’s hands. The DevOps culture can be achieved only if the development team and the operations team break down the silos and collaborate, takes the shared responsibility for maintaining the system that executes the product and prepare the product to run on the system with increased feedback frequency on quality and delivery automation.

DevOps Tools

DevOps tools are the automation tools that help in creating an automated workflow or chain just like a manufacturing factory. It consists of configuration management, repository, builds systems, automation test tool, application deployment tool, version control, and monitoring tools. Continuous integration, continuous delivery, and continuous deployment require orchestration of these tools and it is a must that the selection of these tools should be compatible with each other in terms of integration. Some of the key tools and practices are as follows.

Source Code Repository: A source code repository is a centralized repository where developers continuously check-in and check out code. The source code repository should be capable to manage the various code versions that are checked in so that developers do not overwrite each other’s work. It should maintain the author’s details, date, and time details for each version of the code. It should be capable to compare the code versions and outline the changes made in the code. The following are the popular source code repository tools.

• Git
• Subversion
• Cloudforce
• Bitbucket
• TFS

Build Server: Build server is an automation tool where the code gets compiled after being checked out of the source code repository into the executable code base. The following are the popular build tools.

• Jenkins
• SonarQube
• Artifactory
• TeamCity

Configuration Management: It defines the server or an environment configuration. The following are the popular configuration management tools.

• Puppet
• Chef

Test Automation: DevOps testing is completely focused on the automated testing within the build pipeline to ensure that the deployable build has passed the necessary tests, and it is ready to be deployed. Test automation makes us very confident about the recent build to get deployed on production without any human intervention. The following are the popular tools.

• Selenium
• Water

Code Deployment: Code deployment tools help to automate the code deployment to different environments on target servers or machines. The following are the popular tools.

• IBM UrbanCode Deploy
• AWS CodeDeploy
• Juju
• Capistrano
• Travis CI
• GoCD
• Jenkins
• Octopus Deploy

Monitoring Tools: The monitoring tools in the DevOps help to monitor the activities such as volume of code check-in made by the developer over time, number of build failures and success, vulnerability scan of the build, etc. The following are the popular monitoring tools.

• Blue optima
• Check Marx
• Black Duck Scan

Conclusion

In this article, we discussed the values of the DevOps culture which comprises a combination of the DevOps culture and the automation tools.

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• The Year 2007: Patrick Debois who is a software development consultant focused on learning all IT aspects. He had taken many different IT roles over fifteen years where he gained a holistic understanding of IT. His various roles include developer role, network specialist role, system administrator role, tester role, and project manager role. He has also worked as a consultant for a large datacenter migration. He took charge of the testing where he had spent his lot of time with Development and Operation. He had performed a deep analysis of the differences between the working of the Development and Operation teams, where he noticed a lot of frustration with the challenges of managing work across these two groups on this datacenter migration as they were working in silos. Continuous integration has gained popularity in the agile community and the Development team has moved closer to the deployment, but still, the divide between the operations and development was in place. Such a situation has provided Patrick an idea to formulate a better way for these two teams to work together in collaboration.
• The Year 2008: In the Agile 2008 Conference, Andrew Shafer shared an idea about an agile infrastructure "birds of a feather" session. Patrick Debois noticed this post and went to the session. The idea of "birds of a feather" was received poorly and Andrew didn’t even show up to his discussion. But, Patrick found this as a common topic of interest and he was so excited to see that he is not alone who is looking for the solution to resolve the challenges of Development and Operation working together. Patrick tracked down Andrew and they started a Google group named Agile System Administration.
• The Year 2009: In the O’Reilly Velocity Conference in San Jose, John Allspaw (senior vice president of technical operations) at Flickr, and Paul Hammond (director of engineering) at Flickr, gave a presentation about "10+ Deploys per Day: Dev and Ops Cooperation at Flickr." The presentation focus was on how Development and Operation can collaborate to improve the software deployment. In Belgium, Patrick Debois followed the presentation via a live stream and he was very inspired to start his conference, #DevOpsDays. The conference acted as a mechanism to bring together an energetic group of forward-thinking minds who are trying to improve software deployment. This group of people kept their conversation going over Twitter with the hashtag #DevOpsDays. Later, ‘Days’ was dropped from the hashtag as an effort to save Twitter character space, and the hashtag became #DevOps.
• The year 2010: In the following year, DevOpsDays sessions were held in Australia and the U.S. Over the period, the DevOpsDays brigade kept on growing in many different countries and cities around the world. More and more people get united to find a solution and energized about DevOps until it had become a full-on popular movement.
• The year 2011: The DevOps movement kept on growing and it has been fueled by individuals and open source tools with very little or no attention from analysts or vendors. Further in the year 2011, the movement began to go mainstream that came to the attention of analysts Cameron Haight from Gartner and Jay Lyman of 451 Research. By this time, Big vendors had already started to market DevOps.
• The year 2012: By 2012 DevOps Days was still a very hot topic continued to grow across the globe.
• The year 2013: In the year 2013, the public curiosity for DevOps information had inspired many authors to write books on the DevOps topic. DevOps books such as The Phoenix Project by Gene Kim, Kevin Behr, and George Spafford and Implementing Lean Software Development by Mary and Tom Poppendiek are the popular example of DevOps book in the year 2013.
• The year 2014: By the year 2012, many big organizations such as Target, Nordstrom, and LEGO became some of the first organizations to bring the DevOps into the enterprise.
• The Year 2015 and onward: The journey of future of DevOps has continued further that is still evolving to cater to every day growing and changing needs of many organizations. Still, they are many challenges which are faced by the organization in implementing the DevOps, but those challenges result in an opportunity to add something new to this DevOps topic. It is still evolving, and in the future, it will further deliver many fruitful results to the organizations.

Conclusion

In this article, we discussed the history and future of the DevOps culture across the globe.

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The growth of the DevOps maturity model depends on continuous learning from teams as well as organization management. As the capabilities and skills grow, more will be the ability to handle issues of scale and complications. There are numerous phases to the DevOps maturity model, the key phases are explained below.

Waterfall or SDLC (Software Development Lifecycle) Development

Before DevOps maturity to hit the organizational workflow of continuous integration, development teams were consuming lots of time to write code for four to six months. After the development completion, the teams would merge their code to prepare the release. The complexity that has been added due to many code versions along with many changes result in delaying the actual integration that can take up to months to complete. SDLC has a separate testing phase too where a considerable of time, effort, and money have been spent to gain confidence with the integrated code to release. Therefore, such a process was very fruitless.

Continuous Integration

Continuous integration is the DevOps model that enables the quick integration of the newly developed code with the existing application code which is to be released to add a new module or functionality. The continuous integration approach helps to save a lot of time towards the release of code. In other words, Continuous integration is an agile engineering practice that originates from the Extreme Programming (XP) methodology. It requires automation to achieve the continuous integration that enables to execute continuous integration successfully. It is considered as the first step down the path toward the DevOps maturity model. The continuous integration process covers the following aspects of the project.

1. Checking the code into the repository.
2. Compiling the code after checking it out of repository to put into usable i.e. binary executable code.
3. Running the basic validation testing on the built code.

Continuous Delivery

The next step in the DevOps maturity model is the Continuous delivery, after the continuous integration. It operates on top of the continuous integration, as shown in the diagram. Continuous delivery helps to execute the following.

1. Add additional automation and testing to assert the compatibility of newly merged code with the actual application code line frequently.
2. It asserts the code that is ready to deploy without any human intervention.
3. It helps to make a practice of preparing the code base continuously in a ready-to-deploy state.

Continuous Deployment

Continuous deployment is different from the continuous delivery phase of the DevOps maturity model. It has considered the most advanced evolution of the continuous delivery phase. It is a practice of deploying the integrated code into production with almost no human intervention. Such a practice completely mitigates the risk of failures due to any manual interventions. It enforces the process to ensure that untested code can never be deployed into production. It enables the execution of the code through automated testing before it gets promoted to production. Initially, the released code is made available to a few users to get their automated feedback loop to monitor the quality and usage before the code gets promoted further. Many organizations such as Pinterest, Flicker, Netflix, Etsy, Amazon, IMVU, and Google are popularly known to truly practice continuous deployment, while others are often focused only on moving towards continuous delivery.

Conclusion

In this article, we discussed different Phases of the DevOps Maturity model and the limitations in the existing SDLC (Software Development Life Cycle). The flaws of the SDLC model were overcome with the use of automation tools and practicing continuous integration, continuous deployment, and continuous delivery.

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The vertical axis portrays the three levels of the DevOps delivery chain. They are as follows.

1. Continuous Integration: Application code integration to the product in iteration.
2. Continuous Delivery: Delivery of the application code to the Quality Assurance team for testing.
3. Continuous deployment: Deployment of the application code on target server or machine to run the product in various environments.

The DevOps community refers to organizations such as Amazon, Etsy, Flicker, IMVU, Netflix, Pinterest, and Google. These organizations practice DevOps culture and use automation tools. We can’t have DevOps without tools, and the automation tools cannot survive in the market without a strong supporting culture. DevOps culture is a blend of the tools, culture, and sense of evolution over the period. The main focus of the DevOps culture is to encourage collaboration between the development and operations team and improve software delivery through automation tools.

DevOps Continuum is an enterprise-scale continuous delivery platform that has used to speed up software development, reducing the software risk, and ensuring the quality of complex software product post-deployment. It has been designed to get easily integrated with the existing DevOps tools such as GIT, Jenkins, Bit Bucket, JIRA, etc., to minimize any disruption to the infrastructure. Simple integration of the DevOps tool helps the processes of the product development lifecycle to get executed in a single, and centralized platform. It allows the different teams like the development team, operations, testing, etc. to be empowered with real-time insight to improve product delivery speed, bottlenecks elimination, and reduce the product deployment costs.

DevOps Continuum Goals

DevOps Continuum is about the DevOps culture and the use of required automation tools to Improve the collaboration among all stakeholders from planning through product delivery and automation of the delivery process. The following are the goals of DevOps culture.

1. Improve deployment frequency: The use of DevOps tools helps to create a CICD pipeline where the building of the application code, running of the automated test cases, and the deployment takes place in the sequence. Each of the steps in the workflow provides the transparency of the logs and the progress.
2. Achieve faster time to market: Use of the automation tools and integration of these tools with other testing and deployment tools help to speed up the product development that ultimately reduces the time to deliver the software to the market.
3. The lower failure rate of new releases: The modern automation tools have equipped to provide the complete insight of the application health, and visual logs representation depicting the code deployment or build failure or success. It reduces the rate of product failure of the new releases as tools are very good in tracking the flaws in the release over releases.
4. Shorten the lead time between fixes: The use of automation tools and the integration of these tools to form a product delivery pipeline reduces the lead time between the product fixes.
5. Improve mean time to recovery: DevOps culture and automation tools together help to improve mean time to recovery.

The developers develop code every day that need to build for the testing. The automated testing ensures the code is working as expected, and it is ready to get deployed and released to a small number of users. The newly developed code has been monitored for a short period to make sure that there are no unforeseen problems with the code. After ensuring the code stability, the new code gets multiplied to the remaining users. Most of these steps in the DevOps culture, after planning and development, are accomplished without human intervention.

Overcoming the Challenges in DevOps Continuum

Several challenges need to be countered in DevOps Continuum to maintain a smooth DevOps culture as follows.

1. Proper training and focus: It is very important to have a vision that comes through proper training and focus. Lack of vision may result in not implementing the DevOps culture in the right direction which may end up in no gain to the organization. Organizations need to maintain the proper guidance and training to make people the real need of adopting the latest culture.
2. Bringing teams together: It is a proven fact that when various teams work together, the overall productivity reaches a peak. The collaboration among the development team, testing team, and operations team, etc. help to bring the complete idea of the product on the table and there will be time-saving due to miscommunication, the product could be built accurately in the first attempt, etc. Breaking the cultural barriers and making various teams work together can pave the way for business expansion and huge profits to the organizations.
3. Flexible tool orchestration options: The tools orchestration is very important to set up a delivery pipeline. Each automation tool that qualifies for the DevOps has its specifications and compatibility described. We should make a wise choice of the tools to omit the problem of tool integration. Emphasize using the tools which are easy to integrate with other tools instead of using the tools which have integration limitations and lack flexibility
4. Automation tools are equipped to provide the analytical data that can be put into one central repository. Such tools that can help in the creation of useful reports and metrics about the percentage change in customer ticket volume, deployment frequency, percentage change in user volume, percentage of passed and failed deployments over the time.

Conclusion

In this article, we discussed the DevOps Continuum, goals, and the challenges that are required to overcome to build a working DevOps culture.

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