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Everything You Need to Know About Test Automation as a Service (TAaaS)

6 minutes, 24 seconds read

The enterprise-level digitization and adoption of DevOps and Agile have made test automation a necessity in today’s time. It reduces the time-to-market and hence the production cost. One can execute test automation on web/mobile/desktop application, performance, and APIs at once; generating a comprehensive report based on functionality, time, and build.

Test Automation as a Service is an on-demand automation offering that overrules manual testing. But before, let’s look at key problems with manual testing-

  • It demands manual effort during release/enhancement.
  • Manual testing requires greater resources.
  • Testers usually avoid lengthy testing because of time and resource constraints.
  • It has a limited scope of tests and cannot accomplish in-depth testing. In other words, manual testing has lesser coverage. 
  • It requires testing the application on multiple computers, mobiles, tablets, etc. with different configurations.
  • The scripts are not reusable, i.e. every time testing will require new scripts for instances like the change in OS version.

How Automation Speeds-up Testing by 70%?

Testing automation not only reduces manual efforts but also speeds-up the entire testing process. Here’s how.

  • It cuts down the repetitive tasks/testing, which the test engineers used to do at the time of product release or enhancement.
  • TAaaS covers lengthy testing, which was unattended by manual testing.
  • It also increases the testing coverage with fewer resources.
  • It finds critical defects at an early stage of testing.
  • Its scripts are reusable. Testers need not code new scripts every time for system upgrades and OS version changes. Tests can recur without errors.

The following are the test automation tools categorized application-wise.

Web-based Application Automation

Selenium Webdriver is an open-source tool for automating web-based applications only. Users can test web applications using any web browser.

  • Types of OS for testing in Selenium: Windows, Mac, Linux
  • Browsers supported for testing: Mozilla Firefox, Internet Explorer, Google Chrome, Safari, Opera

Additional Resource: Selenium Testing Automation Framework

Mobile-based Application Automation

Appium is an open-source tool to test web applications running in mobile browsers. It also supports the automation of native and hybrid mobile applications developed for iOS and Android OS. Appium uses Selenium API to test the applications.

You can test a mobile application in just four steps-

  1. Write your test script on Eclipse.
  2. Connect your device to Computer (PC).
  3. Start Appium server.
  4. Run your script (test cases).

Appium supports Chrome browser for testing Android apps and Safari for iOS.

API Automation

Testing is difficult in Java as compared to dynamic languages like Ruby and Groovy. REST Assured is a Java library that provides a domain-specific language (DSL) for writing powerful, maintainable tests for RESTful APIs. Most of the web services are based on REST architecture. Everything is a resource in the RESTful web service. It is lightweight, scalable, and allows creating easy to maintain web apps. How it works-

  • REST Assured captures the (JSON) response of the API call.
  • It validates if the response status code is equal to 200.

Windows App Automation

Winium is a Selenium-based open-source automation framework for the Windows platform. You can test your Windows App following these steps-

  • Write your test script on Eclipse.
  • Start Winium Desktop Driver.
  • Set the path of application in the script.
  • Using “UISpy” inspect the elements.
  • Run your script (test cases).

Frameworks for Test Automation as a Service

A framework is a collection of reusable components that make the overall test execution and development easy and efficient. It is a custom tool designed by Framework Developers to simplify test automation processes.

A framework is a well-organized structure of components. For instance, one driver file executes an entire batch of commands without any manual intervention. The following are the types of frameworks along with the use scenarios specific to Test Automation as a Service protocol.

Data Driven Framework

This automation framework focuses on keeping test script logic and test data separate. For testing, it inputs data sets from a variety of sources like MS Excel Sheets, MS Access Tables, SQL Database, XML files, etc.

When the same test case needs to be executed multiple times with different data sets, the data-driven framework provides data to the test scripts.

Modular Driven Framework

Here, testers create test scripts for individual, small modules of the application. These small scripts (or test modules) can be combined into a master script to test specific scenarios or end-to-end testing. The test modules can also act as a library of functions to use in the future.

When applications contain a lot of modules, a modular framework is suitable for testing.

Keyword Driven Framework

This framework is also known as table-driven testing because it uses a table format to define keywords or action words for each function that the tester needs to execute. It’s a user-friendly framework. Test Engineers can develop test scripts even with limited knowledge of automation tools and programming language.

Behavior Driven Development Framework (Cucumber Framework)

It is a testing framework which supports Behavior Driven Development (BDD). It allows the tester to define application behavior in plain English and simple grammar as defined in Gherkin language. The following are the components of the cucumber framework.

Feature Files: It is an entry point to the cucumber tests. Here, the tester describes the test cases in a descriptive language like English. Feature files are important because they serve as an automation test script as well as live documents. A feature file can contain one or many scenarios. The following is a sample feature file.

#Author: your.email@your.domain.com

#Keywords Summary:

#Feature: List of scenarios.

#Scenario: Business rule through list of steps with arguments.

#Given: Some precondition step

#When: Some key actions

#Then: To observe outcomes or validation

#And, But: To enumerate more Given, When, Then steps

#Scenario Outline: List of steps for data driven as an Examples and <placeholder>

#Examples: Container for s table

#Background: List of steps run before each of the scenarios

#””” (Doc Strings)

#| (Data Tables)

#@ (Tags/Labels): To group Scenarios

#<> (placeholder)

#””

## (Comments)

#Sample Feature Definition Template

@tag

Feature: Title of your feature

I want to use this template for my feature file

  @tag1

  Scenario: Title of your scenario

Given I want to write a step with precondition

And some other precondition

When I complete action

    And some other action

And yet another action

Then I validate the outcomes

And check more outcomes

  @tag2

  Scenario Outline: Title of your scenario outline

Given I want to write a step with <name>

When I check for the <value> in step

Then I verify the <status> in step

Examples:

   | name | value | status |

   | name1 | 5 | success |

   | name2 | 7 | Fail    |

Apart from these testers also use Linear Scripting Framework and Hybrid Testing Framework for Test Automation.

Step Definitions: A Step definition is a small piece of code with a set pattern. The pattern links the Step Definition to all the matching steps. Cucumber executes a Step according to Gherkin Steps.

Test Runner: The JUnit runner uses the JUnit Framework to run cucumber. It is an open-source unit testing framework for Java. It is useful for writing and running repeat/reusable test cases. It requires a single empty class with an annotation-

@RunWith(Cucumber.class)

@CucumberOptions(features=”features”, glue = {“stepDefinitions”})

public class TestRunner {}

Also read – How to perform load testing on applications.

Best Practices for Creating an Effective Testing Framework

  • Integrate Appium and Selenium to cover mobile and web testing together.
  • Integrate REST Assured for API automation to ensure APIs are working as per set functionalities. It saves a great deal of time and resources.
  • Integrate Winium/AutoIt for testing standalone applications.
  • Integrate Cucumber for behaviour-driven development.
  • Use Page Object Model to create generic packages of common classes (codes) that can be used over all the test scripts. It helps to achieve reusability of codes.
  • Integrate JUnit to manage test cases and generate reports.
  • Integrate Maven or Jenkins to achieve continuous testing. Jenkins also helps to run the script for lengthy testing and generate extended reports delivered to all stakeholders. It is useful for tests that take hours to days to complete.

We specialize in business-specific test automation services. Drop us a word at hello@mantralabsglobal.com to streamline and accelerate your product/solution launch.

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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