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Post Category: Blog

Postman API Testing: Scalable and Reusable Test Strategy

Posted on by Layana Chandran

Introduction

Optimise Postman API testing with smart scripts, reusable logic, and dynamic variables for scalable, efficient, and reliable automated test workflows.

Postman is a popular and user-friendly platform for API testing. Although it’s easy to get started, you can go much further by streamlining and strengthening your testing process. Rather than treating each test as a separate task, you can build a smarter, more maintainable framework that saves time, improves consistency, and reduces the risk of errors.

This guide outlines three essential strategies to help you get more from Postman: writing intelligent scripts, reusing logic efficiently, and managing data through effective use of variables.

Adding Smart Checks with Scripts

Postman lets you write JavaScript snippets—known as scripts—that run before or after an API request. These scripts help you automate tasks and validate responses without needing to manually inspect each result.

You can use pre-request scripts to generate timestamps, define dynamic variables, or create authentication tokens. Once a response comes back, test scripts can check status codes, confirm the presence of specific values, or verify response times.

For example, you can write a simple script to confirm that the response status is 200 and includes the correct data. By using these scripts, you remove the need for manual checks and ensure your tests stay consistent. This automation increases test reliability and frees up time for more complex validation work.

Reusing Test Logic to Save Time

As your API library grows, writing the same checks over and over becomes time-consuming and hard to maintain. Instead of duplicating code, you can reuse logic by placing shared scripts at the folder or collection level. This way, every request within that structure follows the same validation rules.

You can also create reusable snippets for common checks, like confirming that the response returns within a certain time or includes expected values. If you need to use the same piece of logic across multiple tests, store it in a variable and reference it when needed.

For instance, if you frequently check for a valid token in the response, you can write the logic once and call it wherever you need it. This approach makes updates easier—you only need to change the logic in one place—and ensures your checks remain consistent throughout the test suite.

Using Variables for Flexible Testing

Postman supports different types of variables that allow you to write flexible, reusable tests. By replacing hard-coded values with variables, you can adapt your tests to suit various environments or scenarios without constantly editing each request.

You can use environment variables to switch between development, staging, and production environments. For broader use, global variables work across all environments and collections. Collection variables focus on one collection, while local variables apply to individual requests or scripts.

Instead of updating every request when the server address changes, you can refer to a variable like {{base_url}}. After updating the variable once, all related requests automatically reflect the change. This method reduces human error and makes it easier to manage large test suites.

Best Practices for Better Testing

To take full advantage of Postman’s capabilities, group related requests in folders and apply shared scripts at that level. Use clear, descriptive names for your variables to make them easier to manage and understand. Store your collections in a version control system such as Git to track changes and support collaboration.

Review your scripts regularly, especially when you update your APIs or add new features. Also, make sure to protect sensitive information—avoid hard-coding tokens or passwords, and use environment variables with secure storage to keep data safe.

Conclusion

Postman offers much more than basic request execution. With the right techniques, it becomes a powerful platform for automated, efficient, and scalable API testing. By writing intelligent scripts, reusing logic, and using variables effectively, you can build a flexible and maintainable testing framework. These strategies not only reduce development time but also help your team deliver higher-quality software. Whether you’re just beginning or refining a mature suite, these practices will support a more structured and efficient testing process.

Need help improving your API testing strategy in Postman? Whether you’re after expert guidance, hands-on training, or a tailored framework review, our team is ready to support you. Contact us today and let’s build smarter, faster, and more reliable tests together.

API Testing with Postman & Newman: A Complete Guide

Posted on by Layana Chandran

Introduction

Streamline API testing with Postman and Newman for automation, CI/CD integration, and scalable test execution. Boost performance, reliability, and speed.

Modern software development relies heavily on effective API testing to ensure smooth and reliable system communication. Postman simplifies this process with its user-friendly interface and powerful features. For teams aiming to automate and scale their testing efforts, Newman—Postman’s command-line collection runner—offers the flexibility to run tests in any environment. This guide explores how Postman and Newman work together to make API testing more efficient and dependable.

Understanding API Testing

Application Programming Interfaces (APIs) act as intermediaries that facilitate interaction between different software components. API testing focuses on validating the functionality, performance, and security of these interfaces, ensuring they behave as intended. Unlike traditional user interface testing, API testing is both quicker and more dependable, making it an essential part of modern development practices.

Why Postman is Ideal for API Testing

Postman is widely appreciated for its intuitive design, enabling users to create, manage, and execute API tests with ease. Its graphical interface allows for the composition and execution of API requests without the need for extensive scripting. Once test cases are created, they can be saved and reused to maintain consistency throughout the testing process. Postman also allows users to organise API requests into collections, which can be managed more effectively with the help of configurable environments. These features are complemented by built-in reporting tools that provide insights such as response times, status codes, and validation outcomes, all of which contribute to ensuring optimal API performance and functionality.

The Role of Newman in API Testing

While Postman excels at manual testing, Newman brings automation to the table by running Postman collections from the command line. This capability is particularly beneficial when integrating API tests into continuous integration and continuous deployment (CI/CD) workflows, using platforms such as Jenkins, GitHub Actions, or Azure DevOps. Newman supports the parallel execution of tests across different environments and can generate structured reports that aid in thorough analysis and debugging.

Advantages of Using Newman

Newman’s scalability makes it ideal for executing large volumes of tests across various environments. It integrates seamlessly with CI/CD pipelines, facilitating faster release cycles by automating tests during development stages. By providing a standardised method of execution, Newman ensures consistent results, regardless of the environment or development team. Additionally, its flexible command-line options and compatibility with external scripts enable users to customise test execution according to their specific needs.

Building an API Testing Strategy with Postman & Newman

To build a strong foundation for API testing, organisations must adopt a structured approach. The first step involves designing meaningful test scenarios by identifying key functionalities and defining the expected outcomes. It is important to plan tests that cover functional, performance, and security aspects comprehensively.

Using Postman, developers can group related API requests into collections and configure them with relevant authentication methods, headers, and body parameters. Setting up environments such as development, staging, and production allows for flexible testing, and environment variables help streamline the use of recurring parameters.

Once the tests are defined, they can be executed in Postman to validate responses and automate assertions using test scripts. Newman can then be configured to run these collections automatically, especially within CI/CD pipelines. This ensures that API tests are performed consistently with every code change, reducing the likelihood of issues going unnoticed.

Best Practices for API Testing

To get the most out of Postman and Newman, certain best practices should be followed. Data-driven testing, using external data files, can significantly expand test coverage. Maintaining collections in version-controlled repositories, such as GitHub, fosters collaboration and helps track changes effectively. Monitoring API performance over time is vital, with regular analysis of response times offering opportunities for optimisation. Security must not be overlooked—tests should include checks for authentication, authorisation, and potential vulnerabilities. As APIs evolve, test suites must be reviewed and updated regularly to reflect the latest changes and maintain accuracy.

Conclusion

API testing is a fundamental component of robust software development, ensuring applications operate correctly and maintain smooth integrations. Postman simplifies the process of creating and managing API tests, while Newman adds the power of automation and scalability. Together, these tools form a comprehensive solution for both manual and automated testing. By following a structured approach and adhering to industry best practices, teams can improve the reliability of their APIs, streamline testing workflows, and accelerate release cycles. Embracing Postman and Newman effectively enables organisations to deliver high-quality software with confidence.

Ready to enhance your API testing strategy with Postman and Newman? Whether you’re looking to streamline manual testing, implement automation, or integrate testing into your CI/CD pipeline, our team is here to help. Contact us today to learn how we can help streamline your testing process with Postman and Newman.

Cucumber BDD: Data-Driven Testing for Mobile Apps & Selenium

Posted on by Layana Chandran

Introduction

Enhance mobile app testing with Cucumber BDD. Improve test readability, collaboration & reporting over TestNG. Boost Selenium & Appium automation efficiency.

In mobile application testing, automation plays a vital role in ensuring quality and performance. While TestNG is widely used for test execution, Cucumber offers a behaviour-driven development (BDD) approach that enhances collaboration and readability. This article explores why Cucumber is more effective than TestNG for mobile application testing using Java and Selenium.

Advantages of Using Cucumber Over TestNG

One of the key advantages of Cucumber is its improved readability and collaboration. Cucumber employs Gherkin syntax, which is human-readable and allows non-technical stakeholders to understand test cases. In contrast, TestNG relies on Java-based annotations, making it less accessible to business teams.

Cucumber supports a BDD-driven approach, enabling tests to be written in plain English and aligned with business requirements. TestNG follows a more traditional unit-testing approach, which makes it harder to map tests directly to user stories.

The reusability of steps is another significant advantage of Cucumber. Step definitions in Cucumber can be reused across multiple scenarios, reducing code duplication and simplifying maintenance. TestNG, however, requires test methods to be explicitly written, which increases maintenance efforts over time.

Cucumber also provides enhanced reporting capabilities. It generates structured and detailed reports, including scenario-wise execution results. In contrast, TestNG reports require additional configuration to achieve the same level of readability and organisation.

Challenges of Using Cucumber

Despite its advantages, implementing Cucumber does come with certain challenges. One of these is the learning curve. Teams unfamiliar with BDD may require time to understand Gherkin syntax and the specifics of Cucumber’s implementation.

Performance overhead is another consideration. The additional layer of step definitions in Cucumber can result in slower execution compared to TestNG’s direct method execution.

Integration complexity can also be a challenge. Adapting Cucumber to existing TestNG-based frameworks may require considerable refactoring and restructuring of test cases.

How to Overcome These Challenges

To mitigate these challenges, teams can conduct training sessions and workshops on BDD and Gherkin to help testers and developers adopt the new approach more effectively.

Optimising step definitions is another crucial step. By avoiding redundant steps and creating modular, reusable steps, execution time can be significantly reduced.

A hybrid approach can also be beneficial. Cucumber can be used for functional scenarios while TestNG is retained for lower-level unit tests, thereby maintaining a balance between readability and execution efficiency.

How to Implement Cucumber for Mobile App Testing

The first step in implementing Cucumber for mobile application testing is setting up the project. This involves installing the necessary dependencies, including Selenium, Appium, Cucumber, and JUnit or TestNG, using Maven.

Next, feature files must be created. These are written in Gherkin syntax and contain scenarios that define test cases in a human-readable format.

Following this, step definitions need to be developed in Java. These map feature file steps to the corresponding Selenium or Appium automation code.

Finally, tests can be executed using Cucumber’s JUnit or TestNG runner, generating detailed reports on execution outcomes.

Cucumber Workflow

Report Comparison: Cucumber vs TestNG

When comparing Cucumber reports with TestNG reports, Cucumber offers greater readability due to its scenario-based format. TestNG reports, which are XML-based, are moderately readable but less intuitive for non-technical stakeholders.

In terms of customisation, Cucumber makes it easier to generate reports in built-in HTML and JSON formats, whereas TestNG often requires third-party tools for enhanced reporting.

Execution insights are more detailed in Cucumber, as it provides logs with screenshots, making it easier to track issues. TestNG reports, in contrast, primarily contain standard test method logs.

Cucumber is also more user-friendly for non-technical team members, whereas TestNG remains more suited to technical users familiar with Java-based annotations.

Feature Cucumber Report TestNG Report 
Readability High (scenario-based) Moderate (XML-based) 
Customisation Easy (built-in HTML & JSON) Requires third-party tools 
Execution Insights Detailed logs with screenshots Standard test method logs 
Non-Technical Friendly Yes No  

What We Learned

Cucumber enhances test readability, collaboration, and alignment with business goals. While TestNG offers faster execution, Cucumber provides a structured and reusable framework for BDD-based testing. Integrating Cucumber with Selenium and Appium improves test maintainability and reporting. Overcoming initial learning challenges and optimising implementation can maximise the benefits of using Cucumber.

Conclusion

Cucumber is a powerful tool for mobile application testing, offering superior readability, structured test execution, and enhanced collaboration compared to TestNG. By understanding its advantages, challenges, and implementation strategies, teams can make informed decisions about adopting Cucumber for automation testing.

Looking to implement Cucumber BDD for your mobile application testing? Our experts can help you streamline your automation framework and improve testing efficiency. Get in touch with us today to discuss how we can support your testing needs!

Apache Kafka: Building Event-Driven Pipelines with FastAPI

Posted on by Layana Chandran

Introduction

Learn to integrate Apache Kafka with FastAPI for scalable, real-time data streaming using Confluent Kafka in modern event-driven Python applications.

Apache Kafka is a distributed event streaming platform designed to handle real-time data feeds. Initially developed by LinkedIn and later open-sourced under the Apache Software Foundation, Kafka is a popular choice for building scalable, fault-tolerant, and high-throughput messaging systems. This guide explores how to integrate Kafka with FastAPI, a modern Python web framework, to enable real-time data streaming using Confluent Kafka.

Why Use Apache Kafka with FastAPI?

Combining Kafka and FastAPI provides a powerful solution for real-time data processing in Python applications. Kafka’s high throughput can manage millions of messages per second, while its horizontal scalability makes it ideal for microservices architectures. FastAPI, built on Starlette and Pydantic, offers fast, asynchronous API interactions. Together, Kafka and FastAPI facilitate event-driven communication between microservices, improving responsiveness and reducing system coupling.

Kafka Architecture

Kafka’s architecture consists of several key components:

  • Producer: Publishes messages to Kafka topics.
  • Broker: Kafka servers that store and deliver messages.
  • Topic: A logical channel where producers send messages and consumers retrieve them.
  • Partition: Subdivisions of a topic that enable parallel message processing and improve throughput.
  • Consumer: Reads messages from topics, either individually or as part of a consumer group.
  • Zookeeper: Manages metadata and coordinates leader elections within Kafka clusters.

Integrating Kafka with FastAPI Using Confluent Kafka

Installing Dependencies

To integrate Kafka with FastAPI, install the required packages:

pip install fastapi uvicorn confluent-kafka

Setting Up Kafka with FastAPI

Kafka Producer in FastAPI

The Kafka producer sends messages to a specified topic. In a FastAPI application, you can implement a producer as follows:

from fastapi import FastAPI
from confluent_kafka import Producer

app = FastAPI()

producer_config = {
    'bootstrap.servers': 'localhost:9092'
}
producer = Producer(producer_config)

@app.post("/produce/{message}")
async def produce_message(message: str):
    producer.produce("test-topic", message.encode("utf-8"))
    producer.flush()
    return {"status": "Message sent"}

This code defines a FastAPI endpoint that accepts messages via HTTP POST requests and publishes them to the “test-topic” Kafka topic. The flush() method ensures that all buffered messages are sent immediately.

Kafka Consumer in FastAPI

The Kafka consumer reads messages from a topic. In FastAPI, you can run a consumer in a background thread to listen continuously for new messages:

from confluent_kafka import Consumer
import threading

consumer_config = {
    'bootstrap.servers': 'localhost:9092',
    'group.id': 'fastapi-group',
    'auto.offset.reset': 'earliest'
}
consumer = Consumer(consumer_config)
consumer.subscribe(["test-topic"])

def consume():
    while True:
        msg = consumer.poll(1.0)
        if msg is not None and msg.value() is not None:
            print(f"Consumed: {msg.value().decode('utf-8')}")

thread = threading.Thread(target=consume, daemon=True)
thread.start()

This code initializes a Kafka consumer that subscribes to the “test-topic” topic. The consume function polls Kafka for new messages and prints them when they arrive. Running the consumer in a separate thread allows it to operate concurrently with FastAPI’s main event loop.

Future Enhancements

A potential future enhancement involves live streaming using WebSockets. FastAPI offers native support for WebSockets, which can be used to deliver Kafka messages to clients in real-time. This approach enhances application responsiveness and allows dynamic, live data feeds to be displayed to users.

Conclusion

Integrating Kafka with FastAPI using Confluent Kafka enables you to build scalable, real-time applications efficiently. Kafka’s high-throughput event streaming combined with FastAPI’s asynchronous capabilities provides a robust foundation for modern, event-driven architectures. Future enhancements, such as live streaming with WebSockets, can further extend your system’s real-time capabilities.

Our team of experts is ready to assist you in designing and implementing scalable, real-time data streaming solutions with Kafka and FastAPI. Contact us today to learn how we can help bring your vision to life.

Getting Started with TestNG in Java Selenium Automation

Posted on by Layana Chandran

Introduction

Boost Selenium automation with TestNG! Learn annotations, parallel execution, reporting & advanced features for efficient Java test automation.

Automation testing has become an essential part of software development, ensuring efficient and reliable software delivery. One of the most powerful frameworks for automation testing in Java Selenium is TestNG. This guide provides a step-by-step approach for beginners to understand TestNG, its benefits, and how to integrate it into Selenium automation.

What is TestNG?

TestNG (Test Next Generation) is a powerful testing framework inspired by JUnit and NUnit. It provides enhanced functionality, making test execution more flexible and efficient. Some key features include:

  • Annotations – Helps define test methods clearly (e.g., @Test, @BeforeMethod, @AfterMethod).
  • Parallel Execution – Allows running multiple test cases simultaneously.
  • Data-Driven Testing – Supports parameterization with @DataProvider.
  • Flexible Execution – Enables grouping, dependency, and priority-based execution.
  • Advanced Reporting – Automatically generates detailed test execution reports.

What is Selenium?

Selenium is an open-source framework used for automating web applications. It allows test scripts to be written in multiple programming languages, including Java, Python, and C#. Selenium simulates user interactions with web browsers, enabling automated functional testing of web applications.

Why Use TestNG for Selenium Automation?

  • Better Test Structure – Organizes test execution efficiently.
  • Assertions for Validation – Ensures test accuracy using Assert statements. 
  • Retry and Failure Handling – Allows rerunning failed tests. 
  • Test Execution Control – Provides options for dependencies and priorities.
  • Comprehensive Reporting – Generates detailed execution reports automatically.

Understanding Annotation Execution Order

TestNG follows a specific execution order for annotations. Below is a general sequence: 

  • @BeforeSuite
  • @BeforeTest
  • @BeforeClass
  • @BeforeMethod
  • @Test
  • @AfterMethod
  • @AfterClass
  • @AfterTest
  • @AfterSuite

Steps to Implement TestNG in Java Selenium

Step 1: Add the Framework to Your Project

  • For Maven Users: Add the following dependency in pom.xml
  • For Non-Maven Users: Download TestNG and add it to your project’s libraries manually.

Step 2: Create a Class for Testing

Create a new Java class and add a basic TestNG test

Step 3: Run the Test

  • Right-click on the class → Select Run As → TestNG Test.
  • You should see TestNG executing your test in the console output. 

Step 4: Implement Basic Annotations

TestNG provides various annotations to control test execution flow. Here’s an example

Explanation:

  • @BeforeClass – Runs once before all test methods in the class. 
  • @Test – Defines test cases.
  • @AfterClass – Runs once after all test methods.

Step 5: Generate Reports

After executing tests, automatically generates reports in the test-output folder. These reports help in analyzing test results and debugging failures.

Advantages Over Manual Testing

As experience with TestNG grows, exploring advanced features enhances the automation framework. Data Providers, using the @DataProvider annotation, allow running the same test with multiple data sets. Listeners, defined with @Listeners, enable customization of execution behavior. Test cases can be efficiently organized using grouping and dependencies. The retry mechanism, implemented through IRetryAnalyzer, ensures automatic re-execution of failed tests. Parallel execution accelerates processing by running tests concurrently, reducing overall execution time.

Report Generation and Future Use

TestNG reports provide structured logs of test execution, categorizing passed, failed, and skipped test cases. These reports are valuable for debugging and tracking issues. Over time, they help in analyzing trends in test failures, optimizing test strategies, and ensuring continuous quality improvements. Integrating these reports with CI/CD tools like Jenkins enhances automated test tracking and reporting.

Advanced Features

As you gain experience, explore these advanced features to enhance your automation framework:

  • Data Providers (@DataProvider) – Allows running the same test with multiple data sets.
  • Listeners (@Listeners) – Helps customize test execution behavior. 
  • Grouping & Dependencies – Organizes test cases efficiently.
  • Retry Mechanism (IRetryAnalyzer) – Automatically re-executes failed tests.
  • Parallel Execution – Runs tests faster by executing them concurrently.

Final Thoughts

Implementing TestNG in web automation structures execution and enhances efficiency. Beginners should start with simple test cases and gradually explore advanced features like parallel execution and data-driven testing. With its robust functionality, TestNG remains a preferred choice for Java-based automation, ensuring reliable and effective test execution.

If you want to enhance your automation testing strategy with TestNG and Selenium, our experts are here to provide comprehensive support, from implementation and troubleshooting to optimizing your test automation framework. Get in touch with us today to streamline your testing process and achieve efficient, reliable automation results.

Content Security Policy (CSP) in Pen Testing: Importance & Fixes

Posted on by Layana Chandran

Why CSP Is a Major Concern in Penetration Testing

Improve web security with a strong Content Security Policy (CSP). Learn how to detect, fix, and monitor CSP vulnerabilities to prevent XSS attacks.

When conducting a security audit or penetration test, one of the most common findings is a missing or weak Content Security Policy (CSP) directive. CSP acts as a client-side security control that restricts which resources, such as scripts, styles, and images, can be loaded by a web application.

If CSP is not properly configured, attackers can inject malicious scripts, hijack user sessions, or steal sensitive data. During penetration testing, security professionals assess whether CSP is implemented and how easily it can be bypassed. A typical penetration testing report might highlight CSP issues such as the absence of a CSP header, an overly permissive CSP that allows inline scripts, or the inclusion of third-party scripts from untrusted sources.

Understanding CSP and Its Functionality

CSP is defined through an HTTP response header that specifies the allowed sources for various types of resources. For example, a basic CSP configuration might look like:

add_header Content-Security-Policy "default-src 'self'; script-src 'self';";

Key directives include:

  • default-src 'self' which restricts all resources to the same origin unless specifically overridden.
  • script-src 'self' which allows JavaScript execution only from the same domain, blocking inline scripts.

When a browser encounters CSP, it blocks any non-compliant resource and logs a violation, reducing the attack surface for Cross-Site Scripting (XSS) and other injection attacks.

Evaluating CSP During Penetration Testing

The first step is to check if CSP is implemented. This can be done using browser developer tools by navigating to the Network tab and checking response headers or by using the command:

curl -I https://target-website.com | grep Content-Security-Policy

If no CSP is present, it represents a critical security finding. The next step is to analyze weak directives, such as the following example:

add_header Content-Security-Policy "script-src 'self' 'unsafe-inline' 'unsafe-eval';";

The presence of 'unsafe-inline' allows inline scripts, making XSS attacks trivial, while 'unsafe-eval' enables execution of JavaScript through eval(), facilitating code injection. To further assess the effectiveness of CSP, penetration testers can attempt to inject scripts through input fields or URL parameters, such as:

<script>alert('XSS Attack!')</script>

If the script executes, the CSP configuration is ineffective. If the browser blocks execution, checking the console for CSP violation errors helps identify potential weaknesses.

Fixing CSP Issues and Implementing a Strong Policy

Before enforcing CSP, a good practice is to start with a report-only mode. This allows security teams to detect potential breakages without blocking resources. A report-only CSP header can be implemented as follows:

add_header Content-Security-Policy-Report-Only "default-src 'self'; report-uri /csp-report;";

Once tested, a stricter CSP policy should be enforced:

add_header Content-Security-Policy "
  default-src 'self';
  script-src 'self' https://trusted-cdn.com;
  style-src 'self' 'nonce-randomNonce';
  object-src 'none';
  base-uri 'self';
  form-action 'self';
  frame-ancestors 'none';
";

This policy ensures that all resources are loaded from the same origin unless specified, JavaScript is only allowed from the site itself and a trusted CDN, inline styles are controlled using a nonce, Flash and other outdated technologies are blocked, and protections against clickjacking and unauthorized form submissions are in place.

Verifying and Monitoring CSP

After enforcing CSP, testing is essential to ensure that legitimate resources are not blocked. Browser developer tools can be used to check for blocked resources, and the CSP policy can be verified with:

curl -I https://yourwebsite.com | grep Content-Security-Policy

To continuously monitor CSP violations, a reporting endpoint should be configured:

add_header Content-Security-Policy "default-src 'self'; report-uri /csp-report;";

This allows for logging and analyzing potential violations, ensuring that CSP remains effective as the website evolves.

The Role of CSP in Web Security

CSP is a crucial security control that significantly reduces the risk of XSS attacks. During penetration testing, weak CSP policies are one of the most common vulnerabilities found.

To maximize security, it is essential to start with a report-only mode to identify potential breakages before enforcement, use nonces and hashes instead of allowing inline scripts, monitor CSP violations for continuous improvement, and avoid unsafe directives such as 'unsafe-inline' and 'unsafe-eval'. Regular reviews and updates to CSP are also necessary to accommodate changes in website content while maintaining strong security controls.

By implementing a well-structured CSP policy, web applications can effectively mitigate a major attack vector, significantly enhancing their security against XSS and other injection-based threats.

Ensuring your web application has a robust CSP policy is crucial for protecting against modern threats. If you need help with penetration testing or strengthening your CSP implementation, our security experts are ready to assist. Contact us now to schedule a consultation and safeguard your digital assets against cyber attacks.

Rocket Chat Integration: A Real-World Technical Deep Dive

Posted on by Layana Chandran

Integrate Rocket.Chat seamlessly with OAuth2, MongoDB, and WebSockets. Optimize scalability, security, and performance for enterprise-ready deployment.

Integrating Rocket.Chat into an application involves more than simply deploying a Docker container. A successful Rocket.Chat implementation requires meticulous planning around authentication, scalability, security, and performance. This article provides a detailed breakdown of our real-world experience, including the challenges encountered, debugging strategies, and key takeaways.

Why We Chose Rocket.Chat

Rocket.Chat stood out as the ideal choice for our needs due to its open-source nature, allowing customization to fit business-specific workflows. Its scalability made it suitable for both small teams and enterprise-level deployments. The platform’s comprehensive API enabled deep integration with our existing systems, and its active developer community provided valuable support and frequent updates. Despite these advantages, we had to carefully evaluate Rocket.Chat’s limitations to ensure it met our requirements before proceeding with the integration.

Deployment Challenges and Considerations

One of the first critical decisions was choosing between self-hosting and cloud deployment. Compliance requirements dictated that we retain full control over user data, leading us to opt for a self-hosted Rocket.Chat instance. This approach introduced challenges such as manual management of updates and patches, ensuring database resilience due to Rocket.Chat’s reliance on MongoDB, and implementing high availability to prevent downtime.

Performance optimization was another key focus. Rocket.Chat primarily uses WebSockets for real-time communication, requiring proper load balancing to manage concurrent connections efficiently while implementing fallback mechanisms for clients experiencing WebSocket issues. MongoDB scalability was also a concern, necessitating proper indexing to avoid performance bottlenecks and setting up replica sets for failover support. Additionally, Redis was integrated for caching session data, optimizing response times, and reducing server load.

Authentication and User Management

Rocket.Chat supports OAuth2 for single sign-on, simplifying authentication across multiple platforms. However, integrating OAuth2 presented challenges, particularly with token expiration management. Some users experienced unexpected logouts due to improper handling of refresh tokens. Ensuring a seamless authentication flow required fine-tuning session persistence and token refresh mechanisms.

Customization and API Integration

Embedding Rocket.Chat into our mobile application required integrating its SDKs. We initially used Flutter with Dashchat2 for the frontend, but encountered stability issues with the React Native SDK, forcing us to rely on direct API usage in some cases. Push notification handling required additional configuration to ensure messages were delivered reliably.

Automating group creation and permission management was streamlined through Rocket.Chat’s API, yet we faced obstacles with rate limits when bulk-creating user groups. Additionally, inconsistencies in role assignments required extra validation to ensure permissions were correctly applied when provisioning users dynamically.

Security Considerations

To ensure secure communications, SSL/TLS encryption was implemented for WebSocket traffic, enforcing WSS across all connections. Audit logging was configured to maintain a detailed event history for compliance and monitoring purposes. Brute force protection measures included enforcing API rate limiting and implementing IP-based restrictions to mitigate unauthorized access attempts.

Performance Testing and Scaling

Before deployment, extensive performance testing was conducted using Apache JMeter to simulate real-world concurrent user activity. This process identified MongoDB bottlenecks, leading to query optimizations that improved response times. To handle peak loads efficiently, horizontal scaling was deployed, ensuring the Rocket.Chat system could accommodate high user demand without degrading performance.

Lessons Learned and Future Enhancements

Through this integration, several key lessons emerged. Rocket.Chat is a powerful solution, but enterprise deployments require significant tuning to achieve optimal performance. Scalability remains a challenge without proper MongoDB replication and caching strategies. OAuth2 integration demands meticulous session management to prevent authentication issues. Looking ahead, future enhancements will focus on integrating AI-powered chatbots to improve automation and implementing advanced analytics for better user insights.

Conclusion

Integrating Rocket.Chat involves more than just running a container; it requires a structured approach to architecture, security, and performance optimization. While Rocket.Chat offers extensive capabilities, successful implementation demands careful planning, customization, and ongoing maintenance. Organizations considering Rocket.Chat should be prepared for these challenges and take proactive measures to overcome them for a seamless and efficient deployment.

Looking to integrate Rocket.Chat seamlessly? Our experts ensure secure, scalable, and high-performance deployments with custom API integrations, security enhancements, and performance tuning. Contact us now to elevate your communication platform.

Mobile App Testing: Selenium with Java and Cucumber – Insights

Posted on by Layana Chandran

Introduction

Automate mobile app testing with Selenium, Cucumber & Appium. Improve test efficiency, ensure scalability, and streamline CI/CD with BDD & parallel execution.

Selenium, Cucumber, and Appium have been essential in automating mobile application testing. These tools reduce repetitive tasks and help teams ensure robust application quality. This article explores real-world scenarios, challenges faced, and best practices for implementing an efficient test automation framework.

Why We Chose Selenium, Cucumber, and Appium

Appium, built on Selenium, extends automation to mobile applications. It supports native, hybrid, and web apps on both iOS and Android, making cross-platform automation seamless. Since it provides a unified API, the learning curve remains low.

Cucumber enhances behavior-driven development (BDD), allowing technical and non-technical teams to collaborate more effectively. It uses Gherkin syntax to create human-readable test scenarios and integrates smoothly with Selenium and Appium. Our goal was to build a scalable and maintainable test automation framework, and these tools offered the ideal foundation.

Setting Up Appium with Selenium and Cucumber

We started by creating a Maven project and defining dependencies for Selenium, Cucumber, and Appium in the pom.xml file. The setup also included configuring the Appium server and specifying device-related settings for mobile automation.

<dependencies> 
    <dependency> 
        <groupId>org.seleniumhq.selenium</groupId> 
        <artifactId>selenium-java</artifactId> 
        <version>4.10.0</version> 
    </dependency> 
    <dependency> 
        <groupId>io.cucumber</groupId> 
        <artifactId>cucumber-java</artifactId> 
        <version>7.10.0</version> 
    </dependency> 
    <dependency> 
        <groupId>io.appium</groupId> 
        <artifactId>java-client</artifactId> 
        <version>8.4.0</version> 
    </dependency> 
</dependencies>

To structure our tests, we used cucumberOptions in the runner class to define feature files and step definitions. This approach ensured the framework could scale efficiently as the application evolved.

Real-World Scenarios and Challenges

One major project involved automating the PETCare/Mythings app. Our tests focused on critical functionalities, such as biometric authentication for login, appointment scheduling, and pet medical history tracking. Since the app had to perform consistently across multiple devices, UI behavior validation was a priority.

However, platform-specific locators presented a challenge. Android and iOS required different locators, which we resolved using Appium’s MobileBy class. Managing multiple devices for parallel execution also proved complex. To solve this, we configured Appium servers with unique ports for each device.

DesiredCapabilities caps = new DesiredCapabilities(); 
caps.setCapability("platformName", "Android"); 
caps.setCapability("deviceName", "Pixel_5_API_30"); 
caps.setCapability("app", "path/to/app.apk"); 
caps.setCapability("automationName", "UiAutomator2");

By integrating Appium tests into Cucumber scenarios, we ensured consistent reporting and execution.

Parallel Testing in CI/CD Pipelines

To optimize test execution time, we enabled parallel execution in Cucumber using JUnit. Running device-specific scenarios in parallel significantly reduced execution time during nightly builds.

@RunWith(Cucumber.class) 
@CucumberOptions( 
    features = "src/test/resources/features", 
    glue = "com.example.steps", 
    plugin = {"pretty", "json:target/cucumber-report.json"}, 
    monochrome = true 
) 
public class TestRunner {}

However, thread safety became an issue. Since multiple tests ran concurrently, each Appium instance needed to remain isolated. We addressed this by implementing a thread-local factory for device management.

Wait<WebDriver> wait = new FluentWait<>(driver) 
    .withTimeout(Duration.ofSeconds(30)) 
    .pollingEvery(Duration.ofSeconds(2)) 
    .ignoring(NoSuchElementException.class);

Additionally, synchronization issues led to test failures due to race conditions. Instead of using fixed delays, we incorporated FluentWait to dynamically wait for elements:

Implementing Page Object Model (POM) for Mobile Applications

To improve maintainability, we adopted the Page Object Model (POM). Each screen had a dedicated class that encapsulated locators and actions. For platform-specific actions, we extended this structure accordingly.

A sample feature file in Gherkin syntax looked as follows:

Feature: Login to PETcare App  
  Scenario: User logs in with valid credentials  
    Given the user is on the login screen  
    When the user enters valid credentials  
    And clicks the login button  
    Then the user should be redirected to the homepage

The corresponding step definitions were implemented in Java:

package com.example.steps; 
 
import io.cucumber.java.en.*; 
import com.example.pages.LoginPage; 
 
public class LoginSteps {  
    LoginPage loginPage = new LoginPage();  
 
    @Given("the user is on the login screen")  
    public void userOnLoginScreen() {  
        loginPage.navigateToLoginScreen();  
    }  
 
    @When("the user enters valid credentials")  
    public void userEntersCredentials() {  
        loginPage.enterUsername("testUser");  
        loginPage.enterPassword("password123");  
    }  
 
    @And("clicks the login button")  
    public void clickLogin() {  
        loginPage.clickLoginButton();  
    }  
 
    @Then("the user should be redirected to the homepage")  
    public void verifyHomePage() {  
        loginPage.verifyHomePage();  
    }  
}

This approach made test cases more readable and maintainable. Gherkin syntax ensured that even non-technical stakeholders could understand the tests. Step definitions became reusable across multiple scenarios, and locator updates were confined to the page class, reducing test maintenance efforts.

Lessons Learned and Best Practices

Planning for scalability was crucial. Modular feature files and step definitions helped organize tests by functionality, while externalizing test data in formats like JSON or Excel improved flexibility. Synchronization mechanisms were refined by avoiding hard-coded sleep statements, instead leveraging FluentWait and ExpectedConditions for more stable test execution.

Maximizing reusability played a key role in efficient automation. Implementing reusable components, including Appium factories, reporting utilities, and custom assertions, streamlined test management. Reporting was enhanced by integrating Cucumber with tools like Allure, providing actionable insights into test execution.

Conclusion

The experience of using Selenium, Cucumber, and Appium demonstrated their ability to transform mobile application testing. Features such as BDD, parallel execution, POM, and data-driven testing contributed to a scalable and robust automation framework. Whether starting or scaling automation efforts, these tools offer a solid foundation for success.

Enhance your mobile app testing with Selenium, Cucumber, and Appium for faster, more reliable automation. Our experts can help you build a scalable framework tailored to your needs. Contact us now to streamline your testing process and boost efficiency!

Optimise Yacht Charter Communications

Posted on by Layana Chandran

Optimise Yacht Charter Communications with AI-Driven Contact Management. Automate crew, client, and service provider coordination for seamless operations.

Managing communications with crew members, clients, and service providers can become increasingly complex as your yacht charter business grows. At ACS, we recognise the challenges of handling an extensive contact list across different seasons and roles. Our smart contact management system is designed to automate and optimise communication processes, ensuring seamless coordination without unnecessary manual effort.

Why Effective Contact Management is Essential for Yacht Charter Operations

A successful yacht charter business requires precise coordination with various stakeholders. From skippers and hostesses to service providers and clients, each contact must receive timely and relevant information. Without an efficient system, challenges such as disorganised contact records across charter seasons, duplicate messages due to unstructured data, and time-consuming manual filtering can arise. Additionally, inefficiencies in bulk messaging can lead to delays and inconsistencies in communication.

Introducing Smart Contact Management: Your Communication Solution

Our intelligent system leverages automation, AI-driven tagging, and real-time updates to simplify contact management, reducing manual workload and improving accuracy. It enhances daily operations by ensuring structured and efficient contact management.

Effortless Contact Organisation

The system features smart role-based classification, automatically categorising contacts by role such as skippers, hostesses, and crew using predefined rules and AI-powered recognition. It separates system users from personal contacts for improved security and efficiency, allowing quick filtering and retrieval of specific contacts. Seasonal organisation tracks crew availability across charter seasons to prevent scheduling conflicts, maintains accurate records with historical data, and ensures multi-season crew members receive only relevant updates.

Automated Features for Seamless Operations

Advanced automation eliminates tedious manual processes. Automatic contact recognition detects, categorises, and updates new contacts in real-time. WhatsApp integration verifies contact availability, syncs communication preferences, and enables seamless messaging. Duplicate detection and merging maintain a clean database by consolidating redundant entries. Effortless importing ensures bulk contact uploads from spreadsheets or CRM systems without formatting issues.

Real-World Applications: How It Works in Practice

Managing seasonal crew updates is simplified by selecting the skipper category, choosing the relevant season, and dispatching messages in a single step. Coordinating with service providers becomes more efficient through filtering based on service type, location, or frequency of engagement. Service history and performance metrics can be tracked, allowing targeted communications when needed. Streamlining hostess assignments is easier with quick access to seasonal availability, bulk messaging tools for availability confirmation, and transparent communication logs for better tracking.

Looking Ahead: Upcoming Enhancements

Continuous improvements ensure new features that optimise contact management and communication flow. Advanced filtering options will introduce AI-powered search capabilities for faster and more precise results, along with custom categories to match unique business needs. Smart communication tools will provide ready-to-use message templates tailored for different scenarios, personalised communication options, and storage of frequently used messages for quick access. Performance tracking and insights will offer analytics on message open rates, engagement trends, and response times, as well as communication history tracking to refine engagement strategies.

Maximising Your Contact Management System

To fully leverage the smart contact management solution, regular updates to contact categories ensure accuracy for each charter season. Bulk messaging should be utilised for time-sensitive announcements, while automated duplicate checks help maintain database hygiene. Keeping crew records updated with availability status streamlines scheduling, ensuring efficient operations.

Elevate Your Yacht Charter Communications

Inefficient contact management should not slow down business operations. The intelligent system is designed specifically for yacht charter operations, reducing time spent on manual contact organisation, minimising communication errors through structured automation, improving crew and service provider coordination, and enhancing overall efficiency with AI-driven tools.

Experience the Difference

Many yacht charter businesses have already transformed their communications with the smart contact management system. Contact our team today for a demo and see how it can work for you.

Supply Chains: A Journey from the Past to the Present

Posted on by Layana Chandran

Discover the evolution of supply chains from barter to AI-driven logistics. Explore advancements in trade, technology, automation, and sustainability.

Supply chains are essential to global trade, ensuring that goods move efficiently from manufacturers to consumers. While modern supply chains incorporate advanced technology such as real-time tracking and artificial intelligence, the fundamental goal remains unchanged: delivering products on time and at the lowest possible cost. Over time, supply chains have evolved significantly, adapting to new technologies and economic shifts.

The Ancient Supply Chain: The Start of Trade

In the earliest days of trade, people relied on barter systems, exchanging goods based on necessity. A farmer might trade wheat for fish from a fisherman, with transactions limited to small communities. Transportation was slow, often depending on walking or the use of animals, which restricted the movement of goods. Supply was unpredictable since availability depended on seasonal changes and local conditions, making trade inconsistent and unreliable.

As civilizations expanded, long-distance trade routes emerged, with the Silk Road becoming one of the first global supply chains around 200 BCE. This vast network connected China, India, the Middle East, and Europe, allowing goods such as silk, spices, and metals to travel across continents. Camels were used for desert crossings, while ships facilitated maritime trade. Despite enabling international commerce, the Silk Road posed significant risks, including storms, bandit attacks, and long delays. Trade flourished, but the system remained expensive and unpredictable.

The Industrial Revolution: Mass Production and Faster Transport

By the 18th and 19th centuries, the Industrial Revolution transformed supply chains. Factories emerged, producing goods like textiles and clothing on a massive scale. Transportation advanced with the introduction of trains and steamships, making it faster and more efficient to move products across vast distances. Warehouses became larger and more organized, allowing businesses to store more inventory and manage supply more effectively. However, despite these advancements, tracking shipments and handling delays still required manual effort, making logistics a complex challenge.

The 20th Century: Modern Logistics

The mid-1900s marked a new era of logistics, as global shipping networks expanded and air cargo became a practical option for fast deliveries. A major breakthrough was the introduction of standardized shipping containers, which revolutionized the transportation industry by simplifying loading and unloading processes. This innovation reduced costs and improved efficiency across the supply chain.

Air cargo further enhanced logistics by enabling rapid transportation of time-sensitive goods such as electronics and medicine. Businesses also refined warehouse management and delivery coordination, making supply chains more efficient. However, global trade still faced challenges, particularly in managing cross-border shipments and navigating customs regulations.

The 21st Century: Technology and Automation

Today, supply chains operate with an unprecedented level of intelligence and automation. Real-time tracking allows businesses and consumers to monitor shipments at every stage, providing transparency and reducing uncertainty. Artificial intelligence helps predict demand, optimize delivery routes, and manage inventory, making supply chains more efficient than ever before. Agile Cyber Solutions specialize in innovative digital solutions that enhance supply chain management and security.

Automation has transformed warehouses, with robots picking, packing, and sorting goods at speeds far beyond human capabilities. Many companies are also prioritizing sustainability, integrating electric vehicles for deliveries and implementing eco-friendly practices to reduce waste and emissions.

Conclusion: Continuous Change

From ancient barter systems to the high-tech supply chains of today, the evolution of global trade has been shaped by innovation and efficiency. Advancements in transportation, logistics, and technology have made supply chains faster, smarter, and more transparent. As technology continues to evolve, the future promises even more automation, real-time tracking, and sustainability efforts, ensuring that supply chains remain a vital force in global commerce.

Stay ahead in the evolving world of supply chains with expert insights and cutting-edge solutions. Whether optimizing logistics, implementing real-time tracking, or enhancing sustainability, we can help. Contact us today to streamline your operations and boost efficiency!