Author: Layana Chandran

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.

Offline First Asset Management App | Organise Your Things

Posted on by Layana Chandran

Overview

Offline-first asset management app with smart sync, seamless collaboration, and real-time-like updates. Organise, track, and share items anytime, anywhere.

“Organise Your Things” is an offline-first asset management app that helps users categorise, manage, and track personal and shared items. Unlike cloud-dependent organisational apps, this solution allows users to function without internet access, requiring a connection only for sharing items with others.

The project aimed to provide a simple yet structured way for users to organise their belongings under predefined and custom categories, attach documents, set reminders, and collaborate securely. However, ensuring a smooth offline experience with seamless data synchronisation and real-time sharing when online presented a major challenge. This case study explores how we designed, tested, and refined the offline-first asset management app based on real user feedback and the impact it had on the end-users.

Project Details

The project involved web and mobile app development, AI integration, UX optimization, and content management. Development took place from January 2018 to February 2023. The budget supported SMEs with scalable options for future growth.

Aspect Details 
Services Web and mobile app development, AI integration, UX optimization, content management. 
Period January 2018 – February 2023. 
Budget Designed to be SME-friendly with scalable options for future growth. 

Key Features

The offline-first asset management app offers full offline functionality, enabling users to add, edit, and manage items without internet access. Users can categorise their belongings using predefined options like Transport, Personal, and Construction, or create custom categories. They can attach images, descriptions, sub-items, documents, tasks, reminders, and notes to each item. For Transport category users, the app fetches vehicle data via an API when a network is available. Collaboration features allow users to connect and share items with View/Edit access permissions, while the system queues and delivers notifications when recipients come online. All offline changes sync automatically once the network reconnects.

Problem Statement

Most organisational apps require constant internet access, making them impractical for users in low-connectivity areas or those who prefer local storage over cloud-based services. Users needed a system that could store and manage items offline without data loss, allow smooth collaboration without requiring simultaneous online access, retrieve transport-related details dynamically when online without unnecessary manual entry, and handle delayed notifications and conflict resolution for shared offline updates. The major challenge involved providing real-time-like collaboration while maintaining offline functionality.

The Client and the Challenges

Field professionals, small businesses, and individuals who need to organise their assets without stable internet access benefitted from this offline-first asset management app. Early adopters included construction site managers tracking equipment and assigning tasks, vehicle owners storing transport details, and personal users managing offline inventories.

Ensuring seamless offline functionality while avoiding sync conflicts required a robust solution. We developed a local-first database architecture using IndexedDB and SQLite with a sync queue system to efficiently manage synchronisation. Sharing and notifications posed another challenge since users could send sharing requests offline, but notifications had to be delivered when both users were online. To address this, we implemented a background process that queued notifications and sent updates automatically when users reconnected.

To avoid unnecessary API calls and slowdowns, the offline-first asset management app used smart caching so previously fetched vehicle data remained accessible offline. Conflict resolution for shared items was another challenge, particularly when two offline users edited the same item. We implemented a version-tracking system with a “last edit wins” model and notified users if their changes were overwritten.

Why Choose Our Solution?

This offline-first asset management app stands out due to its 100% offline functionality, eliminating the need for a login or internet access unless users share items. The smart background sync ensures automatic data updates, removing the need for manual refreshes. Granular sharing permissions give users precise control over who can view or edit items. Optimised API usage reduces bandwidth consumption by fetching data only when needed. See Our Services.

Technology Stack

We built the frontend using Flutter for both mobile and web platforms. The backend runs on Node.js, while the database relies on Neo4j. Firebase handles the sync mechanism, and local authentication includes periodic online validation. A transport API fetches vehicle data when online.

What We Learned

Developing an offline-first asset management app required rethinking traditional assumptions about constant server access. We designed an efficient local database structure that prioritised fast read/write operations. Users expected real-time-like behaviour even when offline, so we created a queue system that mimics real-time updates without requiring continuous connectivity. To ensure transparent conflict resolution, we introduced a change log that allows users to review modifications and track overwritten data.

The Results

Early testing showed significant improvements: Data entry speed increased by 40% compared to cloud-based competitors that require constant internet connectivity. Sync errors dropped by 80% after implementing queue-based syncing. Beta users provided 90% positive feedback on the app’s offline usability. Collaboration became three times faster than manual data-sharing methods.

User Testimonials

Users provided overwhelmingly positive feedback, highlighting the app’s convenience. A construction manager praised the app for eliminating concerns about losing data access when working on-site. A vehicle owner appreciated the time-saving vehicle lookup feature, and a personal user found the offline document storage invaluable for managing invoices and warranties.

Next Steps

Our future roadmap includes AI-powered merge suggestions for conflict resolution when multiple users edit the same offline item. We plan to expand API integrations beyond transport to include property, electronics, and other asset categories. We also aim to introduce offline-first messaging to enable message sharing between connected users without immediate internet access. Mobile and desktop syncing will be enhanced for seamless transitions between platforms.

Conclusion

“Organise Your Things” successfully delivers an offline-first asset management app, ensuring users can work without internet dependency. With structured categorisation, smart sync logic, and role-based sharing, the app fills a crucial gap for users requiring reliable offline organisation. Unlike traditional cloud-dependent tools, this application provides true freedom and flexibility, making it an ideal solution for professionals and individuals alike.

Looking for a reliable offline-first asset management app to keep your data accessible anytime? Whether you’re managing inventory or organizing personal assets, we provide seamless, secure solutions tailored to your needs. Contact us today to see how “Organise Your Things” can work for you!

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.

AI-Driven Vehicle Maintenance System : Automated Service Alerts

Posted on by Layana Chandran

Introduction

AI-driven vehicle maintenance system fetches data via API, sends automated service reminders, reduces breakdowns, and improves vehicle longevity.

Vehicle owners often forget important service dates, which, according to the National Highway Traffic Safety Administration (NHTSA), contributes to nearly 20% of vehicle breakdowns. As a result, maintenance costs increase, and safety risks become more significant. To address this issue, we developed an AI-driven solution that not only fetches vehicle details from the registration number via an API but also uses artificial intelligence to send timely service reminders. Consequently, this system optimises maintenance schedules and extends vehicle longevity.

Project Details

Aspect Details 
Services Web and mobile app development, AI integration, UX optimization, content management. 
Period January 2024 – October 2024. 
Budget Designed to be SME-friendly with scalable options for future growth. 

Problem Statement

Traditional vehicle maintenance tracking relies heavily on manual efforts. As a result, many owners miss service deadlines, leading to inefficient schedules and unexpected breakdowns. To solve this problem, we needed to automate vehicle detail retrieval and integrate AI-driven notifications for timely servicing. By doing so, we could ensure that vehicles remain in optimal condition while reducing the likelihood of costly repairs.

Solution Overview

Our AI-powered system offers a seamless way to track vehicle maintenance. First, it fetches vehicle details from a government or third-party API using the registration number, ensuring real-time and accurate data retrieval. For instance, the system integrates with the UK’s DVLA API and India’s Vahan API to pull necessary vehicle information. Next, it analyses the make, model, and maintenance history to determine when the next service is due. Finally, automated notifications are sent through SMS, email, or in-app alerts before the due date, helping users stay on top of their maintenance schedules.

Implementation

To begin with, the system retrieves vehicle details by calling an API with the registration number. It then fetches key data, including make, model, year, fuel type, last service date, and mileage. This information is stored in a centralised database, making tracking and analysis more efficient. As the service due date approaches, AI-powered scheduling triggers reminders. In addition, notifications are sent through multiple channels. For example, emails provide detailed service information, while in-app alerts offer personalised recommendations. As a result, vehicle owners receive timely and relevant reminders, reducing the risk of missed maintenance.

Technologies Used

For efficient data retrieval, the system integrates with RESTful APIs. Additionally, it utilises SendGrid for email notifications and Firebase for push notifications, ensuring reliable message delivery. On the frontend, Flutter is used to create a smooth experience across both mobile and web platforms. Meanwhile, the backend is powered by Node.js, which ensures scalability and fast processing. Furthermore, all vehicle data is stored in Neo4j, enabling efficient tracking and retrieval of maintenance records.

Benefits

This AI-driven solution offers several advantages. Firstly, automated tracking eliminates the need for manual service monitoring. Secondly, regular maintenance improves vehicle health and reduces repair costs. Moreover, personalised reminders enhance the user experience, keeping customers well-informed. Lastly, preventive maintenance lowers long-term expenses, making vehicle upkeep more cost-efficient for owners.

Conclusion

By combining API integration with AI-powered reminders, this system significantly improves vehicle maintenance management. In fact, early trials in fleet management have shown a 30% reduction in overdue maintenance cases. As a result, vehicles last longer, and owners save money on unexpected repairs. AI’s predictive capabilities ensure timely service, reducing breakdown risks and enhancing overall efficiency. Ultimately, this solution transforms traditional vehicle servicing into a smart, automated experience that benefits both individuals and businesses alike.

Enhance vehicle maintenance with our AI-driven system that automates service tracking, reduces breakdown risks, and optimises schedules. Whether you’re an individual, fleet manager, or service provider, our solution ensures efficiency and cost savings. Contact us today for a demo and simplify vehicle maintenance.

AI-Powered Solution for Categorisation and Secure Data Sharing

Posted on by Layana Chandran

Overview

AI-powered solution for inbox categorisation & secure data sharing. Boost efficiency, automate tasks & cut costs with intelligent role-based access.

In today’s fast-paced digital world, businesses handle vast amounts of data daily. Managing and categorising this information efficiently is a significant challenge, especially for enterprises looking to streamline operations without exceeding their budgets. We collaborated with a mid-sized enterprise to develop an AI-powered solution designed to simplify inbox categorisation and enable secure data sharing with role-based access. This case study explores the challenges faced, our innovative approach, the solution we built, and the impact it had on the client’s business.

Project Details

To address the client’s challenges, we designed and implemented a comprehensive solution leveraging AI for intelligent inbox categorisation and role-based data sharing. The project aimed to deliver a cost-effective, scalable, and user-friendly platform accessible on mobile and web platforms. The scope included integration with existing systems, a robust security framework, and real-time AI suggestions. 

Aspect Details 
Services Web and mobile app development, AI integration and UX optimisation. 
Period January 2023 – November 2024. 
Budget Designed to be SME-friendly with scalable options for future growth. 

The Client and Their Challenges

The client, a growing mid-sized enterprise, needed a better way to manage large datasets and improve collaboration among teams. Speed and accuracy in data categorisation were critical for decision-making, yet their existing processes relied heavily on manual data tagging. This resulted in delays, inconsistencies, and inefficiencies.

Sharing categorised data among teams also proved to be a challenge. The client lacked a sophisticated yet easy-to-use permission system that would allow for secure and flexible data access. Security concerns were a major factor, as sensitive information needed to be protected while remaining accessible to authorised personnel.

Budget constraints further complicated the situation. The client required an advanced solution that was both high-quality and cost-effective, ensuring that innovation was not sacrificed for affordability.

Why Our Solution?

Our expertise in AI-powered solutions made us the ideal partner for this project. By understanding the client’s needs and constraints, we delivered a solution that automated data categorisation with high accuracy, enabled secure role-based data sharing, integrated seamlessly with both mobile and web platforms, and was delivered on time and within budget.

The Solution

The AI-powered solution was designed to intelligently analyse incoming data and automatically sort it into relevant categories. For example, a property listing document containing terms such as “sale price,” “square footage,” and “address” would be classified under “Real Estate,” while a project proposal mentioning “Budget Breakdown,” “Milestones,” and “Deliverables” would be sorted into “Business Planning.” Similarly, invoices containing fields like “Amount Due,” “Due Date,” and “Client Name” would be placed in “Financial Records.”

Beyond basic categorisation, the system also introduced document association. Uploaded documents were analysed and linked to corresponding categories, reducing the need for manual input. A file titled “Monthly Electricity Bill,” for example, would automatically be placed under “Utilities,” while tax-related documents referring to “Income Statement” and “Deductions” would be stored in the “Tax Documents” category.

To enhance accuracy, the AI model continuously learned from user feedback. If a document was incorrectly categorised and manually adjusted, the AI adapted its logic to prevent similar errors in the future, ensuring a system that became more precise over time.

Secure role-based data sharing was another key feature of the solution. Users could define access levels for shared data, controlling whether a file could be viewed, edited, or deleted. To further enhance security, all data-sharing processes were encrypted, protecting sensitive information during both transmission and storage. Additionally, a comprehensive audit trail provided transparency by tracking who accessed or modified data and when these actions took place.

Key Takeaways

Throughout this project, several important lessons emerged. Developing the solution with direct input from users ensured that it effectively addressed real-world challenges. Post-deployment monitoring and feedback played a crucial role in refining AI performance, allowing continuous improvement based on actual usage patterns. Scalability was another essential factor, as the solution was designed to grow alongside the client’s evolving needs without requiring major overhauls.

The Results

By automating categorisation, the AI-powered solution significantly improved efficiency, reducing manual effort by 70 percent. This allowed the client’s operations team to process 2,000 documents in a single month with minimal manual intervention, compared to just 600 documents previously.

Collaboration within the organisation also improved. The secure, role-based sharing system enabled teams to work together more effectively while maintaining data integrity. Before implementation, only 50 percent of shared documents reached the intended recipient without further corrections. After deploying the new system, that figure rose to 85 percent.

Cost savings were another clear benefit. The client reported a 25 percent reduction in operational costs in the first quarter after deployment, demonstrating that AI-powered solutions can be both high-quality and cost-effective, even for SME clients.

User feedback was overwhelmingly positive, with employees praising the system’s ease of use and intelligent automation. One team member commented that they had “saved countless hours that we can now dedicate to strategic initiatives.”

What’s Next?

Future enhancements will further expand the solution’s capabilities. The next phase will introduce advanced analytics to provide actionable insights into data usage trends, helping the client measure efficiency gains and identify areas for further improvement. Plans are also in place to integrate multilingual AI support, making the platform more accessible for a global user base.

Additional third-party integrations with tools such as CRMs and project management software will streamline workflows and enhance overall functionality. To accommodate the client’s continued growth, scalable infrastructure improvements will ensure the system remains efficient and responsive as the user base expands.

Conclusion

The AI-powered solution for categorisation and data sharing transformed the client’s data management processes. By automating categorisation, enabling secure collaboration, and delivering a cost-effective platform, the solution exceeded expectations. The improvements in productivity, accuracy, and security demonstrated the value of AI-driven innovation in solving complex business challenges efficiently.

With future updates planned for analytics, multilingual support, and third-party integrations, the solution is set to provide even greater value in the long run. This project highlights how AI can simplify data management while remaining accessible and affordable, proving that businesses of any size can leverage cutting-edge technology to enhance their operations.

Struggling with data management and secure collaboration? Our AI-powered solutions streamline operations, enhance efficiency, and improve security—all within budget. Contact us now to discover how AI can transform your business.

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.

Enhance Governance Risk Management | Ricknetic Case Study

Posted on by Layana Chandran

Executive Summary

Enhance governance risk management with Risknetic. Modernised UI, real-time tracking, automation & multi-language support boost efficiency & compliance.

The Environmental Social Action Plan (ESAP), also known as the Risknetic Platform, tracks and manages governance risks while enabling users to take informed actions. This platform supports Portfolio Managers and Investment Teams in monitoring risks and actions, while Client Admins and Client Managers create and manage projects, risks, and corrective actions efficiently.

The existing system needed modernisation to enhance its functionality, automation, and user experience. By rebuilding the system, we significantly improved real-time risk tracking, multi-language support, role-based access control, and bulk data management. This case study highlights the transformation of ESAP and the tangible benefits it delivered.

Project Overview

This project focused on optimising a web application and improving its UI/UX. The technology stack included AngularJS and Yii2. The project ran from January 2020 to January 2021, with a budget designed to be SME-friendly while allowing scalability for future growth.

Aspect Details 
Service Web App and UI/UX Optimisation 
Technology  AngularJS and Yii2 
Period January 2020 – January 2021 
Budget Designed to be SME-friendly with scalable options for future growth.  

The Client and Their Challenges

The client, responsible for managing governance risks and compliance across diverse portfolios and investments, faced multiple operational challenges. The previous system lacked efficiency and responsiveness due to outdated technology, resulting in sluggish performance and a suboptimal user experience.

Portfolio Managers and Investment Teams struggled to access real-time updates on risks and actions, leading to delays in decision-making and operational inefficiencies. Additionally, the legacy system lacked multi-language support, limiting accessibility for a diverse user base.

Role-based access control restricted administrators from configuring dynamic access permissions. The reliance on manual reporting slowed governance risk assessments, increasing errors and inefficiencies. Automation was essential to streamline reporting and improve overall accuracy.

Why They Chose Us

The client selected our team because of our expertise in developing scalable, user-friendly risk management platforms. Our approach prioritised automation, customisation, and enhanced tracking capabilities, ensuring a more efficient and intuitive system. See Our Services

The Solution

To address these challenges, we modernised and automated the system with key enhancements. We rebuilt the entire system to improve speed and user interaction, introducing an intuitive, dashboard-based UI for seamless navigation and risk tracking.

We developed a dashboard for instant monitoring of risks and actions, allowing Portfolio Managers and Investment Teams to track risk status updates in real time.

We integrated a dynamic language preference system, enabling users to switch languages effortlessly. Role-based access control received significant upgrades, offering more flexible role assignments and strengthening security protocols to ensure appropriate access levels for all users.

To eliminate time-consuming manual data entry, we implemented bulk upload capabilities, allowing administrators to import multiple risks and actions in a single operation. These improvements drastically reduced dependency on manual reporting.

Implementation Challenges and Solutions

One of the main challenges involved resistance to new technology, as some users hesitated to transition to the new system. We addressed this by conducting hands-on training sessions, helping users become familiar with the new UI and workflows.

Ensuring data integrity during migration posed another challenge, requiring robust data validation and backup protocols to prevent loss or corruption. Language compatibility issues also emerged when some translated text did not fit within UI constraints. We optimised the UI to accommodate variable text lengths, maintaining consistency across languages.

Results That Speak

Within six months of deployment, the platform delivered measurable improvements. Efficiency increased by 35% due to faster risk tracking and action management. Automated bulk uploads reduced manual reporting effort by 50%. Response times improved by 40% with real-time tracking, enabling quicker decision-making. The enhanced UI and role-based access control contributed to an 85% user adoption rate.

Lessons Learned

User training played a crucial role, with early engagement and training sessions ensuring a smooth adoption process. Customisation significantly influenced user satisfaction, as allowing clients to tailor dashboards and workflows led to higher adoption rates. Scalability emerged as an essential factor, with a flexible architecture supporting future system enhancements and ensuring longevity.

Next Steps

Future plans for the platform include integrating AI-driven risk prediction, using machine learning models to proactively predict and mitigate risks. Advanced reporting and analytics will provide customisable dashboards for deeper insights into governance risks and compliance trends. Additional regional language support will further enhance accessibility for a broader user base.

Transform Your Risk Management System Today

If your organisation requires a modern, scalable risk management solution, our team specialises in delivering tailored systems designed to improve efficiency and compliance. Let’s collaborate to create a future-proof platform that meets your unique needs. Contact Us now to start your journey toward streamlined operations and compliance excellence.

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!

Boost Logistics Efficiency – A Case Study in Operational Efficiency

Posted on by Layana Chandran

Executive Summary

Boost logistics efficiency with AI-powered automation. Improve query resolution by 30%, cut manual work by 45%, and enhance shipment tracking for better CX.

A logistics company faced increasing inefficiencies in handling customer queries and tracking shipments, resulting in delays and administrative burdens. Customers struggled to receive timely responses, while support teams were overwhelmed with repetitive tasks. To resolve these challenges, a smart, automated solution was needed to manage queries efficiently, provide real-time shipment updates, and allow better administrative oversight.

To address these issues, we developed a mobile app for end-users and a bespoke dashboard for administrators. The integration of automated query management and live chat support led to a 30% improvement in query resolution times, a 20% increase in customer satisfaction, and a 45% reduction in manual administrative processes. Within six months, the company transformed its logistics workflow into a highly automated and scalable system, significantly reducing human effort while enhancing operational efficiency.

Project Overview

The project focused on mobile and web application development, utilizing Flutter and WordPress technologies. Implementation took place from January 2021 to September 2021 with a budget designed to be SME-friendly while ensuring scalable options for future growth.

Aspect Details 
Service Mobile App and WebApp 
TechnologyFlutter and WordPress 
Period January 2021 to September 2021 
Budget Designed to be SME-friendly with scalable options for future growth.  

Challenges Before Implementation

One of the primary challenges was delayed customer query resolution, where customers had to wait several hours or even days to receive responses regarding shipment updates, delivery timeframes, and general inquiries. Additionally, inefficient manual query handling meant that support teams processed every query manually, leading to high operational costs and excessive workload. A lack of a centralized data management system further complicated the process, resulting in inconsistent responses and unresolved queries.

The Solution: Integration of a Mobile App and Dashboard

A mobile application was developed to provide users with an intuitive interface for business-related inquiries and real-time shipment tracking. An AI-powered Q&A system matched user queries with predefined responses, ensuring quick and accurate information retrieval. If no match was found, the system redirected users to a live support agent for further assistance. This significantly reduced wait times and improved the overall user experience.

The administrator dashboard allowed client admins to manage the Q&A database, monitor live chat conversations, and refine query handling processes. Admins could modify predefined responses, oversee interactions, and ensure continuous improvement by adding new queries and responses based on user feedback. The dashboard also featured analytics and query tracking capabilities to optimize the AI-powered Q&A system’s efficiency.

How These Two Technologies Work Together

When a customer submits a query in the mobile app, the system searches the predefined Q&A database. If a relevant response is found, the system provides an instant reply. If no match is detected, the query is escalated to a client admin through live chat, where an administrator can manually resolve the issue and add it to the database for future automation. Shipment tracking requests are handled seamlessly within the live chat, eliminating the need for users to switch applications.

Implementation Challenges and Solutions

One of the initial challenges was improving AI matching accuracy, as the system had difficulty recognizing variations of similar queries. By training the AI using over 5,000 real customer queries, accuracy improved by 35%. Another challenge was integrating real-time shipment tracking without manual input from support agents. This was resolved through the integration of shipment tracking APIs that dynamically fetched data based on order numbers provided by users.

User adoption and training also posed difficulties, as some administrators were hesitant to transition from manual processes to automation. A structured onboarding program and interactive training modules helped achieve a 90% adoption rate, ensuring smooth implementation.

Results That Speak

The implementation led to a 30% faster query resolution time, enabling customers to receive instant responses without delays. Customer satisfaction increased by 20% due to improved self-service options and the availability of live support when needed. Manual administrative work was reduced by 45%, allowing support teams to focus on complex issues, thereby reducing overall operational strain.

Lessons Learned

AI-powered automation requires continuous refinement, with administrative oversight playing a crucial role in maintaining accuracy and relevance. Real-time data syncing proved to be essential, as fast API connections for shipment tracking significantly improved response times. Effective change management was a key factor in successful implementation, requiring ongoing training and internal buy-in to ensure smooth adoption.

Next Steps

Future improvements will include the enhancement of predictive query suggestions, where AI will suggest relevant responses even before a question is fully typed. Expanding multilingual support will allow the platform to cater to international users by incorporating French, Spanish, and German languages. Additionally, advanced analytics will be introduced through BI dashboards to analyze query trends and customer interactions, providing deeper insights for administrators.

Final Thoughts

By integrating a mobile app for end-users and a dashboard for administrators, this solution successfully streamlined logistics operations while enhancing customer engagement. Businesses seeking a similar approach can leverage this model to automate customer queries, improve shipment tracking efficiency, and optimize support operations.

Looking to streamline logistics with AI-powered automation? Our solutions enhance efficiency, reduce manual work, and improve customer satisfaction. From automated query management to real-time shipment tracking, we help optimize operations. Contact us today to transform your logistics workflow and drive success!

AI-Driven Tools: Revolutionising Sales Operations | Salesfeel

Posted on by Layana Chandran

Executive Summary

Boost sales efficiency with AI-driven tools! Automate reporting, optimise field operations & cut costs. See how AI transformed this pharma company’s success!

A pharmaceutical company struggled with inefficiencies in its sales operations, including manual reporting, limited field monitoring, cumbersome expense management, and inconsistent communication. By adopting AI-driven tools, the company streamlined workflows, reduced reporting time by four hours per agent each week, increased productivity by 15%, and optimised resource management. This case study highlights how tailored digital solutions transformed business processes and accelerated growth.

Project Overview

The project focused on optimising mobile and web application UI/UX using Flutter, Yii2, and AngularJS to ensure seamless functionality. Conducted between January 2019 and April 2021, the initiative offered SME-friendly pricing with scalable options for future expansion.

Aspect Details 
Service Mobile APP and Web APP UI/UX Optimisation 
Technology  Flutter , Yii2, And Angular JS 
Period January 2019 to April 2021 
Budget Designed to be SME-friendly with scalable options for future growth.  

The Client and Their Challenges

The pharmaceutical company, which managed over 200 field agents, faced major operational hurdles. Agents spent five hours each week compiling handwritten reports, often submitting them late or incomplete, creating operational blind spots. Managers lacked real-time oversight, which led to unproductive travel routes and uneven workloads. Expense management was inefficient, as approval delays for travel and daily allowances caused frustration among agents. Static email PDFs served as the primary method for product knowledge updates, but many agents ignored or forgot them before client interactions. Additionally, inefficient tracking of product sample distribution led to a 30% surplus in annual costs.

Why They Chose Us

The company selected our team because of our ability to deliver cost-effective, AI-driven solutions with minimal disruption to existing workflows. Our emphasis on measurable ROI and ease of use set us apart from competitors. See Services Archive | AI-Driven Web, Mobile Apps for SMEs, Startups

The Solution

We developed a mobile application for field agents and a web-based admin dashboard for managers. Automated reporting replaced manual entries, reducing reporting time from five hours to just one hour per week. AI-powered pre-filling used historical data to streamline data entry, allowing agents to log daily visits and orders in real-time.

GPS-enabled location tracking provided managers with real-time oversight of agent movements, optimising travel routes and reducing unnecessary trips. Agents uploaded receipts for travel and allowances directly into the app, where AI flagged incomplete or duplicate claims, cutting approval delays by 50%.

We also introduced on-demand training resources, including videos, product details, and quizzes. A gamified approach encouraged completion, and performance analytics helped managers assess agent readiness for client meetings. Attendance tracking allowed managers to review productivity based on daily check-ins and client visits. For better resource allocation, an AI-driven system monitored product sample distribution, reducing waste and identifying high-demand regions.

Implementation Challenges and Solutions

Many agents initially resisted the new technology. To address this, we conducted interactive workshops and live demonstrations, which quickly improved adoption rates. Some AI models misclassified expense claims and report entries in the early stages. By incorporating user feedback and retraining models, we improved accuracy by 60% within two months. Integrating the app with existing CRM and accounting systems posed another challenge, but our custom APIs ensured a seamless data flow.

Results That Speak

Within six months, field agents increased productivity by 15%, directly contributing to a 20% revenue boost. Improved sample tracking reduced wastage by 25%, while automated reporting saved each agent an average of four hours per week. With AI optimising their workflow, agents spent more time engaging with clients rather than handling administrative tasks.

Lessons Learned

Iterative development played a crucial role in the project’s success. By continuously refining features based on user feedback, we ensured system reliability. Transparent communication and hands-on training helped ease the transition. A user-focused design approach led to high adoption rates and positive engagement.

Next Steps

The company plans to enhance fraud detection by using AI to identify high-risk expense claims. Sentiment analysis will help monitor customer satisfaction and predict churn. Additionally, adaptive learning tools will personalise training modules, improving agent performance.

Transform Your Business Today

Struggling with inefficiencies? AI-driven tools can transform your operations. Our team specialises in affordable, scalable solutions tailored to your challenges. Let’s create your success story together—contact us today to get started.