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Background : A SaaS (Software as a Service) provider offered its clients a comprehensive business management platform. With a multi-tenant architecture and continuous updates based on user feedback, ensuring backward compatibility and stability was a formidable challenge. Challenges : Frequent updates and releases required extensive regression testing to cover various use cases, data sets, and integration points. Automated Testing limitations were another challenge because it does not cover all edge cases. Manual testers were required to ensure backward compatibility and stability even though Automated Testing helped with repetitive areas. CI/CD pipeline had broken due to changes in code, and dependencies. Therefore, maintaining a stable pipeline and quickly resolving issues requires a proactive approach. While the QA team regularly updated and expanded test suites to cover new features and edge cases, the Dev team made some improvements from their end for version controlling and branching. Apart from that, Root Cause Analysis was another good thing the team did to identify underlying causes and prevent similar problems in the future. However, the below points were not among our challenges. Oh yes... - User and Client Dependencies such as having a diverse user base using various software versions. - Client customizations with customized implementations based on specific versions. Strategy : The testing team introduced Regression Testing as a Service (RTaaS), offering a dedicated regression testing framework that seamlessly integrated with the SaaS platform's release cycle. Clients could opt-in to scheduled regression testing cycles or trigger ad-hoc regression tests before major updates. Understand the SaaS Platform Structure: - Identify the architecture, APIs, and data flows within the SaaS platform. - Understand the user journeys and critical functionalities that require regression testing. Define Regression Testing Scope and Objectives: - Determine which tests should be included in regression testing (e.g., critical business functions, integrations, security features). - Set objectives such as minimizing test coverage gaps, reducing test cycle time, and ensuring stable platform functionality. Choose a Test Automation Framework: - Select a robust test automation framework that supports the programming languages and technologies used in the SaaS platform. - The framework includes Selenium and TestNG. Design Test Suites for Regression: - Create modular test suites that focus on different parts of the SaaS platform (e.g., frontend, backend, APIs). - Include both positive and negative test cases to cover various scenarios. Implement Continuous Integration and Continuous Deployment (CI/CD): - Integrate regression tests into the CI/CD pipeline to ensure automated and consistent testing with every code change. - Tools like Jenkins, GitLab CI, or GitHub Actions can be used for CI/CD automation. Use Cloud Infrastructure for Scalability: - Leverage cloud-based infrastructure to run regression tests at scale, enabling faster test cycles and parallel test execution. - Considered service Google Cloud for scalable test execution. Implement Test Data Management: - Develop a strategy for managing test data, ensuring it remains consistent and relevant across test environments. - Consider using synthetic data to reduce dependency on production data and to address data privacy concerns. As an example: Generating synthetic data for a banking application involves creating artificial data that resembles real-world banking transactions, customer profiles, accounts, and other related information. The goal is to create data that has similar characteristics and patterns to actual banking data without using real customer information, ensuring privacy and security. Tools like Mockaroo can be used for generating synthetic data with customizable fields and schema. Create a wide range of scenarios to test different cases in the banking application. This might include: - Different account types with varying balances. - Diverse customer profiles with different demographics. - Introduce edge cases and anomalies to test robustness (e.g., fraudulent transactions, accounts with multiple overdrafts). Monitor and Maintain Regression Test Suites: - Regularly update and maintain test suites to reflect changes in the SaaS platform and to address test flakiness. - Implement test analytics to identify trends, track test results, and pinpoint areas for improvement. Develop a dashboard to provide real-time visibility into regression test results: Even though creating a Regression Testing Dashboard is a good strategy, it was not considered. Implementation : Leveraging cloud-based testing infrastructure, the RTaaS platform executed regression tests in isolated environments tailored to each client's configuration. Test results and impact analysis were transparently communicated to clients, empowering them to make informed decisions about software updates. Results : The RTaaS model provided clients with assurance of software stability and compatibility without the overhead of maintaining an extensive testing infrastructure. By outsourcing regression testing to experts, clients could focus on their core business activities while benefiting from continuous assurance of software quality. The platform's proactive approach to regression testing bolstered client trust and loyalty, driving customer retention and business growth. In conclusion , effective regression testing strategies are indispensable for ensuring software stability amidst continuous updates. Whether adopting an agile approach, leveraging risk-based testing methodologies, or embracing innovative testing-as-a-service models, organizations can mitigate regression risks and deliver superior software experiences to users. By drawing insights from these case studies, software development teams can refine their regression testing practices and navigate the ever-changing landscape of modern software development with confidence. Therefore, providing Regression Testing as a Service (RTaaS) allows clients to customize their regression testing based on their unique needs, business priorities, and urgent requirements. Here are a few questions for you. You can contribute to any. 1. What advantages do you see in offering Regression Testing as a Service (RTaaS)? 2. What is your opinion on using synthetic data for regression testing? If you create your own dummy data, have you encountered any privacy issues? I still recall the test data I used during the early days of my time as an Associate QA engineer. :D 3. What types of projects do you typically use synthetic data for? 4. What are the test strategies you have used to ensure backward compatibility and system stability? 5. What is the dashboard use used to provide real-time visibility into regression test results? 6. What are the challenges you faced with CI/CD integration? Thanks for your time. Subscribe to our newsletter

Background : A financial services company developed and maintained a complex software platform for financial management. With regulatory changes and evolving market requirements, ensuring software stability amidst frequent updates was critical. Here’s an overview of what risk-based regression testing involves and how it's applied in a financial context: Strategy : The testing team implemented a risk-based regression testing approach, focusing testing efforts on areas of the application most susceptible to regression issues. They collaborated closely with business analysts and domain experts to identify critical functionalities and prioritize test cases accordingly. Risk Identification : Determine the risks associated with the finance transactions. What if there are software changes? What if there is a downtime? Are there any workarounds? Are there any options to recover the frauds or invalid transactions? If there are any, is there a log facility to get sufficient information? This includes analyzing which parts of the system are most likely to have defects or are critical to business operations. Therefore, risk identification in many aspects is the first key thing. Risk Assessment: Assess the impact and likelihood of each risk. Consider factors like data sensitivity, transaction volume, and potential financial loss. Data Sensitivity: Financial software handles sensitive data like personal identification information, credit card numbers, bank account details, and transaction records. If a defect leads to a data breach or corruption, the impact could be severe, including reputational damage, legal consequences, and financial loss. High-sensitivity areas should be tested rigorously. Therefore, proper validations are required for these data fields. Transaction Volume: Financial software often processes large volumes of transactions. A defect in high-volume areas can lead to significant financial loss, data inconsistencies, or service disruptions. Test cases should focus on ensuring these transactions are accurate and reliable. Potential Financial Loss: Some parts of financial software are directly linked to revenue generation, such as payment gateways or trading systems. A defect in these areas could result in lost revenue, unauthorized transactions, or fraud. Test cases should verify the integrity and security of these components. These areas need to be tested thoroughly. Therefore, the team always ensured the test coverage by adhering with test case designing techniques. Generating mind maps, flow charts or decision tables always helped to ensure the test combinations. Therefore, deriving test cases based on that ensures the full test coverage. Preparing the Traceability matrix also ensured the test coverage with business requirements. Therefore, going with these test procedures ensures the full test coverage in these risky functions. Risk-based test prioritization : This is the next step after identifying the risks and doing a risk assessment. Risk-based regression testing in financial software involves prioritizing test cases based on the potential risks associated with the software changes. This approach is crucial for financial systems, which often handle sensitive data and complex transactions, as it focuses testing efforts on the areas where failure could cause the most significant harm. Implementation : Leveraging test management tools, the team categorized test cases based on their impact on core business processes and requirements. The team adopted a hybrid testing approach, combining automated regression tests for existing functionalities with manual testing for changes and edge cases. Results : The risk-based regression testing approach proved instrumental in optimizing test coverage and resource allocation. By directing testing efforts toward high-impact areas, the team maximized the effectiveness of regression testing while minimizing redundant tests. As a result, the software exhibited enhanced stability, resilience to change, and regulatory compliance according to the business requirements. In conclusion , the adoption of a risk-based regression testing approach significantly improved the reliability and robustness of the financial services company's software platform. By prioritizing test cases according to identified risks and focusing on critical functionalities, the testing team was able to effectively manage the complexities of frequent software updates in a highly regulated environment. Key outcomes of this approach included enhanced software stability, reduced redundancy in testing, and more efficient resource allocation. The close collaboration with business analysts and domain experts facilitated a comprehensive risk assessment, which underpinned the success of the testing strategy. The team's meticulous focus on data sensitivity, transaction volume, and potential financial loss ensured that critical areas of the platform were thoroughly tested, reducing the likelihood of severe defects and their associated risks. Overall, the risk-based regression testing approach proved to be a strategic and effective solution for managing the challenges of a complex financial software platform. It serves as a valuable model for other organizations in the financial sector seeking to maintain high-quality software in a dynamic regulatory landscape. I recently visited a bank and had to wait for over an hour. While I was there, I noticed a few customer complaints. One involved a young customer who was having trouble with an online fund transfer. By mistake, he had sent money to the wrong account number. As he was filing his complaint, the bank officer mentioned that the issue could have been reported through an online platform, saving him time for the bank. Then the customer responded that he had looked for such an option but couldn't find it. The officer then logged into the customer portal and showed him where it was. The key point of this is that while it's crucial to prioritize the functionality of financial transactions, it's equally important to overlook the UI/UX aspects. Ignoring them can lead to frustration and mistakes that could have been avoided. I'd like to hear your perspective on this, too. Subscribe to our newsletter!

Project Background: In today’s competitive eCommerce landscape, businesses continually seek ways to improve their conversion rates—turning site visitors into paying customers. One way of optimization of these rates is through A/B testing which is when two versions of a webpage or checkout procedures are compared in order to determine which is better. The objective is to make data-driven decisions that enhance user experience and ultimately increase sales. In this article, we are going to discuss about how we are going to boost the conversion rates of an eCommerce website by conducting A/B tests on critical pages, and the checkout process. Strategy: The first step in the project will involve selecting the key pages and elements that have a significant influence over conversion rates. If the client had done some A/B testing rounds before and if they had the awareness of what key pages you need to consider for A/B Testing then that awareness is going to be useful for QA. The following strategy can be developed to optimize the Conversion Rates by Testing Different Versions of Web pages and Checkout Process: 1. Define Goals: The first strategy is the clear-cut goals of the client is to be stated. The rest of the tasks will be dependent upon the objectives that are to be accomplished by the client. Here, the team should establish clear goals for the A/B tests, such as increasing the number of completed purchases, reducing cart abandonment, or improving click-through rates on specific calls to action. 2. Analyze user behavior data: If prior test rounds has been performed by the client end then the team should thoroughly go through the previous reports as the next step. After that, the team should take more quantitative data for the last couple of months of the traffic volume and then see how users have been engaged with the web page or the checkout process via heat maps, session recordings, and funnel analysis. Furthermore, dead clicks and rage clicks are also the metrics that can be taken into consideration for this study. 3. Hypothesis Formation: Based on user behavior data and analytics, the team should form hypotheses about what changes could lead to improvements. For instance, simplifying the checkout process might reduce abandonment rates, or a more prominent "Add to Cart" button might increase conversions. Sometimes the checkout process can be associated with many steps and the user has to go through these steps by clicking on the "Next" buttons. It may have a facility to view all details on one page. However, in previous projects, the team noticed that some information disappeared when switching from these views. Therefore, the user had to re-enter the given details and we noted that the checkout process confused the user. Therefore, the checkout process should be a simplified one. 4. Create Variants: Variants can be suggested for multiple versions of the web pages and checkout steps to test. One type of design may have a one-page checkout while the other type of design may require going through several steps. The team should give more weight to the checkout process apart from the below Product and search pages when conducting A/B testing with different variants. Here are some key aspects the team can consider about: 4.1 Product Pages 4.1.1 Product Images - Size and Quality: Test high-resolution images versus smaller or lower-resolution ones. - Image Gallery Layout: Compare different image gallery styles, like thumbnails below the main image versus a slider. - Zoom Functionality: Test enabling or disabling the zoom feature on images. 4.1.2 Call to Action (CTA) Buttons - Button Text: Test different texts like “Add to Cart” versus “Buy Now” versus “Purchase.” - Button Color: Test the impact of different button colors on click-through rates. - Button Size and Placement: Test larger buttons or reposition them on the page. 4.1.3 Product Descriptions - Length of Description: Test long, detailed descriptions versus shorter, concise ones. - Formatting: Experiment with bullet points versus paragraphs, or bold text for key features. - Tabs vs. Accordion Layout: Test how information is presented, such as all on one page versus behind tabs or in an expandable accordion. 4.1.4 Pricing Information - Display of Discounts: Test different ways of showing discounts, such as percentages off versus the actual amount saved. - Price Placement: Test whether placing the price higher or lower on the page affects conversions. - Payment Plan Options: Test the visibility or format of installment payment plans. 4.1.5 Customer Reviews - Review Placement: Test the placement of reviews, such as above the fold versus below product details. - Review Summaries: Experiment with summary boxes showing average ratings versus detailed reviews. - Inclusion of User-Generated Content: Test incorporating customer photos or videos. 4.1.6 Trust Signals - Badges and Icons: Test the presence of trust badges, such as “Money-Back Guarantee” or “Free Shipping.” - Security Seals: Test the visibility of security seals, especially near payment options. 4.2. Search Pages 4.2.1 Search Filters - Filter Placement: Test filters on the left sidebar versus a top horizontal bar. - Default Filter Settings: Experiment with defaulting to popular filters like “Best Sellers” or “Lowest Price.” - Expandable vs. Collapsible Filters: Test whether filters are expanded by default or need to be clicked to expand. 4.2.2 Sort Options - Default Sort Order: Test different default sorting options, such as relevance, price, or best-selling. - Sort Menu Design: Experiment with how the sort options are presented, like dropdown menus versus tabs. 4.2.3 Search Results Layout - Grid vs. List View: Test the impact of displaying search results in a grid format versus a list/pagination. - Number of Products per Page: Experiment with showing more or fewer products per page. - Image Size in Results: Test larger images versus smaller thumbnails. 4.2.4 Product Information Display - Amount of Information: Test showing more information (price, ratings, brief description) versus just product name and price. - Quick View Option: Test a quick view pop-up versus requiring users to click through to the product page. - Pagination vs. Infinite Scroll 4.2.5 Page Navigation: Test traditional pagination versus infinite scroll to see which method keeps users engaged. 4.2.6 Load More Button: Test a “Load More” button versus automatic loading of more products as users scroll. 4.2.7 Highlighting Sale Items - Badge Visibility: Test the effectiveness of sale badges or tags on search results. - Prominent Sale Section: Experiment with dedicated sections for sale items within search results. 4.2.8 Search Bar Design - Search Bar Placement: Test the size and prominence of the search bar, including default text like “Search Products.” - Auto-Complete Suggestions: Experiment with different styles and types of search suggestions. 5. Test Execution: A/B tests should be implemented by dividing the traffic equally between the different versions. Tools like Google Analytics and Microsoft Clarity can be used to manage the testing process and collect data. 6. Data Collection & Analysis: The performance of each version should be tracked and analyzed against the established goals. Metrics like conversion rate, average order value, and time spent on the page will be crucial for this analysis. If needed the support of AI tools can be taken for the analysis. However, it will depend on the client's budget as most of those are paid tools. Challenges & Solutions: Traffic Volume will be a challenge Solution: Ensuring that the eCommerce site have sufficient traffic to generate statistically significant results is important. As the solution, testing needs to be done over a longer period and during peak traffic times to gather enough data for reliable insights. 2. Managing Multiple Tests will be a challenge Solution: To avoid interference between tests and ensure accurate results, tests need to be run sequentially rather than concurrently. This method will help isolate the impact of each change. 3. Technical Implementation will be a challenge Solution: The QA team should closely collaborate with the development team to overcome the technical challenges. Regular meetings and a clear testing roadmap will keep the project on track. Results: The A/B testing can provide significant improvements in conversion rates. Booking.com is one good example that got the desired benefits via A/B testing. Via A/B testing, they tested different versions of the booking button. They considered certain factors like color, size, and placement to see which version led to higher conversions. They also experimented with urgency indicators, such as showing the number of rooms left and check how many people are currently viewing a property. Booking.com gained the expected benefits via this strategy. They did the optimizations through continuous testing. As the Outcome: Booking.com has consistently improved its conversion rates, leading to higher revenue and customer satisfaction. Conclusion: A/B testing has proved to be a valuable tool in optimizing the eCommerce platform’s performance. By systematically testing and refining different versions of webpages and checkout processes, the project can be achieved substantial gains in conversion rates. The success of this initiative underscores the importance of data-driven decision-making in eCommerce and highlights A/B testing as a critical strategy for continuous improvement. Check out our newsletter! #casestudy #ecommerce #softwaretesting #sales #vitesters #growth

Background: Integrating e-commerce platforms with Enterprise Resource Planning (ERP) systems is crucial for businesses aiming to enhance operational efficiency and deliver a better customer experience. This enables the synchronization of real-time data between online sales channels and backend operations such as inventory management, order processing, financial accounting and customer relationship management. For a Software Testing project, ensuring the reliability and functionality of this integration is paramount, as it involves validating that data flows correctly between the systems, processes are automated accurately, and there are no disruptions in business operations. Strategy: 1. Assessment and Requirement Gathering: 1.1 Analysis: Determined the business requirements and objectives for the integration. Identify key data points and processes that need to be synchronized. 1.2 System Evaluation: Evaluated the current eCommerce and ERP systems to understand their integration capabilities and limitations. 1.3 Prioritize Requirements: Identified complex areas and high-priority business requirements give the highest priority 2. Understanding Business Requirements Goals and Objectives: The team clearly defined what the integration aims to achieve. For this project, it was identified as streamlining the order processing and enhancing the customer experience. Therefore, gathering inputs from relevant stakeholders to understand their expectations and requirements was done in this stage. 3. Test Planning The Test Plan was prepared by identifying the scope and out-of-scope features. Apart from the main things of a Test plan, the communication channel, test templates, and test environment setup were finalized initially. Data Synchronization: Ensuring real-time data updates between eCommerce and ERP systems. Order Processing: Validating the end-to-end order lifecycle, from placement to fulfillment. Inventory Management: Checking accurate inventory updates and stock levels. Financial Transactions: Verifying accurate and timely financial entries and updates. There were workflows to do approval and rejections and it was handled via the Admin module. This had to be done based on the user's segregation. Therefore, this is also taken for the Test scope. 4. Test Case Development: 4.1 Functional and Integration Testing: The team developed test cases to validate the functionality of the integration. Ensure that data synchronization, order processing, inventory updates, and financial transactions work as expected. As the Order processing was flexible with cancellations/rejections, test cases were developed to test the accuracy of inventory and financial entries. Apart from that many edge cases were considered to test the system's restrictions for invalid user transactions that can cause a major impact to the inventory and finance modules. Apart from verifying the flow, the team developed many negative test cases to identify whether the user can skip certain steps and complete the flow whether the user can make certain modifications to the utilized data. (Ex: Changing item price details when that item was utilized in a Sales invoice, etc...) At that time there was no version management for these modifications. whether the user can do flow steps without going through the flow's order These verifications were heavily dependent on the responsibility of the Admin user. Therefore, most of these test cases were developed as System Limitations. Many improvements were suggested along with a detailed audit log. For eCommerce: 4.2 Apart from verifying the order processing of the eCommerce system, test cases were generated for UI/UX Testing and Compatibility Testing. Based on the client's requirements, browsers and devices were selected. 4.3 Test cases for data mapping The team developed test cases specifically for data mapping validation. Test the end-to-end data flow to ensure data is accurately transferred and transformed. For this project, the team did not do Performance and Security testing. We plan to include "Business Intelligence Testing" for the next projects. Challenges & Solutions: Incorrect Data Mapping: Data fields in the eCommerce platform are not correctly mapped to corresponding fields in the ERP system. There were some differences in data formats, structures, or values between the eCommerce platform and ERP system. As the solution, detailed Mapping Documentation was prepared and its validation was checked initially. The data mapping documentation was reviewed and approved by all stakeholders. Handling many critical issues: The team had to handle many issues and most of them were encountered due to unavailability of restrictions and version management. Therefore, some master data were overridden with the latest modifications and there were many inconsistencies in the final reports. Some transactions were crashed without giving proper error messages. And there were no revert mechanisms to handle certain finance transactions. As the solution, once the team identified these critical issues, they handled test data with proper identifications. Testing of the negative test cases and edge cases was done at the last after completing the happy paths. Issues in workflows: The issues and weaknesses in the workflows brought up many distractions to testing. As a solution, this testing is also done at the last stage after identifying the status of the application. The team suggested many recommendations to implement the restrictions to avoid flaws in the workflows. Results: Optimized Efficiency: The incorporation of the systems made sure that processes were simplified, leading to less manual data entry and lesser errors. Order processing lengths were significantly decreased, causing order delivery to happen quickly and delighting customers. Real-time Data Synchronization: This well-tried integration ensured timely updates between eCommerce and ERP systems by providing correct levels of inventories, financial details, and information about clients. Better Reporting and Analytics: The integration of data has allowed for detailed analysis and report generation that provides knowledge on sales patterns, stock management, and financial performance. Conclusion: The integration of eCommerce platforms with ERP systems is essential for businesses looking to enhance their operational efficiency and customer experience. A well-planned and thoroughly tested integration can lead to significant improvements in data accuracy, process automation, and business agility. By addressing challenges such as data inconsistencies, integration downtime, scalability, and security risks through meticulous testing and robust solutions, businesses can achieve a reliable integration that drives growth and success. Check out our newsletter! #vitesters #ecommercetesting #erptesting #integration #softwaretesting #softwarequlityassurance #uiuxtesting #compatibilitytesting #blog

Background: UI/UX is important in eCommerce development because it directly impacts the overall user experience and customer satisfaction. The application's success depends on how it allows the users to interact with it in a user-friendly way. A well-designed user interface associated with a better user experience enhances customer satisfaction. A well-designed UX/UI improves navigation, reduces friction, increases conversions, and builds brand loyalty. The role of UI/UX testing for eCommerce websites will be discussed in today's article. The purpose of this testing was not merely focused on finding out bugs. The team focused on pointing out the improvements based on their industry experience to keep the client's website in the top position among their competitors. Strategy: The eCommerce platform employed a multi-phase strategy to test and enhance its UI/UX. Analysis of the user requirements: The team had discussions with the stakeholders to understand the target audience's types, needs, preferences, and pain points including competitors. Wireframes and prototypes designed by the development team were reviewed thoroughly and doubts were clarified in the initial stage. As the website was live for a reasonable time, the team could analyze the customer's complaints received so far. It was a really good step to have a better idea about the real users. That analysis helped to know about the confusions the end users had even though some functionalities were already available in the website. Analysis of the metrics: The results of A/B testing conducted by the client were analyzed thoroughly. They have done it for some key pages such as the homepage, product pages, and checkout process page with the changes of colors, navigations, and user interfaces. Along with that, the metrics they have taken for click-through rates, bounce rates, and conversion rates are also analyzed. It helped us to have a better idea about the end user's preferences. The heatmaps they used to identify patterns, popular features, and drop-off points also helped the team to do the analysis. Based on their metrics such as Cart Abandonment Rate, the team gave priority for respective functionalities such as the 'Check out' function to see how the QAs can suggest the necessary improvements. Challenges and Solutions: Testing the UI/UX aspects of the website does not bring us challenges. We considered the analyzed challenges and pain points of the client. When the team started the testing, the client had addressed some challenges. But QA perspective points have missed out. So, we addressed those points by testing the website with a mix of QA and end-user views and pointed out our suggestions for necessary improvements. High Cart Abandonment Rate was one of the challenges they had. At the time we tested the system, they had reduced the number of steps of the Checkout process. However, they did not address some issues in the Checkout process. Not keeping the given delivery information in the cache was one issue they had. So, if the end-user wanted to amend the cart items and return to the checkout process, it created additional steps for them. Basically, the end user had to enter all the delivery information again when coming back to the Checkout page. Therefore, we suggested many improvements for this functional area as some solutions to overcome this challenge. Product search difficulties The team noticed that the product search functionality was not sufficient for end users to get the required products though it was implemented with some filters. As the solution, the team suggested some search mechanisms including a predictive search feature along with a product comparison feature to let the user compare the product's features on one page. Navigation Difficulties Even though some features were available on the website, some were not available to find out and navigate easily. Advanced search filter was one of them and it was available under another option called "More". Even though it keeps the UI clean with fewer links, it was a hidden feature. Additional product information with promotional elements was the other. As the solution, the team suggested to bring them to the UI in effective ways. Apart from that, there were some inconsistencies across the navigations. Some navigations exist as breadcrumbs while others exist inside the images. So, the team addressed all these inconsistencies to enhance the navigation. Mobile Usability Issues This challenge exists from our end. Even though some features worked and looked well on the browsers, they did not look well on the devices. Elements positions and overlapping issues, alignment issues, and font inconsistencies were encountered by the team. Fixing these bugs in one device, raised similar bugs on the other devices. Therefore, the team had to conduct multiple test rounds to take it to a stable version. As a solution, the team implemented a responsive design approach to ensure the platform was fully optimized for mobile devices. Conclusion: The UI/UX testing and subsequent improvements led to significant positive outcomes. The case study demonstrates the pivotal role of UI/UX testing in the success of an eCommerce platform. By understanding user needs and continuously iterating on design improvements, the platform was able to provide a superior user experience that translated into higher conversions, greater customer satisfaction, and increased loyalty. The results underscore the value of investing in comprehensive UI/UX testing as an integral part of eCommerce development strategy. Through meticulous research, testing, and implementation, eCommerce platforms can significantly enhance their performance and achieve long-term success. Finally, all these efforts help remove the friction points and increase user experience. Some of the world's leading eCommerce retailers like Amazon and Shopify used continuous and thorough UI/UX testing. As you know, Amazon changes user interfaces by improving the navigations and implementing new elements. Their strategies are focused on the customer's experience. Check out our newsletter! #vitesters #ecommercetesting #softwaretesting #softwarequlityassurance #uiuxtesting #compatibilitytesting #blog

Background: Successful and secured payment in the checkout process of an eCommerce platform is a must to ensure that the customer's payment goes through safely. Any loophole will result in a loss of customers by losing customer satisfaction and trust. Apart from the other key testing types, API testing for payment gateways is highly required to ensure the integration of the applications. This edition discusses API integration testing with third-party payment providers and how it can be effectively carried out. Strategy: The team went with the below steps of the test strategy by identifying the key Integration Points. 01. Create detailed maps of all the key system touchpoints with third-party payment APIs: payment initiation, confirmation, refund management, and error flows. 02. Understand the requirements and spec. Study the API docs they provided (endpoints, request/response formats, auth; error codes, etc) Define transactions (Credit Card Payments, refunds, voids..) 03. Information Gathering Collect Information for all master cards for creating credit card numbers for testing (Dummy cards) Get Payment Gateway Details Collect the error codes together with payment gateway docs (Where is an error, is it an application bug or payment gateway error?) 04. Test Planning & comprehensive Test Coverage: Develop test cases that cover a wide range of scenarios, including successful transactions, declined transactions, partial payments, and refunds. Ensure all possible outcomes are tested. 4.1 Identify what needs to be tested. Parameters passed through the payment gateway and application Amount related information (via the query, string, or variable) Check the format of the currency Check session time out When the payment gateway is down Verify the encryption of Database entries for the transaction (credit card details are stored or not) Transaction limits if there are any 4.2 Develop Test Scenarios: Successful Transactions: Payments that have successfully been processed. Failed Transactions: Cases where payment fails such as insufficient funds, incorrect card details, expired cards, etc. Edge Cases: Abnormal versions of the scenarios like; minimum and maximum transaction scenarios, and field entries with special Characters. Refunds & Cancellations: These are scenario-based examples that represent an end to end flow of transactions from refunds - to cancellation. Currency handling: Transactions in multiple currencies if required 05. Define Test Data Prepare a mix of valid and invalid test data, including various card numbers, expiry dates, CVVs, and transaction amounts. 06. Testing API Endpoint Testing: Authentication - Make sure the API does what it says it does with security, making use of anything from API keys, tokens, or OAuth. Authorization - There needs to be proper authorization for different transactions for different user roles. Transaction Processing - Test endpoints for Create Transaction, Capture Payment, Void Payment, and Process Refund. Integration Testing: End-to-End Flow - Test from entering the payment details till the final confirmation or the error message. Validation Testing: Response Codes: Check that the API returns the correct HTTP status codes (e.g., 200 for success, 400 for bad request). Response Content: Validate the structure and content of the API responses against the expected schema. Some Test Cases for API Testing: Test cases of API testing are based on Return value based on input condition: it is relatively easy to test, as input can be defined and results can be authenticated Does not return anything: When there is no return value, the behavior of API on the system to be checked Trigger some other API/event/interrupt: If the output of an API triggers some event or interrupt, then those events and interrupt listeners should be tracked Update data structure: Updating data structure will have some outcome or effect on the system, and that should be authenticated Modify certain resources: If an API call modifies some resources then it should be validated by accessing respective resources Challenges & Solutions: Challenge: Error Handling and Edge Cases Solution: Create detailed test scenarios which correspond to different use cases with rare and common error conditions. It has to have test cases for network failures, wrong data, transaction timeouts etc. Error Handling Testing Scenarios: Create test cases to test error handling (e.g., insufficient funds(balance), expired cards and invalid CVV.) Edge Cases: These are all test scenarios with boundary values (e.g., maximum and minimum transaction amounts) and unusual input data (e.g., special characters in fields). Error Handling: Ensure that Transaction timeouts and retries are handled at the API level according to our retry logic. Challenge: Handling Multi-Currency and International Payments Solution: Currency Testing: Develop test cases that cover transactions in different currencies, ensuring that currency conversions and international payment processing work correctly. Localization Testing: Test the system’s handling of various localization aspects, such as different date formats, decimal separators, and language settings, to ensure a smooth user experience for international customers. Normally, managing test data is challenging when handling sensitive data. However, the team does not need to use any data masking techniques because dummy credit card numbers were used for various master cards in a controlled, isolated environment. The team used of well-known Dummy Data: If the dummy credit card numbers are recognized as non-real, well-known test numbers (e.g., 4111 1111 1111 1111 for Visa, 5555 5555 5555 4444 for MasterCard), and there is no chance of these being confused with real data. Results: By focusing on API integration testing with third-party payment providers, Enhanced Reliability significant improvement: Our thorough testing strategy ensured that payment integrations worked reliably across different scenarios, avoiding the risk of transaction failures. Finally, it is building customer trust and protecting your business. Conclusion: API integration testing with third-party payment providers is essential for ensuring secure payment processing in eCommerce platforms. This not only improves customer satisfaction but also protects the business from potential payment failures. As eCommerce continues to evolve, maintaining API testing practices will be key to sustaining growth and trust in the digital marketplace. Check out our Newsletter! #vitesters #ecommercetesting #softwaretesting #softwarequlityassurance #apitesting #blog #linkedin #casestudies

Background: In today's fast-moving eCommerce world, it's crucial to keep the user experience smooth and hassle-free. Online retailers frequently update their content—product listings, prices, promotional banners, and more—to stay competitive and cater to customer preferences. However, these frequent updates pose a significant challenge for the Software Testing team. Therefore, addressing the Frequent Updates is a challenge. How to handle the frequent updates during a short time was the key challenge. To overcome this, the team mainly focused on ensuring that automated test scripts remain relevant and up-to-date amidst these dynamic changes is essential for maintaining website functionality. Strategy: Before preparing the automation test suite, the team had discussions with the stakeholders to identify the dynamic content changes. Then the team focused on the below areas of the eCommerce website.` Product Listings: -> Testing dynamic product displays, sorting, filtering, and pagination. -> Verifying the correct product information, such as prices, descriptions, and images. Search Functionality: -> Ensuring search results are accurate and relevant. -> Testing search filters. Promotions and Discounts: -> Verifying application of promotional codes and discounts. -> Ensuring dynamic pricing rules are applied correctly. Inventory Management: -> Checking stock levels and availability updates. -> Ensuring out-of-stock items are handled appropriately in the UI. Challenges & Solutions: Implementing automated test scripts for dynamic content changes involves several challenges. It's all about maintenance. Based on the study, 30% of testers' time goes to test maintenance. Isn't it crazy? :D + Nightmare Identify the suitable Locators: The selection of locators was an important part. Because if particular attribute was changed, then it requires the changes of locators in the test script. In the selection of dynamic locators also, the team had to consider about complexity of the locators and the performance aspects of the application. Because writing and maintaining dynamic locators can be more complex than using simple, static locators. It requires a deeper understanding of the application's structure and more advanced knowledge of locator strategies. The other thing is dynamic locators, especially those using complex XPath expressions or involving multiple attribute checks, can be slower to execute compared to simpler, direct locators. However, unlike static locators, which may break when an element's attributes change, dynamic locators are more flexible and resilient. To address the drawbacks of applying dynamic locators such as Performance overhead and unintended elements, the team got the Dev team's support. The team got the Dev support in this matter and both agreed to keep the structure with minimal attribute changes by going with a unique attributes structure. Apart from that, the team discussed with the Dev team before implementing the changes and those discussions were held to identify the impact of the changes. It went to the extent of identifying the impact of automation test scripts. It helped with script troubleshooting when analyzing the failures, too. Next time we would like to go with AI-based dynamic locators. :) Test Data Maintenance: High test data maintenance effort was required to update test scripts in line with content changes. As an example when there were changes to the product list, then it affects the search functionality, discounts, inventory management and so on. Implementing data-driven approaches where test data is externalized from the test scripts, allowing for easy updates. Store test data in external files such as CSV, making it easier to update without altering the test scripts. Modular Test Design: Creating modular and reusable test components to reduce redundancy and enhance maintainability was the other key thing the team did as a best practice. The team adhered to the Page Object Model (POM) by creating separate classes for each page of the application. Apart from that, the team managed well-organized test suites to run relevant tests based on changes (e.g., smoke tests, regression tests). Adopting these strategies and solutions led to: Reduced Test Maintenance Effort: Automated adaptation of test scripts with minimal maintenance effort significantly reduces the manual effort, and troubleshooting required for maintenance. Enhanced Test Coverage: Ensures comprehensive testing across all dynamic content changes, leading to better test coverage. Finally, it ensures the flawless application. Faster Time-to-Market: Streamlined testing processes contribute to faster releases and updates, giving eCommerce platforms a competitive edge. Cost Efficiency: Lower maintenance costs and efficient use of resources result in overall cost savings. Conclusion: In the ever-evolving landscape of eCommerce, the ability to adapt automated test scripts to dynamic content changes is crucial. By employing dynamic locators, data-driven testing, and modular test design, the Test teams can ensure their testing processes are robust, reliable, and efficient. These strategies not only enhance the accuracy and coverage of tests but also contribute to faster and more cost-effective product releases. As eCommerce platforms continue to grow and evolve, the adoption of adaptive automated testing will be key to maintaining a high-quality user experience. By implementing these best practices, the eCommerce platform can stay ahead of the curve, ensuring that dynamic content changes do not compromise the quality and functionality of your site. Some questions for you regarding AI-based dynamic locators 1. Have you used AI-based dynamic locators? If so, what is the tool? 2. Does it target the right element? 3. Does it facilitate to highlight the attribute changes? Check out our newsletter!

Background: Retaining customers and driving sales is the most important in the rapidly evolving world of eCommerce. Therefore, ensuring a seamless user experience in an eCommerce platform is challenging. eCommerce platforms become more complex with various new features. Sometimes, eCommerce platforms are integrated with ERP systems and it handles the Sales module of the ERP application. With that integration, some inputs are coming from the ERP system apart from the Customers. Then an admin role comes to the middle to handle some workflows and approvals. Therefore, apart from managing the eCommerce features, multiple users are involved with segregated access levels. Due to such many reasons, both functional and non-functional testing becomes an essential part. Functional testing verifies that each feature of an eCommerce platform works according to the specified requirements. It covers various aspects, including product search functionality, shopping cart operations, payment gateways, order processing, chatbot, product comparisons, item management, etc. Integration testing handles the integration of all the modules. Without that testing, having functionality verified in each module is pointless. There could be situations where some functionalities are not working on a particular device even though everything looks fine on all the browsers. Compatibility testing comes to address this part while UI/UX testing ensures user interfaces and user experiences. Apart from that, Security testing and Performance testing play key roles. Given the competitive nature of the online marketplace, even minor glitches can lead to significant revenue loss and damage to the brand's reputation. Strategy: To implement effective functional testing strategies, the team followed these steps. These are mentioned as a high-level overview. We will discuss these points separately in the next editions. Define Clear Requirements: Begin with well-documented requirements that outline what each feature of the eCommerce platform should do. This clarity helps testers understand what needs to be validated. Create Comprehensive Test Cases: Develop detailed test cases covering all functionalities of the platform. These should include scenarios for browsing/searching products with various search criteria, adding/editing/deleting items in the cart, checking out, making payments, and tracking orders. Automate Testing: Utilize automation tools to streamline repetitive tasks and improve test coverage. Automation is particularly beneficial for regression testing, ensuring that new updates do not break existing functionalities. Perform Compatibility & UI/UX Testing: Ensure the platform functions correctly across different web browsers and devices. This step is crucial since users may access the site using various devices. Integrate Continuous Testing: Incorporate continuous testing within the CI/CD pipeline to detect issues early in the development cycle. Continuous testing helps maintain the quality of the platform despite frequent updates. Challenges: Many User Journeys: eCommerce platforms often have many user flows, making it challenging to cover all possible scenarios. As an example, we cannot assume that the user will do the checkout after adding all the items to the cart within one round. They may change things even after providing their delivery details. So, when come back to the checkout process after editing the cart, we have seen many bugs and caching issues. The offers/promotions based on time, location, and worth of the items are the other aspects that increase the tester's scope. Therefore, in eCommerce testing, it is so important to think as an end user by combining the tester's skills. Third-Party Integrations: Integrating with third-party services like payment gateways and order tracking systems adds layers of complexity. Even though there is a part for shipping, the team was not involved with that integration except for Tax calculations under different conditions. However, when shipping is excluded to certain countries, that part needs to be handled. Dynamic Content: Frequent updates to product listings, prices, and promotional offers require constant testing. Examples: Flash Sales: The platform offers flash sales where certain products have discounted prices for a limited time. Location-Based Pricing: Prices of products vary depending on the user's geographic location. Personalized Promotions: Logged-in users see personalized promotions based on their browsing history and past purchases. Solutions: Prioritize Testing Scenarios: Focus on the most critical user journeys and high-traffic areas of the site. Use data analytics to identify and prioritize these paths. Test Data Management: Without taking each and every test data for testing, the team handled some techniques like pairwise testing to do efficient testing with good test data combinations. Automated Scripts for dynamic content changes: Create automated test scripts that can adapt to the dynamic content changes, ensuring that tests remain relevant and up-to-date. Results: Implementing a robust testing strategy yields numerous benefits: Improved User Experience: Thorough testing ensures that users can navigate the site effortlessly, leading to higher satisfaction and increased conversions. Reduced Downtime: By identifying and fixing issues before they reach production, the platform experiences fewer disruptions, maintaining business continuity. Higher Revenue: A reliable and smooth user experience translates to higher customer retention and increased sales, directly impacting the bottom line. Enhanced Brand Reputation: Consistently delivering a high-quality eCommerce experience strengthens the brand's reputation and fosters customer loyalty. Conclusion: Functional and Non-Functional testing is indispensable for eCommerce platforms aiming to deliver a flawless user experience. By defining clear requirements, creating comprehensive test cases, automating testing processes, and addressing common challenges with strategic solutions, businesses can ensure their platforms perform optimally. The results of a well-executed testing strategy include improved user satisfaction, reduced downtime, increased revenue, and a stronger brand reputation. In the competitive eCommerce landscape, investing in testing is not just an option but a necessity for sustained success. How's your experience in eCommerce Testing? 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