Vijay Kumar Sharma

Check out this video of Saikat Sengupta & Mayank Joshi's demonstration, "WebAuthn Automation using Selenium" from #SeConf 2022. #Webauthn

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Automating web components within a Shadow DOM is a common challenge for automation QAs. Playwright manages open Shadow DOM elements gracefully, and with some workarounds, it’s possible to interact with user-agent and closed components as well. In this blog, Arundev Nair discusses how the Testing Mavens team has addressed this challenge in our automation projects. We’d love to hear your feedback! Read the full article here: https://lnkd.in/gpz96F_2 #Automation #ShadowDOM #WebAutomation #TestingMavens #AutomationQA #Playwright #QACommunity #TestAutomation #TechBlog #AutomationTips #Seleniun #Testing

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MANAGING CALCULATED RISKS IN QA PRACTICES In his insightful piece, "Practicing Risk Acceptance," Mark Roache discusses the vital balance QA professionals must strike between managing and accepting risks. He emphasizes that while risk analysis helps identify potential problems, it's the courageous steps towards risk acceptance that truly drive progress, especially in urgent therapeutic areas. Mark advocates for a balanced approach where taking calculated risks can lead to significant advancements in patient care. Read Mark's full blog post here: https://lnkd.in/gdTB8pkf Share additional insights and/or questions in the comments below! #pharma #biotech #qualityassurance

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Shift left testing in regulated industries... While leveraging methodologies like shift-left testing to integrate testing into the earlier phases of the software development lifecycle enhances agility and reduces time-to-market, there can be a concern that the continuous pressure to move quickly can lead to inadequate testing coverage. This can be a particular concern in regulated industries, where thorough testing is critical for compliance and to ensure that all scenarios are covered. Our QA practice lead, Martin Sutcliffe, explains: “Early testing can identify many issues from the start, yet the focus on speed and early development stages might result in less attention being paid to problems that manifest only at later stages or under specific conditions. “For example, performance and security issues, which are critical in regulated environments, might not be fully addressed if the testing does not thoroughly simulate real-world operational conditions. However, on the other hand, shifting left enables more frequent and thorough security testing, such as static application security testing (SAST) and dynamic application security testing (DAST), early in the software development process. This approach helps uncover vulnerabilities before they can be exploited, significantly reducing the potential for security breaches and the associated fallout. “Therefore, the focus must be on getting the balance between agility and stability right. By adopting a balanced approach, depth is not sacrificed for speed, ensuring that critical issues are not overlooked in the rush to accelerate development cycles.”   The move towards DevSecOps typifies the need of QA and security to work hand in hand and create agility. Within Iridium our specialists are experienced at working with this mindset, Dom Hardman, Cyber Security practice lead, and internationally recognised CISO explains:    "The reality of today’s global cyber threat landscape means that ensuring robust security measures is not optional; it is essential. The good news is that it is entirely possible to balance the need to deliver at pace with the equivalent imperative of security controls to safeguard against vulnerabilities that could lead to significant issues. By properly integrating continuous security testing into the development lifecycle, we can mitigate cyber risks and ensure compliance with regulatory standards.” Read more from Martin Sutcliffe on embracing the delicate balance between ability  and stability in software testing here:    https://lnkd.in/ei8a2emf Please feel free to reach out to Martin with feedback or questions. #softwaretesting #agility #shiftleft #regulatedindustries #QA

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💊 The Indian API industry has a solid, consistent track record with investors in the past. With a favourable macro landscape, the industry is well-equipped to continue its growth trajectory into the future. Shiraz Bugwadia, Senior Managing Director, and Prasanna Bora, Managing Director, share their insights on this interesting space. Learn More - shorturl.at/rumpR #o3Capital #InvestmentBanking #API #lifesciences #Healthcare #Research #ndtvprofit

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Catch the latest insights from Vijay Kanal, National Product Manager at Crayon, as he sits down with Simran Shiwalkar from CIO News at the EmergeTech Chapter Pune event. Vijay discusses groundbreaking innovations, emerging trends, and the impact of new technologies on the industry. If you enjoyed this interview, be sure to like, comment, and share this video with your network. For more exclusive interviews and tech updates, subscribe to our channel and hit the notification bell so you never miss a new video! #TechInnovation #EmergeTechPune #CIONews #CrayonTech #EmergingTrends #TechUpdates

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🌟 QA Tip May: Prioritizing Test Cases 🌟 In this month's QA Tips Video, we explore the crucial task of prioritizing test cases to enhance user experience and manage project risks effectively. Prioritizing test cases can be challenging, but focusing on high-risk areas and balancing risk and impact helps streamline the process. Delivering high-quality software and advancing to new projects brings immense satisfaction. Stay tuned for more insights on QA best practices! #QATips #Wizeline #QAEngineering #SoftwareTesting #QualityAssurance #TechInsights

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"Metrics I use to measure the effectiveness of our mobile test automation efforts: Test Coverage, Defect Detection Rate, ROI, Test Maintenance Effort, Frequency of Test Runs" says Vaibhav Shukla. Read his full interview: https://lnkd.in/gKVnKWG7 Don't miss his demonstration at #AppiumConf 2024. Register now! #MobileTesting #BiometricAuthentication #QRCodeTesting #BarCodeTesting #TestAutomation #Appium

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"Metrics I use to measure the effectiveness of our mobile test automation efforts: Test Coverage, Defect Detection Rate, ROI, Test Maintenance Effort, Frequency of Test Runs " says Vaibhav Shukla. Read his full interview: https://lnkd.in/gKVnKWG7 Don't miss his demonstration at #AppiumConf 2024. Register now! #MobileTesting #BiometricAuthentication #QRCodeTesting #BarCodeTesting #TestAutomation #Appium

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🚀 Transform Your QA Process with AI! 🤖 AI in QA is growing fast, and tools like ChatGPT are leading the charge! 📈 The AI landscape in testing is fragmented, but ChatGPT has emerged as the go-to for automation and script generation. What AI tools are you using to streamline QA? Share your insights below and join the conversation! 🛠️ #ContextQA #AIInQA #SoftwareTestingTools #ChatGPT #Automation #QualityAssurance #AITesting #TechInnovation #SoftwareQuality 👉 Let's elevate software testing with the power of AI!

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AI-Assisted Testing Boom Continuous AI and machine learning will become even more integral to helping you boost your testing efficiency. But is any tester really using AI to help with testing now? Surprisingly, based on surveys like LambdaTest and The TestGuild webinar polls, most testers have already adopted AI into their workflow. A recent Lambada Test survey found AI Adoption among Software Testers at 78%. AI testing offers several advantages across various domains: Speed and Efficiency: AI testing can execute tests much faster than manual testing. Automated processes can run tests around the clock, accelerating the overall testing cycle and enabling quicker releases of products or software. Scalability: AI testing can scale to handle large volumes of data and complex systems. Whether testing software applications, websites, or hardware components, AI can manage testing tasks efficiently, even as systems grow in size and complexity. Consistency and Reliability: AI testing ensures consistent and repeatable testing processes. Unlike humans, AI algorithms do not tire or become fatigued, reducing the likelihood of errors and ensuring reliable test results. Cost-Effectiveness: AI testing tools and infrastructure, in the long run, AI testing can be more cost-effective than manual testing. Enhanced Test Coverage: AI testing can explore a broader range of test scenarios and edge cases compared to manual testing. AI algorithms can systematically generate and execute tests across different parameters, improving test coverage and identifying potential issues that might be missed with manual testing. Predictive Analysis: AI testing can leverage machine learning algorithms to analyze historical test data and predict potential future issues or areas of concern. This predictive capability allows organizations to proactively address issues before they impact end-users. Adaptability and Learning: AI testing tools can adapt and learn from new data and experiences. Machine learning algorithms can continuously improve testing processes based on feedback, leading to more effective testing strategies over time. Risk Reduction: By automating repetitive and mundane testing tasks, AI testing frees up human testers to focus on more complex and creative aspects of testing, such as exploratory testing and scenario analysis. This reduces the risk of human error and improves overall testing quality. Cross-Platform Compatibility: AI testing tools can be designed to work across different platforms, operating systems, and devices. This flexibility allows organizations to conduct comprehensive testing across diverse environments, ensuring consistent performance across all platforms. Early Detection of Issues: AI testing can detect defects and issues early in the development lifecycle, when they are typically less costly and easier to fix. By identifying problems sooner, AI testing helps minimize delays and reduce the overall time-to-market for products and services.

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AI-Assisted Testing Boom Continuous AI and machine learning will become even more integral to helping you boost your testing efficiency. But is any tester really using AI to help with testing now? Surprisingly, based on surveys like LambdaTest and The TestGuild webinar polls, most testers have already adopted AI into their workflow. A recent Lambada Test survey found AI Adoption among Software Testers at 78%. AI testing offers several advantages across various domains: Speed and Efficiency: AI testing can execute tests much faster than manual testing. Automated processes can run tests around the clock, accelerating the overall testing cycle and enabling quicker releases of products or software. Scalability: AI testing can scale to handle large volumes of data and complex systems. Whether testing software applications, websites, or hardware components, AI can manage testing tasks efficiently, even as systems grow in size and complexity. Consistency and Reliability: AI testing ensures consistent and repeatable testing processes. Unlike humans, AI algorithms do not tire or become fatigued, reducing the likelihood of errors and ensuring reliable test results. Cost-Effectiveness: AI testing tools and infrastructure, in the long run, AI testing can be more cost-effective than manual testing. Enhanced Test Coverage: AI testing can explore a broader range of test scenarios and edge cases compared to manual testing. AI algorithms can systematically generate and execute tests across different parameters, improving test coverage and identifying potential issues that might be missed with manual testing. Predictive Analysis: AI testing can leverage machine learning algorithms to analyze historical test data and predict potential future issues or areas of concern. This predictive capability allows organizations to proactively address issues before they impact end-users. Adaptability and Learning: AI testing tools can adapt and learn from new data and experiences. Machine learning algorithms can continuously improve testing processes based on feedback, leading to more effective testing strategies over time. Risk Reduction: By automating repetitive and mundane testing tasks, AI testing frees up human testers to focus on more complex and creative aspects of testing, such as exploratory testing and scenario analysis. This reduces the risk of human error and improves overall testing quality. Cross-Platform Compatibility: AI testing tools can be designed to work across different platforms, operating systems, and devices. This flexibility allows organizations to conduct comprehensive testing across diverse environments, ensuring consistent performance across all platforms. Early Detection of Issues: AI testing can detect defects and issues early in the development lifecycle, when they are typically less costly and easier to fix. By identifying problems sooner, AI testing helps minimize delays and reduce the overall time-to-market for products and services.

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Quick Checklist for Writing Effective BDD Scenarios 💯 🔵 Start scenarios early for shared understanding. 🔵 Ensure features and scenarios are descriptive. 🔵 Use Background for common context. 🔵 Reuse step definitions for consistency. 🔵 Use Data Tables and Scenario Outlines for complex data. 🔵 Write declaratively to highlight behavior. Keep this checklist handy for effective BDD sessions! 💪 #BDD #ConformIQ #ConformIQVisualizer

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#SeleniumConf 2024 is coming online in June. Check out this video "Scaling and managing Selenium Grid" by Dima Kovalenko from #Selenium Conf India 2014. #TestAutomation #SeleniumTesting #SeleniumGrid

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Check out this video of Saikat S. & Mayank Joshi's demonstration, "WebAuthn Automation using Selenium" from #SeConf 2022. #Webauthn

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