Post-deployment testing and monitoring systems serve complementary but distinct purposes. Monitoring systems are designed to track system performance and alert teams to issues in real-time, focusing on detecting anomalies, downtime, and overall system health. However, they might not catch every issue, especially those related to specific functionality or user experience.
Post-deployment testing, on the other hand, verifies that new changes or deployments work as expected in the production environment. It helps to ensure that the deployment hasn’t introduced new bugs or degraded existing features. This type of testing can uncover issues related to integrations, data migrations, and user interactions that monitoring alone might miss.
In summary, while monitoring systems provide ongoing oversight and alerting, post-deployment testing ensures that the deployed changes meet quality standards and function correctly in the live environment. Combining both practices enhances overall system reliability and user satisfaction.
Thank you for your question about repository management for functional and performance testing.
For API development, it is advisable to keep functional and performance testing code in a separate repository close to the API codebase. This setup allows for better modularity, ease of maintenance, and independent management of API-specific tests. It also supports versioning and deployment processes without affecting the UI codebase.
For UI development, storing functional and performance testing code in the same repository as the UI codebase, but in a separate directory, is generally preferred. This ensures tight integration between the tests and UI code, facilitating easier maintenance and updates. Having both the UI and testing code in one repository simplifies the CI/CD workflow and helps in synchronizing changes effectively.
In essence, separating repositories for API tests and co-locating UI tests with their respective codebase enhances both modularity and integration.
Thank you for your question regarding the difference between Selenium and Jenkins.
Selenium is a widely-used open-source tool for automating web application testing. It enables users to write test scripts in various programming languages (such as Java, Python, and C#) to simulate user interactions and verify functionality across different web browsers.
Jenkins, on the other hand, is an open-source automation server designed for continuous integration and continuous delivery (CI/CD). It automates various stages of the software development lifecycle, including building, testing, and deploying applications. Jenkins can integrate with tools like Selenium to facilitate automated testing within a CI/CD pipeline.
In summary, Selenium is focused on automating web application tests, while Jenkins orchestrates and manages the broader automation processes within CI/CD workflows.
I hope this clarifies the distinction between the two tools.
Thank you for your question on generative AI tools in test automation.
Generative AI can significantly enhance test automation in several ways:
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Automated Script Generation: AI tools can generate test scripts based on application requirements and user behavior, reducing manual effort.
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Test Data Creation: AI can create diverse and realistic test data, which is crucial for comprehensive testing.
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Adaptive Test Cases: These tools can dynamically update test cases as application features change, ensuring relevance.
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Error Prediction: AI can analyze patterns to predict and identify potential issues early.
While not a complete solution, generative AI offers valuable improvements in efficiency, coverage, and adaptability in test automation.
Thank you for the great session @LambdaTest
Here is the Answer
Manual testing skills can significantly enhance automation scripts. Start by leveraging your deep understanding of test requirements and scenarios from manual testing to design comprehensive automation scripts. Utilize your knowledge of critical test areas and edge cases to ensure robust test coverage. Focus on diverse test data and effective error handling, which manual testing experience helps you identify. Optimize scripts for performance based on insights from manual testing, ensuring efficient execution and quick feedback. By integrating these manual testing insights, your automation scripts will be more effective and reliable.
Here is the Answer that I think:-
Graph-based builds use dependency graphs to map code changes to relevant tests, aiming to optimize test execution. While this approach enhances efficiency by running only impacted tests, it can introduce complexity in managing and updating these graphs. The key challenge is ensuring the graph accurately reflects dependencies to avoid missing critical tests or running unnecessary ones. With proper management and continuous validation, the benefits of targeted testing can outweigh the complexities, leading to more effective and efficient test processes.
As a Tester and In my own experience,
The skill gap in the QA industry is indeed a significant challenge when trying to align teams on the same path, especially as the complexity of modern software systems continues to grow. In the Testμ 2024 session led by Simon Stewart, the core message was that simplifying complex software systems is essential, but it also requires a skilled and adaptive team to implement and maintain these simplifications effectively.
Key challenges of the skill gap in QA:
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Inconsistent Knowledge Levels: QA professionals often have varying levels of expertise in automation, coding, and modern testing tools. This disparity can slow down the adoption of streamlined test frameworks and build tools, which are crucial for simplifying production and test code.
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Evolving Technologies: With CI pipelines and modern development practices continually evolving, there’s a constant need for upskilling. Without a consistent baseline of skills across the team, embracing these tools and methodologies becomes difficult, resulting in longer feedback loops and inefficient processes.
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Cross-functional Communication: Simplicity in systems is not just about reducing lines of code or optimizing processes; it also relies on smooth collaboration between developers, testers, and DevOps teams. A skill gap in areas like coding, test automation, or CI/CD workflows can create communication bottlenecks.
Mitigating the Skill Gap: Simon emphasized that addressing this issue requires a combination of continuous learning and mentorship programs. Companies need to invest in training their teams on emerging technologies, such as AI-driven test automation and modern build tools. Additionally, creating environments where junior and senior QA professionals can collaborate will help align skills and foster a more unified approach to testing.
To ensure that simplifying a tool doesn’t compromise important features, a balanced approach is essential. Simplification should focus on reducing unnecessary complexity without stripping away core functionality that users rely on. Here are some strategies:
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Understand User Needs: Start by identifying the key features that users depend on. Engage with users to understand their pain points and what they find essential in the tool. Simplification efforts should avoid touching these vital features.
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Prioritize Features: Use feature prioritization frameworks like MoSCoW (Must-Have, Should-Have, Could-Have, Won’t-Have). This helps ensure that the “Must-Have” features are preserved while simplifying less critical areas.
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Modular Design: Design tools in a modular way, where core functionality remains intact, but additional features can be added or removed as needed. This allows the tool to remain simple for most users while offering customization for power users.
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Progressive Disclosure: Hide advanced features behind more accessible interfaces but make them available when needed. This approach keeps the tool simple for new or occasional users but still powerful for advanced users.
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Test Early and Continuously: Regularly test the tool with real users during the simplification process. Gather feedback to ensure that streamlining doesn’t unintentionally hinder the tool’s usefulness.
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Optimize, Don’t Strip: Look for opportunities to optimize code and user interfaces
without removing functionality. For example, instead of eliminating a feature, make it more intuitive or automate it in the background.
By focusing on user experience, prioritizing features, and taking a careful approach to simplification, it’s possible to streamline a tool while preserving its critical functions.
In the discussion on “Tool-based productivity vs test effort estimations optimizations,” I believe both aspects are vital in evaluating test automation tools, but they serve different purposes.
Tool-based productivity enhances testing efficiency by automating tasks, shortening cycles, and streamlining workflows. In contrast, test effort estimation optimization focuses on accurately forecasting the time and resources required for testing.
In evaluating test automation tools, balancing both is crucial. A productive tool must also provide accurate effort estimates for better planning and risk mitigation. The ideal tool boosts productivity while ensuring precise estimations, helping teams deliver high-quality software efficiently and predictably.
Looking for an entry level or internship position for software testing. I am a front end developer who just transition into QA tester.
I am reaching out to express my interest in pursuing an entry-level or internship position in software testing.
Having recently transitioned from a front-end development role to a QA tester, I am excited about the opportunity to leverage my technical skills and gain hands-on experience in quality assurance. My background in front-end development has provided me with a solid understanding of web technologies and user experience, which I believe will be invaluable in identifying and addressing potential issues in software applications.
I am eager to learn and grow in the field of software testing, and I am particularly drawn to [Company’s Name] because of [specific reason related to the company or its values, such as innovative projects, a strong commitment to quality, or a supportive learning environment]. I am enthusiastic about the possibility of contributing to your team and helping ensure that products meet the highest quality standards.
Thank you for considering my application. I look forward to the opportunity to discuss how my skills and passion for quality assurance can contribute to your team.