Which is more demanding Selenium with java or Python
How to utilize Test Estimation Technique in generative AI project
Post-deployment testing is an essential step in the software development lifecycle, and it serves a different purpose than monitoring systems. While monitoring systems are designed to detect issues and anomalies in real-time, post-deployment testing
Would you like Selenium webdriver individually contribute on both web and Desktop automation??
We should come up with AI Testing and the future of testing tools and tech. What are the ways we can develop ourselves and get ready with the tools
How do you balance the number of commits to risk to quality
How continuous integration , and AI go together and what are the changes we can anticipate with AI .As an individual what are the AI tools we can get trained on
What is your suggestion for following area. Where functional/performance testing code should live for API and UI development ? Respective to in development repo or in separate repo?
Why do we need to Testing Post Deploy ? will the monitoring systems not cover for this ?
For API development, functional/performance testing code should live in a separate repository, close to the API codebase. For UI development, functional/performance testing code should live in the same repository as the UI codebase, but in a seprate
What is difference between Selenium and Jenkins
What is your say on Tool-based productivity vs test effort estimations optimizations as a part of the test automation tool evaluations?
Can generative AI tool really help in test automation
How do you ensure that simplifying a tool doesn’t take away important features?
How do you translate you manual testing analytical skills in automation scripts
Graph based build, who maps the what changes will impact what tests to be invoked ? does this not make it more complex and increase risk?
Thank you for raising such an insightful question during the session "The Complexity of Simplicity. From my point of view I guess this will be the right fit answer
To ensure that simplifying a tool doesn’t compromise key features, it’s crucial to focus on user-centered design. By understanding the core needs and workflows of users, we can prioritize essential functionalities and streamline the user experience without losing value. Additionally, involving users in the feedback loop throughout the development process helps maintain a balance between simplicity and capability. It’s about decluttering the interface, not removing the depth or power of the tool, and ensuring that advanced features are still accessible when needed.
I hope this addresses your question! Please feel free to reach out if you’d like to discuss further.
Here’s my perspective:
Tool-based productivity and test effort estimation optimization are two essential components in the evaluation of test automation tools. They work best when seen as complementary rather than competing priorities.
- Tool-based productivity is about how efficiently a tool can help testers automate, run, and analyze test cases. A productive tool enables faster testing cycles and reduces the time spent on managing the tool itself.
- Test effort estimation optimization, meanwhile, ensures that teams can accurately predict the resources, time, and complexity involved in setting up and maintaining test automation. This leads to better planning and reduces unexpected challenges.
Ultimately, a good test automation tool should offer a balance of both: it should enhance productivity while also giving teams the clarity needed to estimate and manage test effort effectively. The key is to ensure that the tool simplifies the testing process without overlooking the importance of long-term effort estimation and scalability.
Thank you LambdaTest for the Insightful session, Here is the Answer of your Question
Yes, the skill gap in QA can be a challenge when trying to align teams, but it’s manageable. Different expertise levels often lead to communication gaps and inconsistent practices. However, organizations can address this by investing in upskilling and standardized training programs, ensuring all team members are on the same page with tools and processes.
Clear communication is key—simplifying complex concepts and providing straightforward documentation can help bridge the gap. Additionally, leveraging automation tools that require minimal expertise empowers everyone to contribute, reducing reliance on specialized skills.
Fostering a culture of collaboration and mentorship is also essential. Encouraging senior QA members to guide juniors and promoting cross-functional teamwork helps everyone grow and align.
Ultimately, while the skill gap exists, it can be minimized through continuous learning, communication, and collaboration, ensuring the team works cohesively toward shared goals.
Thank you for the insightful question!
Yes, From my point of view AI prompt engineering does simplify automation jobs, but with some important caveats. It significantly reduces the need for manual coding in routine tasks such as test case generation, bug reporting, and script creation. By translating natural language prompts into automated actions, it opens up automation to a wider audience, even those without in-depth technical knowledge.
However, while AI can handle repetitive or straightforward tasks efficiently, it doesn’t eliminate the need for human expertise in more complex scenarios. Automation requires strategic decision-making, contextual understanding, and problem-solving, which AI cannot fully replicate. Engineers still need to review, refine, and oversee AI outputs to ensure they meet quality standards.
In short, AI prompt engineering is a valuable tool that simplifies parts of the automation process, but human input is essential for managing more intricate tasks and ensuring optimal results.