Daniel: Continuous learners in the QA industry can leverage AI to optimize and streamline various testing activities. Identify repetitive or time-consuming tasks such as test case generation, test data management, or automation script creation, and explore how AI tools can enhance efficiency in these areas.
Daniel: While self-healing AI can automatically correct certain changes, it may not always detect developer-induced errors in ID formats. Blindly trusting self-healing AI without code reviews can lead to potential issues. Collaborative efforts between testers and developers and code reviews remain crucial for ensuring the accuracy of test scripts.
Daniel: Absolutely, staying updated with test automation tools like Selenium is crucial. However, it’s equally important to broaden your skill set. Consider delving into emerging technologies such as AI in testing. Don’t limit yourself; explore API testing, security testing, and performance testing.
Adaptability is key in our industry, so keep an open mind, implement change, and continuously upskill. AI is making waves, and incorporating it into your toolkit can bring new efficiencies to your testing practices. Remember, it’s not about abandoning what you know but expanding your capabilities for a more holistic approach to testing in the ever-evolving tech landscape.
How well do AI-powered testing tools handle big projects or big companies?
Daniel: The importance of defining goals and tasks specific to your project. While AI can be used in projects of any size, it’s crucial to consider your testing process’s specific needs and challenges.
Hi there,
In case you missed the live session, no problem at all! You can check out the recording right here:
Additionally, we’ve got you covered with a detailed session blog:
AI-powered testing tools demonstrate remarkable efficacy in handling big projects for large companies. Their automation capabilities streamline testing processes, ensuring efficiency and comprehensive coverage. These tools effectively manage the complexity of extensive projects, providing reliable and scalable solutions for robust quality assurance.
To ensure robust AI integration testing, employ a combination of unit testing, integration testing, and end-to-end testing. Validate input-output accuracy, assess system behavior under diverse scenarios, and conduct stress testing. Additionally, leverage continuous testing to address evolving AI models. Regularly update test cases to align with changing requirements.
Hi,
When you take on the role of an AI tester, your job involves diving into AI concepts like machine learning and being the data maestro – cleaning, validating, and transforming data sets to ensure they’re top-notch for training AI models. Your main hustle? Testing those models rigorously to guarantee accuracy, reliability, and smooth performance under different scenarios. And of course, don’t forget the ethics hat – you’re on a mission against biases and pushing for responsible AI practices.
Now, let’s talk challenges for manual testers turning AI enthusiasts. It’s a bit of a tech rollercoaster! You’re stepping into a fresh tech playground with languages like Python and tackling concepts like machine learning. Embracing hands-on data work becomes a big deal, and you’ve got to shift gears from manual to automated testing, mastering scripting, and getting cozy with automation tools.
Teamwork is the name of the game too – collaborating with data scientists and developers means speaking their language and getting into their workflows.
Hi,
Misconceptions about AI in testing are quite common, and it’s essential to navigate through them to make informed decisions. One prevalent misconception is the belief that AI can replace human testers entirely. While AI is powerful, it doesn’t replace the nuanced understanding and creativity of human testers. To avoid falling for this misconception, it’s crucial to view AI as a tool that complements human expertise rather than a substitute.
Another common misconception is that implementing AI in testing is a one-size-fits-all solution. AI tools and techniques need to be chosen based on the specific needs and characteristics of the project. Not all AI applications are suitable for every testing scenario. To avoid being misled, it’s important to thoroughly understand the capabilities and limitations of the AI tools in the context of the testing requirements.
Furthermore, there’s a misconception that AI can magically create perfect test cases without human input. While AI can assist in generating test cases, human testers play a pivotal role in defining test scenarios, understanding business requirements, and interpreting complex test results. To avoid being fooled, it’s essential to acknowledge that AI is a collaborative partner that enhances, rather than replaces, human testing efforts.
Lastly, some may think that AI testing is a plug-and-play solution that requires minimal involvement from testing teams. In reality, successful implementation of AI in testing demands a deep understanding of the technology, continuous learning, and ongoing collaboration between testing and development teams. To avoid falling for this misconception, it’s crucial to invest time in training and keeping abreast of advancements in AI technology.
Hey
This session was amazing; inspired by this session, I have done some research and got a way how we can use AI for self-healing.
Using AI for self-healing tests in automation can involve techniques like using machine learning algorithms to dynamically update XPath or IDs when they change. Here’s a simplified example of how this might work:
from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC import re
class SelfHealingWebDriver(webdriver.Chrome): def find_element(self, by=By.ID, value=None): try: return super().find_element(by, value) except: print(“Element not found. Trying to self-heal…”) updated_xpath = self.self_heal_xpath(value) if updated_xpath: return super().find_element(By.XPATH, updated_xpath) else: raise NoSuchElementException(f"Element with value ‘{value}’ not found after self-healing.")
def self_heal_xpath(self, original_xpath):
# Use AI/machine learning to generate an updated XPath
# For this example, we'll just update the last part of the XPath
xpath_parts = original_xpath.split('/')
last_part = xpath_parts[-1]
new_last_part = re.sub(r'\[\d+\]', '[1]', last_part) # Update the index to 1
xpath_parts[-1] = new_last_part
updated_xpath = '/'.join(xpath_parts)
return updated_xpath
Example usage
driver = SelfHealingWebDriver() driver.get(“https://www.example.com”) driver.find_element(By.ID, “nonexistent-element”) # This will trigger the self-healing mechanism
In this example, the SelfHealingWebDriver class extends webdriver. Chrome and override the find_element method to include a self-healing mechanism. If an element is not found using the original method, it attempts to self-heal by updating the XPath (in this case, simply updating the index to 1).
In a real-world scenario, the self-healing mechanism would be more sophisticated, possibly using machine learning to analyze the DOM changes and update the XPath or IDs accordingly.
Hi there,
You can find AI testing tools in the following places:
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Online search: Use search engines to look for AI testing tools. Use keywords like “AI testing tools,” “machine learning testing tools,” or “artificial intelligence testing tools.”
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Software marketplaces: Explore popular software marketplaces like GitHub, GitLab, or Atlassian Marketplace, where developers often share and discover AI testing tools.
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Conferences and events: Attend AI or software testing conferences and events where companies often showcase their latest tools and technologies.
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Online forums and communities: Join online forums and communities related to AI or software testing. These platforms often have discussions and recommendations for AI testing tools.
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Vendor websites: Visit the websites of AI and software testing tool vendors to explore their offerings.
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Industry publications and blogs: Read industry publications and blogs focused on AI or software testing. They often feature reviews and recommendations for AI testing tools.
By exploring these sources, you can find various AI testing tools that suit your needs.
Privacy is a big deal, especially when it comes to AI in sensitive domains like fintech and banking. The last thing you want is to compromise customer data during testing. To keep privacy intact, one smart move is anonymizing data. Swap out real customer info with fictional or scrambled data so that the testing process doesn’t accidentally spill any beans.
Another trick up the sleeve is using synthetic data. It’s like creating a virtual twin of real data without the actual sensitive bits.
This way, you can put your AI through its paces without risking a privacy fiasco. Bottom line: when it comes to AI testing in finance realms, it’s all about getting the job done without snooping into people’s private business.
Hope this helps!
Hey…
The misnomer that AI can entirely replace testing and development is just that – a misnomer 
AI is a powerful tool, but it’s more of a collaborative partner in the software development and testing journey. It doesn’t replace the essentials; it enhances them.
In my experience, teams typically start integrating AI into testing when they’ve reached a certain level of maturity in their development processes. It’s not about replacing traditional testing but augmenting it. This often happens when there’s a solid foundation in place – robust testing practices, a well-structured codebase, and a clear understanding of the testing needs.
Code quality, privacy, and other critical aspects of testing remain at the forefront. AI testing comes into play when the team is well-versed in these fundamentals and sees an opportunity to leverage AI for repetitive or complex tasks. For instance, AI can be applied for test case generation, identifying patterns in code review for potential issues, or even automating mundane testing tasks.
The key is to approach AI integration strategically, understanding its capabilities and limitations. It’s not a one-size-fits-all solution, and successful implementation involves a thoughtful balance between AI and traditional testing methods. It’s a journey, and teams often start small – perhaps with specific use cases or projects – to gauge the impact and refine their approach over time. So, think of AI as a tech-savvy teammate, not a replacement for the fundamentals that make software development and testing robust.
Some popular AI-based software testing tools include LambdaTest, Functionize, Copado, Mabl, etc.
Absolutely! If you’re eyeing roles in AI, getting certified can be a smart move. It adds a formal touch to your skills, making you stand out in the competitive job market. Plus, it’s a great way to deepen your understanding of AI. However, keep in mind that real-world experience and staying updated are equally crucial. Check out certifications from big players like IBM or Google, and don’t forget to balance it with hands-on projects for that extra edge.
Hi,
Sounds exciting!
To test your AI-integrated project, start by creating diverse scenarios that cover a range of possible user inputs. Verify how the AI bot responds to various queries, ensuring accuracy and relevance. Don’t forget edge cases and unexpected inputs to assess its robustness. Test different user personas to validate the bot’s adaptability.
Leverage automated testing for repetitive tasks and regression testing. Collaborate closely with developers to understand the bot’s underlying logic. Finally, simulate real-world usage to ensure it handles the dynamic nature of user interactions seamlessly.
Hi…
Sure!
Self-healing in test automation is like having a magical touch. It’s all about tools that can automatically fix test scripts when they break due to changes in the application. Tools like LambdaTest is trending for their self-healing capabilities.
These tools use AI to detect changes in the UI and adapt the test scripts accordingly. Think of it as your testing sidekick that can handle unexpected changes without you breaking a sweat. Self-healing tools not only save time but also make your automation scripts more robust.
It depends on the setup and the level of confidence in the AI. Some teams fully automate the approval process, letting the AI handle case resolutions based on code changes.
However, many prefer a cautious approach, incorporating a manual approval step as a safety net. This ensures human oversight, especially when dealing with critical changes or in situations where the AI might not have enough historical data for accurate decisions. Striking the right balance between automation and human judgment is key for a reliable and efficient process.
Hey there,
Learning about LLM (Large Language Models) and neural networks from any free resources some of them are:
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Tutorials and Documentation: TensorFlow documentation: TensorFlow is a popular framework for building neural networks, including language models.
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PyTorch documentation: PyTorch is another widely used framework for deep learning.
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Research Papers: Papers published by OpenAI, Google Research, and other organizations on topics related to large language models and neural networks.
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Blogs and Articles: Medium and Towards Data Science often have articles on deep learning and natural language processing topics.
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YouTube Channels: Channels like “Two Minute Papers” and “DeepLearning.TV” often cover research and developments in deep learning.