That is because the search sites you are searching want you to click on these links so that they can make money.
Here’s a summary of the 3 key takeaways from the article “How AI Is Transforming the Future of Software Testing”:
- Increased Efficiency: AI can automate repetitive tasks like generating test cases, freeing up testers’ time for more complex and creative work like designing new testing strategies.
- Improved Accuracy: AI can analyze large datasets and identify patterns that might be missed by humans, leading to more comprehensive and effective testing. This can help catch bugs earlier in the development process, saving time and money.
- Smarter Test Creation: AI can learn from past testing data and user behavior to generate more relevant and targeted test cases. This helps ensure that the software is tested for real-world scenarios and user needs.
Overall, AI is transforming the future of software testing by making it faster, more efficient, and more effective. However, it’s important to remember that AI is not a replacement for human testers, but rather a tool that can augment their skills and expertise.
I use Xmind for this.
Here is my quick overview on it if you need help: Inspiration Matters | Software Engineering Management with Xmind | Webinar with Rahul Parwal (youtube.com)
I’m sure I replied to this, and I wanted to update my response, but I can’t find it here, and the browser search has been pre-empted by the site search! So - I want to share a talk I just now watched, How Do We Test Our AI Chat And Voice BOT ?
I’m not alone in picking “AI In Software Testing by Andreea Draniceanu”
For me I found the main takeaway to be that it is a tool to better enable us to perform our jobs, rather than be something to replace us. With all areas of testing, from automation to accessibility to security, we want to arm ourselves with the best tools and AI can be a powerful tool.
The article makes a number of suggestions around the use of AI in generating our test scenarios and test data. I definitely see the value in this but also the need to be extremely cautious on what we are sharing.
My work is very restrictive and apprehensive in adopting AI based tools and focuses on a select few tools that it can acquire special licenses for. However we do have our own Chat GPT so I am interested in interacting with that a little more and seeing what scenarios it can come up with for some of the stories that we’ve got next in the backlog. It would be interesting to know if it can create scenarios that we missed out.
I am also interested in exploring using it to create automated test cases in the frameworks for our newest project.
Great thanks Rahul for sharing the tool, highlight appreciated.
I’m working on Master dissertation, a case study research on using AI and crowdsourcing in testing/QA practices. Can I consider you in one of the interview(s) needed for the purpose of the research? if yes, let me know how I can contact you.
Absolutely. You can DM me here or on LinkedIn.
Here is the link: Rahul Parwal | LinkedIn
Here is my contribution, sorry for being late , I didn’t know that we will be doing this during the weekend :
- What are the essential concepts, tools, or methodologies discussed?
Concepts
AI: Artificial Intelligence.
Tools:
a. Chat GPT
b. Google Gemini.
c. UI Path.
d. Preflight.
e. Parasoft.
Methodologies:
a. Data
b. Root cause analysis.
c. Multimodal AI.
d. Test coverage.
e. Process images, text and requests to Automated testing.
f. API tests.
- Consider how the insights from the article apply to your testing context. Do you see potential uses for AI in your projects? What are the challenges or opportunities?
I believe it will as for now I don’t know where to start working with AI. That is the biggest challenge for me.
I was already familiar with some low-code and no-code automation tools that had features such as self-healing and the capability for visual testing. Recently, I’ve seen the emergence of AI in creating test scripts or test cases based on interactions with the app.
To me, it seems using AI in testing can help leverage a shift-right situation, wherein organizations can have non-QA team members onboard easily with the testing process and help out with testing.
For people who are already established in QA, AI can be used to not only automatically write test cases but also to optimize existing ones, as well as analyze problematic areas that may need more focus on testing.
This blog post discusses the increasing integration of AI in software testing, predicting a future where AI and humans coexist in this field. Key takeaways include the necessity of automated testing due to the rise in code production facilitated by AI tools, the evolving role of testers who must now assess AI-generated outputs for coherence and utility, and the emergence of private, offline Large Language Models (LLMs) for companies concerned about data security. It emphasizes the importance of human expertise in ensuring software quality, despite the efficiency AI brings to the process.
“In 2024, testers will respond by embracing AI-powered testing tools to keep up with developers using AI-powered tools and not become the bottleneck in the software development life cycle (SDLC).”
We are doing exactly this at our workplace . The Salesforce team produces functionalities so fast, and do to the overall complexities involved with testing Salesforce Lightning, we are navigating towards using AI-powered tools that offer no-code solutions for speedy creation of tests that can self-heal (amazing if true).
AI In Software Testing — How can AI be used in software testing? | by Testsigma Inc. | Medium
AI can be used in software testing in various ways and can save time, improve testing accuracy, reduce costs and improve quality and user experience.
How AI can be used in software testing:
-
Automated Test Generation: AI can generate test cases automatically based on specifications, code analysis, and historical data. This speeds up test creation and ensures comprehensive coverage, reducing the manual effort required.
-
Anomaly Detection: AI algorithms can analyze test results and identify unusual patterns or unexpected behaviors that might indicate defects or vulnerabilities in the software.
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Regression Testing Optimization: AI can prioritize test cases for regression testing, focusing on the most critical areas affected by recent code changes. This optimizes testing efforts and reduces the time required for testing cycles.
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Predictive Analytics: AI can predict which parts of the software are more likely to have defects based on historical data. This helps testers allocate resources more effectively and concentrate testing efforts where they are most needed.
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Natural Language Processing (NLP): NLP techniques enable AI to understand and process natural language, facilitating the creation of test cases and the analysis of requirements and documentation.
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Defect Prediction: AI can predict potential defects by analyzing code changes, commit history, and other relevant data. This allows testers to address issues proactively.
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Test Execution and Monitoring: AI-powered bots can execute tests on various platforms and devices, mimicking user interactions. They can also monitor system performance and responsiveness during testing.
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Test Data Generation: AI can generate diverse and realistic test data that covers different scenarios, ensuring thorough testing of various conditions.
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Log Analysis: AI can analyze log files to identify errors, exceptions, and patterns that may indicate issues, helping testers pinpoint defects quickly.
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Usability Testing: AI can simulate user interactions and provide feedback on the user experience, identifying usability
Looking forward to getting to know more about the how next
Not an article, but I bring a podcast: AB Testing • A podcast on Spotify for Podcasters
AB Testing podcast is going deep into AI and What it is, how does it work and how to understand it. So if you have a daily conmute to do, or you feel like going for a walk while learning stuff, please give Alan and Brent a chance.
I listened to a podcast instead, which was well worth my time:
Main Takeaways:
- Machine Learning is a subset of AI, which is again a subset of Data Science
- There are a lot of different subsets of AI (I might create an illustration in the future so I can remember it)
- You can think of AI as automated intelligence, which automates the brain similarly to how the industrial revolution automated the body
- The AI systems we know are “weak” AIs, because they are specialized to a certain task or domain
- The holy grail of AI is “general intelligence”. Such systems are called “AGI” (artificial/automated general intelligence)
- Machine Learning took over most other subsets of AI, because it allows to throw the same patterns at different domains. Previously to that, one could only automate very specialized domains. Machine Learning with Deep Learning and Neural Networks revolutionized that, making it a lot easier to automate a broad number of domains
I’m not gonna try to apply this to a testing context, because at this stage my main goal is to understand how ML/AI basically works (not ready for the “apply” step yet)
They were skeptical about Agile Testing, and about Devops, and about Unit Testing not being called Unit Checking…
I have started using ChatGPT as an “advisor” for possible test cases or uses cases that I could have missed during test execution.
This blog post highlights how critical we need to be when using LLM’s during test preparation.
It also emphasizes how “wauw” Chatgpt is experienced in our companies but how much time we as testers need to put down to validate its accuracy and correctness even to the border at which it may not be worth it? or?
Sorry guys playing catchup after a few days illness
Course: Introduction to Artificial Intelligence in Software Testing | Udemy
hi guys, i had a look at this short 30min intro video. Please note this was a freebie.
was very interesting and covered TestIM tool.
I may sound cynical but the tutor gave the impression that the tool would automatically adjust to changes in the UI, so that the tests would always run. i am not sure that would be my goal for testing, would want to know of changes and use AI to intelligently adjust, not blindly adjust
for me i would want my AI to ensure the tests completed yes, but any differences would be highlighted and allow for the adjustments to be applied.
AI Testing
“AI testing” will become the norm in the next few years, bringing incredible advancements in the way we think and do software testing.
Application of Using AI for Testing
- AI Enables Faster and Smarter Test Creation
- AI Can Quickly Generate Test Data for Data-Driven Testing
- AI Makes Test Maintenance Effortless
- AI Enhances Visual Testing
Tools for AI Testing
- Katalon Platform
- TestCraft
- Applitools
- Testim Automate
Risk, yes! With the known Hallucinations that occur, there will be ‘deep’ verification required. My journey is mainly about "how do we know the LLM / image recognition / etc is getting better and not just more confident?
Hello,
I found this article related to QA Teams and AI in software testing:
Key takeaways from article:
-
AI can mimic human behavior and allow testers to move towards a more precised continuous testing process.
-
Test planning efforts can be completed by AI automation tools
-
Improved Regression Testing
-
Enhanced defect tracing
This article gave me a lot of information regarding test automation tools and AI platforms that I did not have prior knowledge of and enhanced my beliefs that AI can make our jobs and quality of work that much better!
Advise:
1.bear in mind that this small text is an exploratory exercise on using AI to test software. My understanding here is most likely and hopefully change soon.
2. There is a time constraint: I’m still reading and exploring details from the articles I used as reference.
Few questions I raised bellow are extracted from an analysis made by Michael Bolton and James Bach of an article published by Jason Arbor.
General purpose AI tools like ChatGtp are trained on public data. Thinks about all ISTQB documentation, testing repositories, testing articles.
Before a tester can explore a version of the source code. How AI tools can avoid syntax related bugs in programming? Think about tools used by developers - GitHub Copilot. Interactions that Jason Arbor has with ChatGPT in his article, could be replaced by boundary tests at unit testing level using GitHub Copilot.(1)
What AI knows about context? Where, for what reason, what value it brings when a software is being develop? Public chatbots tools are not trained on a context that is restricted data that is not public - most of the software I test.Imagine you are trying to classify people holding a cellphone in a picture, and the Model flags a person that scratches its ear.
There is a problem around LLM responsibility. Models can generate text based on what is inserted on prompt disregarding truth of the facts. This can introduce an even bigger problem subject to Brandolini’s Law.(2)
“The amount of energy needed to refute bullshit is a order of magnitude bigger than that needed to produce it.”
A recent example can be found on this article where AI generated bogus cases to argue in favor of a case. A lawyer used it in court.(3)
Given that AI model could have been trained in fictitious cases, I started to realize how much information on software testing I’ve read in the past, that could misguided the model - “bullshit” either too general and not applicable to the context I was testing.