🤖 Day 2: Read an introductory article on AI in testing and share it

Hi All,

I discovered this article on Browserstack which I thought was quite interesting about how AI can assist QA’s and help to streamline the testing process.

It discusses the challenges of an ever growing test regression pack and the demands on QA’s to ensure coverage - it mentions that AI is more adept at scanning through log files or lines of code, without the human impact of tiredness etc. removing the risk of human error.

Article: How AI can help Software Testing | BrowerStack | BrowserStack

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Will AI Replace Software Testers?

Do not afraid it, utilize it

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Article: https://theqalead.com/ai-ml/ai-test-automation/

I found this article to be insightful on the challenges faced when first stepping into AI in testing. My department is still very new to the automation aspect, let alone AI in it. So I can use this to avoid making the same mistakes as others when they started out.

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I had exact same though reading through articles…

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Awesome mind map @parwalrahul

The challenges mentioned were on spot:

  • Long Learning Curve : Yes, without proper learning on AI it would be so much difficult to know where to integrate AI in our day-to-day testing activities.
  • Difficulty of Debugging : Lack of transparency in AI models makes it difficult to know it’s decisions. Hopefully waiting for the rise of Explainable AI models in the future.

Absolutely, with the reduction of repetitive works it also adds intelligence to the testing process. It’s utmost essential to maintain a balance through acknowledging the potential risks involved when integrating AI into QA process.

Brilliant :star2:

Agreed, let’s approach AI with cautious optimism, recognizing its current limitations while remaining open to its future advancements.

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Link : AI Testing: The Future of Software Testing
Key Takeaways:

  1. AI is not going to replace Testers, it will only help us with better-quality work by minimizing our efforts and time estimations.
  2. This blog introduces to many such amazing tools, which I will explore and use in my current projects.
  3. Why and how using AI can be a good thing for testers.

I found this article on itconvergence.com about how ai provides benefits for software testing


I chose a simple article that gives a few examples of what GenAI can and cannot do. TL;DR is that we require testers with domain expertise and access to infrastructure to make decisions where GenAI like ChatGPT lacks.

In the article, there is a sample selenium test script that cannot be run by ChatGPT since the platform is not clear. It would require human intervention to make key decisions on where to run the tests. Perhaps this is something that can be solved in the future but for now, it is important to focus our energies on where GenAI can be leveraged - tedious tasks, or assisting with test scenarios.

Article: Software Testing: What Generative AI Can—and Can't—Do | TechBeacon


Thanks for sharing, such valuable article

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Benefits of Artificial Intelligence in Software Testing

Artificial Intelligence (AI) offers numerous benefits in software testing, transforming how testing works by making the testing process more efficient and effective. Here are the top benefits of using AI in software testing.

  1. Easy test case creation. Testers can create a large number of test cases, even for complex scenarios, in less time.
  2. It allows for rapid feedback on application quality and reduces time-to-market.
  3. You can cover multiple test scenarios and edge cases that might be challenging to identify manually.
  4. It eliminates human errors, ensuring consistent and reliable test results.
  5. AI enables continuous testing by integrating with CI/CD pipelines, ensuring testing is seamlessly integrated into the development process.
  6. It significantly reduces manual effort, accelerates test cycles, and increases test efficiency.
    Article by : AI in Software Testing | Why it is Important In Test Automation

Well explained. Thanks for sharing


Could not catch up with the challenge then so doing it now.

For this I considered @pricilla09 article on ai and ml for testers in the #TestSigma Blog

Link to the article: Artificial Intelligence and Machine learning for Software Testers %

Here is what i learnt

Introduction to AI and Machine Learning (ML)

Artificial Intelligence (AI): Machines’ ability to perform tasks requiring human intelligence.
Machine Learning (ML): A subset of AI where algorithms learn from data and improve over time without explicit programming.

Origin: AI coined by John McCarthy (1955), ML by Arthur Samuel (1959).

Types of AI
Narrow AI: Performs specific tasks (e.g., Siri, facial recognition).
General AI: Matches human intelligence, can perform varied tasks (not yet achieved).
Super AI: Exceeds human capabilities (theoretical, not yet realized).

Categories of AI by Functionality
Reactive Machines AI: Reacts to inputs, doesn’t learn from the past.
Limited Memory AI: Retains past data for decision-making.
Theory of Mind AI: Simulates human understanding (still in development).
Self-Awareness AI: Understands its existence and has self-reflection (theoretical).

Types of Machine Learning
Supervised Learning: Algorithms learn from labeled training data.
Unsupervised Learning: Algorithms find patterns in unlabeled data.
Semi-Supervised Learning: Mix of labeled and unlabeled data.
Reinforcement Learning: Agents learn through interaction with the environment and rewards.

AI and ML in Software Testing
Automation: AI and ML can automate repetitive testing tasks.
Defect Detection: AI/ML can identify potential coding defects.
Test Case Generation: AI/ML can create test cases based on software requirements.
Real-Time Feedback: AI/ML can provide instant feedback to testers.
Anomaly Detection: AI/ML identifies unusual patterns or behaviors in software.

Benefits of AI and ML in Software Testing
Improved Accuracy: AI/ML detects patterns that humans might miss.
Increased Efficiency: AI/ML speeds up testing by automating tasks.
Reduced Costs: Automation can lower overall testing costs.
Improved Risk Management: AI/ML helps identify potential risks.
Enhanced Data Analysis: AI/ML analyzes large datasets for insights.

Challenges of AI and ML in Software Testing
Autonomicity: AI-driven testing might miss things requiring human intuition.
Cost: Initial costs for AI/ML technology can be high.
Bias: AI/ML can inherit biases from training data.
Security: Protecting sensitive information is crucial.
Complexity: Understanding AI/ML algorithms can be challenging.

AI and ML offer significant potential in software testing but come with challenges.
They should be used to augment, not replace, human testers.
Testers should leverage AI/ML while applying their domain knowledge and creativity to ensure effective testing.

I liked this article very much as it gave me a foundation to AI and ML. It’s also written in a simple manner amongst all articles on the internet.

Thank you for this task. :grinning: It fills my knowledge gap.


Aw, thanks for tagging me here @mahatheed :hugs:

So happy to hear that my blog helped you, and I see lot of resources here which I am ready to explore and learn. Thanks again :heavy_heart_exclamation:


I was on holiday in March, so I missed the start of the challenge, and when I got back I lost my motivation when I realised how far behind I was. But I think I will give it a chance, even if I do it alone :slight_smile:

I read this article about “AI and Software Testing: Pros & Cons And Future Trends” and then I used ChatGPT to summarise it:

AI is revolutionizing software testing by introducing advanced tools and frameworks that enhance efficiency and accuracy. These AI-powered tools analyze large data sets quickly, identify patterns unseen by human testers, and use learning algorithms to adapt and refine testing strategies. Notable tools like Selenium, Testim, Applitools, and Functionize showcase how AI can automate complex tasks, improve accuracy, and maintain tests over time.

AI-enhanced testing frameworks, such as TensorFlow, CodeceptJS, Robot Framework, and Appium, integrate AI and machine learning capabilities to improve the testing process. These frameworks enable predictive analytics, smart element identification, and adaptable test scripting, making testing more sophisticated and reliable.

AI-powered test automation brings transformative changes to testing by automating test creation and maintenance, improving accuracy and speed, providing predictive analytics and risk-based testing, enhancing test coverage, and offering real-time feedback and reporting. This leads to a more efficient and thorough testing process, ensuring higher quality software.

AI-based test case prioritization further optimizes testing by focusing on high-risk areas, analyzing historical data for better accuracy, aligning with agile development needs, and continuously learning and adapting. This approach enhances both the speed and quality of software development.

The integration of AI into software testing offers numerous benefits, such as efficiency, speed, accuracy, and cost-effectiveness, but also presents challenges like ethical considerations, potential biases, and the need for human oversight. Despite these challenges, the future of software testing with AI looks promising, with advancements in predictive analytics, enhanced automation, performance testing, and user experience testing.

AI should be accepted as a vital tool for quality assurance engineers due to its ability to process large datasets, automate repetitive tasks, accelerate testing, and provide proactive problem-solving. This integration allows QA engineers to focus on more complex tasks and ensures continuous improvement in software quality.

I was surprised to read that even Robot Framework and Appium are being enhanced by AI, so I want to find out how.

I also liked the part about AI-based test case prioritisation, as I am looking for something usable in agile development. However, I don’t know any tools for it, so there is more to discover :slight_smile:

It resonates with me that AI should help QA engineers to focus on more complex tasks, so I would like to integrate it into my projects, but the challenges might be the relatively low budget and the fast and never stopping execution, which makes it hard to learn and integrate something new because there is “no time to experiment”.