Artificial Intelligence (AI) in Software Testing

Explore what artificial intelligence means, from definitions of machine intelligence to mimicking human thinking, learning, seeing, and problem solving, and its role in software testing.

Explore weak AI, which performs basic tasks like reading text messages and converting speech to text, and strong AI, which analyzes its environment and aims for human-level intellect.

Examine why artificial intelligence matters in software testing by defining expert systems and enabling machines to think, analyze, and decide. Examples include autonomous driving, accessibility aids, and signboard translation.

Discover how artificial intelligence trains self-driving cars from driving experience and powers wearable tools to read and translate signboards, recognize people and products, and boost safety and accessibility.

Explore real-world ai applications like number plate recognition, self-driving cars, and diabetic retinopathy screening, and understand how machine learning and deep learning relate to artificial intelligence.

Train models from large datasets to detect patterns and predict outcomes without explicit programming, highlighting that machine learning is a subset of artificial intelligence.

Explore why machine learning handles tasks too complex to code, by learning from data to build models that recognize patterns, anomalies, three dimensional objects, and credit card transactions.

Explore how machine learning powers real-world tasks, from credit card fraud detection to cat versus dog image classification, by learning patterns from thousands of labeled photos to build predictive models.

Explore deep learning, a subset of machine learning and artificial intelligence, using artificial neural networks with multiple hidden layers to learn hierarchical representations and make predictions.

This lecture explains why deep learning is needed, showing how it handles high-dimensional data and complex problems that traditional methods struggle with, enabled by abundant data and computational power.

Compare deep learning and machine learning by showing automatic identification of features for classification and how deep learning selects which features matter more.

Explore data science as the discipline that makes sense of data, intersecting with AI, machine learning, and deep learning to transform data into information and information into insight.

Explore why data science matters as vast data from daily activities enables predictive analysis and pattern discovery, guiding better decision making, route planning, and understanding traffic time.

Explore predictive analysis to prevent flight delays and improve route planning in the airline industry, and examine how Netflix uses collaborative filtering to recommend movies.

Explore how artificial intelligence enhances software testing, enabling dynamic locators, real time object analysis for stable tests, and smarter, automated testing across web pages and login flows.

Explore how AI shapes testing ecosystems by standardizing practices and generating automated, code-free test scripts from user acceptance criteria to boost coverage.

Artificial intelligence in software testing uses environment-aware learning to automate tasks, generate and optimize test cases, and provide instant feedback, reducing costs and improving UI testing and security.

Explore the pros and cons of ai in software testing, including generating and optimizing thousands of test cases, running them, and reporting functional performance and security results.

Apply AI to software testing by deploying smart algorithms that simulate user behavior, identify duplicates and bugs, leverage log analytics, and prioritize regression based on historical data.

Act as a smart assistant for testing, not a replacement for testers or QA teams. Automate repetitive tasks while testers focus on strategic decisions and reviewing results.

Discover how artificial intelligence elevates test automation by learning from data to automate tasks, improve validation, and even create rules that automate responses.

Train AI bots to understand environments, learn from input data, and recognize patterns and behaviors to improve intelligent actions in software testing.

Explore the challenges of AI-powered software testing, from algorithm development and data collection to how models learn from input data and adapt to app changes.

Explore real-world uses of artificial intelligence for emotion detection, from facial emotion analysis to improve car safety, personalization, candidate assessments, and real-time gaming feedback.

Demonstrates facial emotion detection using AI, analyzing uploaded photos to identify emotions such as happy, angry, surprised, and sad with a fully convolutional neural network and confidence percentages.

Explore a text analysis API powered by artificial intelligence to perform sentiment analysis, classifying text into positive or negative emotions across blogs, forums, reviews, surveys, and social media.

Explore how the I-Spy mobile app uses artificial intelligence to identify objects and people from images, showcasing AI-powered image recognition that goes beyond basic image recognition.

Learn ai-powered automation testing tools that auto adapt to element changes, enable no-code test creation, and accelerate regression and visual testing.

Explore how tools discussed earlier enable AI automation for software testing across apps, and learn to use them to speed up operations and maintenance, with season 2 release updates.

Explore how AI test automation using Testim speeds up execution and maintenance of web tests with a machine learning script that identifies elements by multiple attributes and adapts to changes.

Explore how artificial intelligence and machine learning shape software testing, covering supervised and unsupervised learning, deep learning, Python tools, and practical interview topics for AI in testing.

Explore how robotic process automation mimics human actions to automate business processes, reducing costs while boosting quality, consistency, and scalability of tasks previously performed by humans.

Compare robotic process automation, a software robot mimicking human actions, with artificial intelligence, the simulation of human thinking by machines, highlighting RPA's rule-based limits and AI's judgment capability.

Robotic process automation boosts cost efficiency through predictable, consistent processes with fewer errors. Software robots perform tasks faster, enable 24/7 operations, and provide audit trails for improvement and compliance.

Illustrates how robotic process automation handles data: pull from email, transpose Excel rows into a master Excel document, enter records in a web system, and email completion notices.