Artificial Intelligence & QA
- 1. QA & Created and Presented By Maliha Ashraf https://www.linkedin.com/in/maliha-ashraf
- 2. Agenda Artificial Intelligence and Machine Learning Difference Artificial Intelligence and its Types QA Role in AI Types of Machine Learning Algorithms of Machine Learning Linear Regression Example
- 3. What is Artificial Intelligence? the term loosely applies to a range of technologies that mirror human cognitive functions devices designed to act intelligently the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.
- 4. Then what is Machine Learning? Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. It is the most common technique that powers AI Often referred to as a subset of AI, it’s really more accurate to think of it as the current state-of-the-art.
- 5. Artificial Intelligence and Machine Learning
- 6. Types of Artificial Intelligence 1. Narrow AI: This is AI at a specific field For e.g. a text-based answering bot More common 2. Generalized AI: - Systems or devices which can, in theory, handle any task - Less common - Led to the development of Machine Learning
- 7. 1- Testing AI Software 2- AI in Software Testing • Use AI to improve testing processes. • Need to know AI techniques and their implementation . • Test the projects which use AI • Should have an idea of testing these projects QA role in AI
- 8. 1- Testing AI Projects 1- Gather testing data 2- Determine Acceptance Criteria 3- Provide better feedback of QA efforts
- 9. 1- Testing AI Projects 1- Gather Testing Data: System tested on more data, better chances of determining the performance accurately
- 10. 2- Acceptance Criteria Determine what would be the acceptance criteria and evaluate the application according to it. For example: Often the intention of building an AI is for it to be human-level or human-like in performance so the testing is normally based on: Does it have human level performance? Does it seem like it is a human? - Normally done using a Turing test. 1- Testing AI Projects
- 11. 3(a)- Statistical Terms the acceptance criteria aren’t expressed in terms of defect number, type, or severity. In fact, in most cases they are expressed in terms of the statistical likelihood of coming within a certain range. Be prepared to support those assertions in statistical terms For example, be 95 percent confident that the application will produce an answer within a given range. How can testers provide better feedback on their efforts on such applications?
- 12. 3(b)- High-Level Understanding Have a high-level understanding of the underpinnings of the application, so that any deficiencies might be able to be ascribed to a particular application component. How can testers provide better feedback on their efforts on such applications?
- 13. 2- AI in Software Testing Artificial intelligence (AI) algorithms learn from test assets to provide intelligent insights like: application stability failure patterns defect hotspots failure prediction, etc. These insights helps to anticipate, automate, and amplify decision- making capabilities, thereby building quality early in the project lifecycle.
- 14. 2- AI in Software Testing For this we need to know what are the techniques of artificial intelligence The most common is machine learning
- 15. Types of Machine Learning 1. Supervised Learning How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree etc.
- 16. Types of Machine Learning 2. Unsupervised Learning How it works: In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.
- 17. Types of Machine Learning 3. Reinforcement Learning How it works: Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Examples of Reinforcement Learning: Markov Decision Process
- 18. Common Machine Learning Algorithms These algorithms can be applied to almost any data problem: Linear Regression Logistic Regression Decision Tree SVM Naive Bayes KNN K-Means Random Forest
- 19. Linear Regression Supervised learning Algorithm It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). How it works: We establish relationship between independent and dependent variables by fitting a best line. This best fit line is known as regression line and represented by a linear equation Y= a *X + b.
- 20. Simple Linear Regression (Activity) Task - Predict a point for given data.
- 21. Q & A