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Introduction to Automated Analysis

Approx. 19 hours to complete

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Course Summary

This course teaches you how to automatically analyze data using different methods and techniques such as clustering, classification, and association rules.

Key Learning Points

Learn how to apply automated analysis methods to real-world datasets
Understand different techniques such as clustering, classification, and association rules
Gain hands-on experience with popular tools like WEKA and R

Learning Outcomes

Understand the basic concepts and techniques of automated analysis
Apply these techniques to different real-world datasets
Gain hands-on experience with popular tools like WEKA and R

Prerequisites or good to have knowledge before taking this course

Basic understanding of statistics
Basic programming skills

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures

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Data Mining and Analysis
Practical Machine Learning
Applied Data Science with Python

Related Education Paths

Notable People in This Field

Pedro Domingos
Hilary Mason

Related Books

Description

This course introduces state-of-the-art techniques for automated analysis. Automated analysis encompasses both approaches to automatically generate a very large number of tests to check whether programs meet requirements, and also means by which it is possible to *prove* that software meets requirements and that it is free from certain commonly-occurring defects, such as divide-by-zero, overflow/underflow, deadlock, race-condition freedom, buffer/array overflow, uncaught exceptions, and several other commonly-occurring bugs that can lead to program failures or security problems. The learner will become familiar with the fundamental theory and applications of such approaches, and apply a variety of automated analysis techniques on example programs.

After completing this course, a learner will be able to: - Understand the foundations of automated verification: randomization and symbolic representations - Distinguish the strengths and weaknesses of random testing, symbolic analysis, static analysis, and model checking - Use a variety of state-of-the-art static analysis and automated testing tools for automated verification - Create executable requirements as an oracle suitable for automated testing and symbolic analysis - Understand how the choice of oracle affects fault-finding for automated analysis strategies. - Use automated testing to achieve full mutation coverage - Create a test plan that utilizes both manually-written tests and automated tests towards maximizing rigor, minimizing effort and time, and minimizing test costs. This course is intended for learners interested in understanding the principles of automation and the application of tools for analysis and testing of software This knowledge would benefit several typical roles: Software Engineer, Software Engineer in Test, Test Automation Engineer, DevOps Engineer, Software Developer, Programmer, Computer Enthusiast. We expect that you have some familiarity with the Software development Life-Cycle, an understanding of the fundamentals of software testing, similar to what is covered in the Introduction to Software Testing and Black-box and White-Box Testing Courses. Familiarity with an object-oriented language such as Java or ability to pick-up Java syntax quickly to write and modify code, and willingness to use tools and IDEs are assumed.

Outline

Introduction to Automated Analysis
Introduction to Automated Analysis
Automated Analysis Techniques
Symbolic Representations
Property Specification
Environmental Specification and Assumptions
Parameterized Unit Testing using junit-quickcheck
Environmental Specifications in junit-quickcheck
(Optional) Installation of Eclipse and Gradle
Overview and Syllabus
Introduction to Automated Analysis
Automated Analysis Techniques
Symbolic Representations
Property Specification

Automated Test Generation
Overview of Automated Test Generation
Automated Test Generation using Random Testing
Automated Test Generation using Symbolic Execution
Automated Test Generation using Metaheuristic Search
Property-Based Testing for Real-Time Systems
Biasing Input Values in junit-quickcheck
Using Generators to Create Complex Inputs in junit-quickcheck
Explanation of Test Harness and Assignment for Microwave Example
Overview of Automated Test Generation
Automated Test Generation using Random Testing
Automated Test Generation Using Metaheuristic Search
Property-Based Testing for Real-Time Systems

Static Analysis
What is Static Analysis
Dataflow Analysis
Program Wellformedness Properties
Designing programs for analyzability
Static Analysis with Infer
Analysis Exercise with Infer
What is static analysis?
Designing programs for analyzability
Summative Review

Effective Automated Verification
Automating Regression Testing
Automating Security Testing Using Fuzz Testing
Runtime Monitoring
Where Automation Fails
Using Multiple Methods Effectively
The Evolution of Software Testing
Fuzz Testing with AFL
Runtime Monitoring Tools
Automating Regression Testing
Automating Security Testing Using Fuzz Testing
Runtime Monitoring

Summary of User Reviews

Discover the benefits of automated analysis with this highly-rated course on Coursera. Students praise the comprehensive curriculum and engaging instructors, while one standout aspect is the practical applications of the coursework.

Pros from User Reviews

Comprehensive curriculum
Engaging instructors
Practical applications of coursework

Cons from User Reviews

Some technical concepts may be difficult for beginners
Coursework can be time-consuming
Limited opportunities for hands-on practice

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