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University of Minnesota

Introduction to Automated Analysis

University of Minnesota via Coursera


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.


  • Introduction to Automated Analysis
    • In this module we will learn about a range of techniques for analysis of programs and methods to automate testing. Along the way we will learn how to specify properties of interest to check about a program and capture assumptions about the environment for effective testing. To reinforce some of the important concepts learned we will practice automated testing using effective tools on a concrete example.
  • Automated Test Generation
    • The focus of this module is to understand how various techniques can help us automate the generation of useful and numerous tests. We will examine ways to specify and use properties of the system and the environment to guide the generation of test data.
  • Static Analysis
    • The goal of this module is to introduce the learner to the principles of statically analyzing programs, understand how analysis techniques work by looking at some example analyses, and some good practices to follow when designing programs to enable the tools to help us detect and avoid defects. The learner will gain an understanding of using static analysis tools by looking at one concrete tool.
  • Effective Automated Verification
    • This module will examine how to use effective automation techniques for a variety of purposes such as performing effective regression testing, discovering security vulnerabilities and monitoring software at run-time for critical properties.

Taught by

Mike and Kevin Wendt

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