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Software analysis is an essential part of software development processes and to become an expert in it, you need to learn all about its theory and practices. This type of analysis will involve diagnosing bugs, testing, debugging, and more modification of software.

Ensuring that programs are working the way it is supposed to be is the main reason why this Software Analysis & Testing is created. Taking this course will give you techniques on how to conduct dataflow analysis, constraint-based analysis, and other implementing tools.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

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Course Credit: Georgia Institute of Technology

Course Curriculum

Introduction to Software Analysis
Intro to Software Analysis and Testing 00:01:00
Why Take This Course? 00:01:00
Ariane Rocket Disaster 00:01:00
Ariane Disaster Post-Mortem 00:01:00
Security Vulnerabilities 00:01:00
What Is Program Analysis? 00:01:00
Dynamic Program Analysis 00:01:00
Static Program Analysis 00:01:00
Program Invariants 00:01:00
Discovering Invariants 1 00:02:00
Discovering Invariants 2 00:01:00
Terminology 00:02:00
Example Static Analysis Problem 00:01:00
Iterative Approximation 1 00:02:00
Iterative Approximation 00:01:00
Dynamic vs. Static Analysis 00:01:00
Undecidability of Program Properties 00:01:00
Who Needs Program Analysis? 00:01:00
Testing Compilers – Software Testing 00:02:00
Software Quality Tools 00:01:00
Integrated Development Environments 00:01:00
What Have We Learned? 00:02:00
Introduction to Software Testing
Introduction to Software Testing 00:01:00
Software Development Today 00:01:00
Key Observations 00:01:00
The Need for Specifications 00:01:00
Developer != Tester 00:01:00
Other Observations 00:01:00
Outline of This Lesson 00:01:00
Classification of Testing Approaches 00:01:00
Automated vs. Manual Testing 00:01:00
Black Box vs. White Box Testing 00:02:00
An Example: Mobile App Security 00:01:00
The Automated Testing Problem 00:01:00
Using Pre- and Post-Conditions 00:01:00
Conditions Example 00:01:00
More on Pre- and Post-Conditions 00:01:00
Using Pre- and Post-Conditions 00:01:00
Pre-Conditions 00:01:00
Post-Conditions 00:01:00
Executable Post-Condition 00:01:00
How Good Is Your Test Suite? 00:02:00
Code Coverage Metrics 00:02:00
Types of Code Coverage 00:01:00
Mutation Analysis 00:02:00
Mutation Analysis 1 00:01:00
Mutation Analysis 2 00:01:00
A Problem 00:01:00
What Have We Learned? 00:01:00
Reality 00:01:00
Random Testing
Introduction to Random Testing 00:01:00
Random Testing (Fuzzing) 00:01:00
The Infinite Monkey Theorem 00:01:00
Random Testing: Case Studies 00:01:00
A Popular Fuzzing Study 00:01:00
Fuzzing UNIX Utilities: Aftermath 00:01:00
A Silver Lining: Security Bugs 00:02:00
Fuzz Testing for Mobile Apps 00:03:00
Generating Multiple Input Events 00:01:00
Generating Gestures 00:01:00
Grammar of Monkey Events 00:01:00
Monkey Events 00:01:00
Testing Concurrent Programs 00:03:00
Cuzz: Fuzzing Thread Schedules 00:01:00
Depth of a Concurrency Bug 1 00:01:00
Depth of a Concurrency Bug 2 00:01:00
Concurrency Bug Depth 00:01:00
Cuzz Algorithm 00:02:00
Probabilistic Guarantee 00:01:00
Proof of Guarantee (Sketch) 00:03:00
Measured vs. Worst-Case Probability 00:02:00
Cuzz: Case Study 00:01:00
Cuzz: Key Takeaways 00:01:00
Random Testing: Pros and Cons 00:02:00
Coverage of Random Testing 00:01:00
What Have We Learned? 00:01:00
Automated Test Generation
Intro to Automated Test Generation 00:01:00
Outline AN 00:01:00
The Problem 00:01:00
An Insight 00:01:00
How Do We Generate Test Inputs? 00:01:00
Scheme for Representing Shapes 00:01:00
Representing Shapes 00:01:00
A Simple Algorithm 00:01:00
Enumerating Shapes 00:01:00
The General Case for Binary Trees 00:01:00
A Lot of Trees! 00:01:00
An Overestimate 00:01:00
How Many Trees? 00:01:00
Another Insight 00:01:00
The Technique 00:01:00
The Pre-Condition for Binary Trees 1 00:01:00
The Pre-Condition for Binary Trees 2 00:04:00
Example Using the Pre-Condition 00:01:00
Enumerating Tests 00:02:00
Example: Enumerating Binary Trees 00:03:00
Enumerating Binary Trees 1 00:01:00
Enumerating Binary Trees 2 00:01:00
Experimental Results 00:01:00
Strengths and Weaknesses 00:02:00
Weaknesses 00:02:00
Feedback-Directed Random Testing 00:02:00
Overview 00:01:00
Randoop 00:02:00
Randoop Algorithm 00:03:00
Classifying a Sequence 00:01:00
Illegal Sequences 00:01:00
Redundant Sequences 00:02:00
Some Errors Found by Randoop 00:02:00
Randoop Test Generation 1 00:01:00
Randoop Test Generation 2 00:01:00
Randoop Test Generation 3 00:01:00
Korat and Randoop 00:01:00
Test Generation: The Bigger Picture 00:01:00
What Have We Learned? 00:01:00
Dataflow Analysis
Introduction to Dataflow Analysis 00:01:00
What Is Dataflow Analysis? 00:01:00
The While Language 00:03:00
Control-Flow Graphs 00:01:00
Control-Flow Graphs 2 00:01:00
Soundness, Completeness & Termination 00:01:00
Abstracting Control-Flow Conditions 00:01:00
Applications of Dataflow Analysis 00:01:00
Reaching Definitions Analysis 00:05:00
Reaching Definitions Analysis 2 00:03:00
Result of Dataflow Analysis (Informally) 00:01:00
Result of Dataflow Analysis (Formally) 00:01:00
RDA Operation 1 00:01:00
RDA Operation 2 00:02:00
RDA Chaotic Iteration Algorithm 00:01:00
Reaching Definitions Analysis Example 00:02:00
Reaching Definitions Analysis 3 00:01:00
Does It Always Terminate? 00:01:00
Very Busy Expressions Analysis 00:01:00
VBEA Operation 1 00:01:00
VBEA Operation 2 00:02:00
VBEA Chaotic Iteration Algorithm 00:01:00
Very Busy Expressions Analysis Example 00:02:00
Very Busy Expressions Analysis 2 00:01:00
Available Expressions Analysis 00:01:00
Available Expressions Analysis 2 00:03:00
Live Variables Analysis 00:01:00
Live Variables Analysis 2 00:02:00
Overall Pattern of Dataflow Analysis 00:02:00
Reaching Definitions Analysis 4 00:01:00
Very Busy Expression Analysis 00:01:00
Available Expressions Analysis 3 00:01:00
Live Variables Analysis 3 00:01:00
Classifying Dataflow Analyses 00:01:00
What Have We Learned? 00:01:00
Pointer Analysis
Introduction to Pointer Analysis 00:01:00
Introducing Pointers 00:03:00
Pointer Aliasing 00:01:00
May-Alias Analysis 00:02:00
Why Is Pointer Analysis Hard? 00:02:00
Approximation to the Rescue 00:03:00
Approximation to the Rescue 2 00:01:00
Example Java Program 00:03:00
Abstracting the Heap 00:02:00
Abstracting Control Flow 00:02:00
Chaotic Iteration Algorithm 00:01:00
Kinds of Statements 00:01:00
Is This Grammar Enough? 00:02:00
Example Program in Normal Form 00:01:00
Normal Form of Programs 00:01:00
Rule for Object Allocation Sites 00:02:00
Rule for Object Copy 00:01:00
Rule for Field Writes 00:02:00
Rule for Field Reads 00:02:00
Pointer Analysis Example 2 00:01:00
Pointer Analysis Example 2 00:01:00
Classifying Pointer Analysis Algorithms 00:01:00
Flow Sensitivity AN 00:02:00
Context Sensitivity 00:02:00
Heap Abstraction 00:01:00
Scheme #1: Allocation Site-Based 00:01:00
Scheme #2: Type-Based 00:02:00
Scheme #3: Heap-Insensitive 00:01:00
Tradeoffs in Heap Abstraction Schemes 00:01:00
May-Alias Analysis 00:01:00
Modeling Aggregate Data Types: Arrays 00:01:00
Modeling Aggregate Data Types: Records 00:02:00
Pointer Analysis Classification 00:01:00
What Have We Learned? 00:02:00
Constraint-based Analysis
Intro to Constraint-based Analysis 00:01:00
Motivation 00:02:00
What Is Constraint-based Analysis? 00:01:00
Benefits of Constraint-based Analysis 00:01:00
Specification & Implementation Quiz 00:01:00
Outline of the Lesson 00:01:00
A Constraint Language: Datalog 00:01:00
Syntax of Datalog: Example 00:03:00
Semantics of Datalog: Example 00:04:00
Computation Using Datalog 00:01:00
Outline Revisited 00:01:00
Reaching Definitions Analysis 00:05:00
Reaching Definitions Analysis 2 00:03:00
Live Variables Analysis AN 00:01:00
Outline Revisited Again 00:01:00
Pointer Analysis in Datalog 00:01:00
Intra-procedural Pointer Analysis 00:03:00
Inter-procedural Pointer Analysis 00:04:00
Querying Pointer Analysis 00:02:00
Context Sensitivity 00:02:00
Cloning-based Inter-procedural Analysis 00:02:00
What About Recursion? 00:01:00
Summary-based Inter-procedural Analysis 00:01:00
Other Constraint Languages 00:02:00
What Have We Learned? 00:02:00
Type Systems
Introduction to Type Systems 00:01:00
Type Systems 00:01:00
Motivation AN 00:01:00
Type Systems 2 00:01:00
What Is a Type? 00:01:00
More Examples 00:01:00
Abstraction 00:01:00
What Is a Type? 2 00:01:00
A Simple Typed Language 00:03:00
Programs and Types 00:01:00
Next Steps 00:01:00
Notation for Inference Rules 00:01:00
From English to Inference Rule 00:02:00
Notation for Inference Rules 2 00:01:00
Rules for Integers 00:02:00
Example: 1+2 00:01:00
A Problem 00:01:00
A Solution 00:01:00
Type Environments 00:01:00
Modified Rules 00:01:00
A New Rule 00:01:00
Rules for Functions 00:02:00
All Rules Together 00:01:00
Type Derivations Example 00:04:00
Type Derivations 00:02:00
Back to the Original Example 00:01:00
A More Complex Rule 00:01:00
Soundness 00:01:00
Comments on Soundness 00:01:00
Constraints 00:01:00
Another Example 00:01:00
Type-Checking Algorithm 00:01:00
Global Analysis 00:02:00
Local Analysis 00:03:00
Global vs. Local Analysis AN 00:01:00
Properties of Type Systems AN 00:02:00
Static Analysis Using Type Rules AN 00:01:00
An Example: The Rule of Signs 00:01:00
Example Rules AN 00:01:00
Another Problem 00:01:00
More Example Rules AN 00:01:00
Flow Insensitivity AN 00:01:00
Comments on Flow Insensitivity 00:01:00
Flow Sensitivity AN 00:02:00
Comments on Flow Sensitivity 00:01:00
Path Sensitivity AN 00:02:00
Comments on Path Sensitivity 00:01:00
Flow & Path Sensitivity AN 00:03:00
Summary AN 00:01:00
What Have We Learned? AN l8 00:01:00
Statistical Debugging
Intro to Statistical Debugging AN 00:01:00
Motivation AN l9 00:01:00
An Idea: Statistical Debugging 00:01:00
Benefits of Statistical Debugging 00:01:00
Two Key Questions AN 00:01:00
Practical Challenges AN 00:02:00
The Approach AN 00:02:00
Overall Architecture AN 00:02:00
Model Behavior AN 00:01:00
Branches Are Interesting 00:01:00
Return Values Are Interesting AN 00:02:00
What Other Behaviors Are Interesting? AN 00:01:00
Identify the Predicates AN 00:01:00
Summarization and Reporting 1 AN 00:01:00
Summarization and Reporting 2 AN 00:02:00
Abstracting Predicate Counts 00:01:00
Populate the Predicates AN 00:01:00
The Need for Sampling AN 00:01:00
A Naive Sampling Approach 00:01:00
Some Other Problematic Approaches AN 00:01:00
Amortized Coin Tossing 00:01:00
An Efficient Approach 00:01:00
Feedback Reports with Sampling AN 00:01:00
Uncertainty Due to Sampling AN 00:02:00
Overall Architecture Revisited AN 00:01:00
Finding Causes of Bugs 2 AN 00:03:00
Tracking Context AN 00:01:00
A Useful Measure: Increase() 00:01:00
Increase() Works AN 00:02:00
Computing Increase() 00:01:00
Isolating the Bug 1 AN 00:01:00
A First Algorithm 00:02:00
Isolating the Bug 2 AN 00:01:00
Isolating a Single Bug in bc AN 00:01:00
It Works AN 00:01:00
Using the Information AN 00:01:00
Sample Report AN 00:01:00
Multiple Bugs: The Goal AN 00:01:00
Another Idea 00:01:00
Revised Algorithm AN 00:01:00
Ranking by Increase(P) AN 00:01:00
Ranking by F(P) AN 00:01:00
A Helpful Analogy 00:01:00
Combining Precision and Recall 00:01:00
Sorting by the Harmonic Mean AN 00:01:00
What Have We Learned? AN l9 00:02:00
Key Takeaway AN 00:01:00
Delta Debugging
Introduction to Delta Debugging AN 00:01:00
Simplification AN 00:01:00
Why Simplify? AN 00:01:00
Real World Scenario AN 00:01:00
How Do We Go from This… to This? AN 00:01:00
Your Solution AN 00:01:00
Binary Search 00:01:00
Complex Input 00:01:00
Simplified Input AN 00:01:00
Binary Search 2 00:01:00
Two Conflicting Solutions AN 00:01:00
Impact of Input Granularity AN 00:01:00
General Delta Debugging Algorithm AN 00:01:00
Inputs and Failures AN 00:01:00
Example of Delta Debugging AN 00:01:00
Changes 00:01:00
Decomposing Changes AN 00:01:00
Summary AN 00:01:00
Testing Test Cases AN 00:01:00
Minimizing Test Cases AN 00:01:00
Search for 1-Minimal Input AN 00:02:00
Minimizing Test Cases 2 AN 00:02:00
Naive Algorithm AN 00:02:00
Work Smarter Not Harder AN 00:01:00
Minimization Algorithm AN 00:01:00
Steps of the Minimization Algorithm AN 00:02:00
Asymptotic Analysis 00:01:00
Minimization Algorithm 2 AN 00:01:00
Case Study: GNU C Compiler 00:03:00
Case Study: GNU C Compiler 2 00:02:00
Case Study: Minimizing Fuzz Input 00:01:00
Another Application 00:01:00
Delta Debugging Wrap-Up AN 00:01:00
What Have We Learned? AN l10 00:01:00
Dynamic Symbolic Execution
Intro to Dynamic Symbolic Execution 00:01:00
Motivation 00:05:00
The Approach AN 00:02:00
Execution Path of a Program 00:02:00
Cognitive Development 00:30:00
Existing Approach I 00:01:00
Existing Approach II 00:04:00
Combined Approach 00:02:00
An Illustrative Example 00:06:00
Computation Tree 00:01:00
A More Complex Example 00:04:00
Example Application 00:01:00
A Third Example 00:04:00
Properties of DSE 00:01:00
Testing Data Structures 00:02:00
Data Structure Example 00:04:00
Approach in a Nutshell 00:01:00
Characteristics of DSE 00:01:00
Case Study: SGLIB C Library 00:02:00
Case Study: Needham-Schroeder Protocol 00:01:00
Realistic Implementations 00:01:00
Case Study: SAGE Tool at Microsoft 00:01:00
SAGE Crashing a Media Parser 00:01:00
What Have We Learned? 00:02:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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