Preface¶
Introduction¶
Programming Tutorials¶
- 2.1. Command Line Basics
- 2.2. Parsing Command Line Parameters In Your Program
- 2.3. Using Command Line Parameters in Eclipse
- 2.4. Installing the Web-CAT Submission Plug-in for Eclipse
- 2.5. Common Debugging Methods
- 2.6. Debugging In Eclipse
- 2.7. Reading Input (from Files or Otherwise)
- 2.8. Random Access Files In Java
- 2.9. JUnit Testing And You
- 2.10. Writing JUnit Tests
- 2.11. Code Coverage In JUnit
- 2.12. Mutation Coverage In JUnit
- 2.13. Mutation Testing Examples
- 2.13.1. Types of Mutants
- 2.13.1.1. Arithmetic Operation Mutant
- 2.13.1.2. Example Code 1: Arithmetic Operation Mutant
- 2.13.1.3. Logical Expression Mutant (Remove Conditionals)
- 2.13.1.4. Example Code 2: Logical Expression Mutant (Remove Conditionals)
- 2.13.1.5. Example Code 3: Multiple Mutants in One (EvenOddCheck)
- 2.13.1.6. Example Code 4: Loop Conditions (optional)
- 2.13.1. Types of Mutants
- 2.14. Mutation Coverage: FAQ
- 2.14.1. Frequently Asked Questions
- 2.14.1.1. What is Mutation Testing and why should I use it?
- 2.14.1.2. Does 100% Mutation Score mean 100% Project Correctness?
- 2.14.1.3. Why does writing Mutation Tests take so much time?
- 2.14.1.4. Why should I use Mutation Testing instead of Code Coverage?
- 2.14.1.5. Why are we using this particular set of mutation operators?
- 2.14.1.6. Does 100% Mutation Score mean my code is perfect?
- 2.14.1.7. Why do I have bugs in my code despite having 100% Mutation Score?
- 2.14.1.8. Why do my mutation tests not cover all branches of my code?
- 2.14.1.9. How do I localize the bugs in my code?
- 2.14.1.10. How do I recover the “Mutation List”/”Mutation Summary” tabs?
- 2.14.1. Frequently Asked Questions
- 2.15. Testing
- 2.16. Testing for Code Coverage
- 2.17. Another Example
- 2.18. Bowling Example
Mathematical Background¶
Algorithm Analysis¶
- 4.1. Chapter Introduction
- 4.2. Problems, Algorithms, and Programs
- 4.3. Comparing Algorithms
- 4.4. Best, Worst, and Average Cases
- 4.5. Faster Computer, or Faster Algorithm?
- 4.6. Asymptotic Analysis and Upper Bounds
- 4.7. Lower Bounds and \(\Theta\) Notation
- 4.8. Calculating Program Running Time
- 4.9. Analyzing Problems
- 4.10. Common Misunderstandings
- 4.11. Multiple Parameters
- 4.12. Space Bounds
- 4.13. Code Tuning and Empirical Analysis
- 4.14. Algorithm Analysis Summary Exercises
Linear Structures¶
- 5.1. Chapter Introduction: Lists
- 5.2. The List ADT
- 5.3. Array-Based List Implementation
- 5.4. Linked Lists
- 5.5. Comparison of List Implementations
- 5.6. Doubly Linked Lists
- 5.7. List Element Implementations
- 5.8. Stacks
- 5.9. Linked Stacks
- 5.10. Freelists
- 5.11. Implementing Recursion
- 5.12. Queues
- 5.13. Linked Queues
- 5.14. Linear Structure Summary Exercises
Design¶
Binary Trees¶
- 7.1. Binary Trees Chapter Introduction
- 7.2. Binary Trees
- 7.3. Binary Tree as a Recursive Data Structure
- 7.4. The Full Binary Tree Theorem
- 7.5. Binary Tree Traversals
- 7.6. Implementing Tree Traversals
- 7.7. Information Flow in Recursive Functions
- 7.7.1. Information Flow in Recursive Functions
- 7.7.2. Binary Tree Set Depth Exercise
- 7.7.3. Collect-and-return
- 7.7.4. Binary Tree Check Sum Exercise
- 7.7.5. Binary Tree Leaf Nodes Count Exercise
- 7.7.6. Binary Tree Sum Nodes Exercise
- 7.7.7. Combining Information Flows
- 7.7.8. Binary Tree Check Value Exercise
- 7.7.9. Combination Problems
- 7.7.10. Binary Tree Height Exercise
- 7.7.11. Binary Tree Get Difference Exercise
- 7.7.12. Binary Tree Has Path Sum Exercise
- 7.8. Binary Tree Node Implementations
- 7.9. Composite-based Expression Tree
- 7.10. Binary Tree Space Requirements
- 7.11. Binary Search Trees
- 7.12. Dictionary Implementation Using a BST
- 7.13. Binary Tree Guided Information Flow
- 7.14. Multiple Binary Trees
- 7.15. A Hard Information Flow Problem
- 7.16. Array Implementation for Complete Binary Trees
- 7.17. Heaps and Priority Queues
- 7.18. Huffman Coding Trees
- 7.19. Trees versus Tries
- 7.20. Proof of Optimality for Huffman Coding
- 7.21. Binary Tree Chapter Summary
Sorting¶
- 8.1. Chapter Introduction: Sorting
- 8.2. Sorting Terminology and Notation
- 8.3. Insertion Sort
- 8.4. Bubble Sort
- 8.5. Selection Sort
- 8.6. The Cost of Exchange Sorting
- 8.7. Optimizing Sort Algorithms with Code Tuning
- 8.8. Shellsort
- 8.9. Mergesort Concepts
- 8.10. Implementing Mergesort
- 8.11. Quicksort
- 8.12. Heapsort
- 8.13. Binsort
- 8.14. Radix Sort
- 8.15. An Empirical Comparison of Sorting Algorithms
- 8.16. Lower Bounds for Sorting
- 8.17. Sorting Summary Exercises
