Preface¶
Review from Basic Data Structures¶
Design¶
Programming & Debugging¶
- 3.1. Command Line Basics
- 3.2. Parsing Command Line Parameters In Your Program
- 3.3. Using Parameters in Eclipse
- 3.4. Installing the Web-CAT Submission Plug-in for Eclipse
- 3.5. Common Debugging Methods
- 3.6. Debugging In Eclipse
- 3.7. Reading Input (from Files or Otherwise)
- 3.8. Random Access Files In Java
- 3.9. JUnit Testing And You
- 3.10. Writing JUnit Tests
- 3.11. Code Coverage In JUnit
- 3.12. Mutation Coverage In JUnit
- 3.13. Mutation Testing Examples
- 3.13.1. Types of Mutants
- 3.13.1.1. Arithmetic Operation Mutant
- 3.13.1.2. Example Code 1: Arithmetic Operation Mutant
- 3.13.1.3. Logical Expression Mutant (Remove Conditionals)
- 3.13.1.4. Example Code 2: Logical Expression Mutant (Remove Conditionals)
- 3.13.1.5. Example Code 3: Multiple Mutants in One (EvenOddCheck)
- 3.13.1.6. Example Code 4: Loop Conditions (optional)
- 3.13.1. Types of Mutants
- 3.14. Mutation Coverage: FAQ
- 3.14.1. Frequently Asked Questions
- 3.14.1.1. What is Mutation Testing and why should I use it?
- 3.14.1.2. Does 100% Mutation Score mean 100% Project Correctness?
- 3.14.1.3. Why does writing Mutation Tests take so much time?
- 3.14.1.4. Why should I use Mutation Testing instead of Code Coverage?
- 3.14.1.5. Why are we using this particular set of mutation operators?
- 3.14.1.6. Does 100% Mutation Score mean my code is perfect?
- 3.14.1.7. Why do I have bugs in my code despite having 100% Mutation Score?
- 3.14.1.8. Why do my mutation tests not cover all branches of my code?
- 3.14.1.9. How do I localize the bugs in my code?
- 3.14.1.10. How do I recover the “Mutation List”/”Mutation Summary” tabs?
- 3.14.1. Frequently Asked Questions
- 3.15. Testing
- 3.16. Testing for Code Coverage
- 3.17. Another Example
- 3.18. Bowling Example
Mathematical Background¶
Algorithm Analysis¶
- 5.1. Chapter Introduction
- 5.2. Problems, Algorithms, and Programs
- 5.3. Comparing Algorithms
- 5.4. Best, Worst, and Average Cases
- 5.5. Faster Computer, or Faster Algorithm?
- 5.6. Asymptotic Analysis and Upper Bounds
- 5.7. Lower Bounds and \(\Theta\) Notation
- 5.8. Calculating Program Running Time
- 5.9. Analyzing Problems
- 5.10. Common Misunderstandings
- 5.11. Multiple Parameters
- 5.12. Space Bounds
- 5.13. Code Tuning and Empirical Analysis
- 5.14. Algorithm Analysis Summary Exercises
Linear Structures Revisited¶
- 6.1. Chapter Introduction: Lists
- 6.2. The List ADT
- 6.3. Array-Based List Implementation
- 6.4. Linked Lists
- 6.5. Comparison of List Implementations
- 6.6. Doubly Linked Lists
- 6.7. List Element Implementations
- 6.8. Stacks
- 6.9. Linked Stacks
- 6.10. Freelists
- 6.11. Implementing Recursion
- 6.12. Queues
- 6.13. Linked Queues
- 6.14. Linear Structure Summary Exercises
File Processing¶
Hashing¶
Memory Management¶
Binary Trees¶
- 10.1. Binary Trees Chapter Introduction
- 10.2. Binary Trees
- 10.3. Binary Tree as a Recursive Data Structure
- 10.4. The Full Binary Tree Theorem
- 10.5. Binary Tree Traversals
- 10.6. Implementing Tree Traversals
- 10.7. Information Flow in Recursive Functions
- 10.7.1. Information Flow in Recursive Functions
- 10.7.2. Binary Tree Set Depth Exercise
- 10.7.3. Collect-and-return
- 10.7.4. Binary Tree Check Sum Exercise
- 10.7.5. Binary Tree Leaf Nodes Count Exercise
- 10.7.6. Binary Tree Sum Nodes Exercise
- 10.7.7. Combining Information Flows
- 10.7.8. Binary Tree Check Value Exercise
- 10.7.9. Combination Problems
- 10.7.10. Binary Tree Height Exercise
- 10.7.11. Binary Tree Get Difference Exercise
- 10.7.12. Binary Tree Has Path Sum Exercise
- 10.8. Binary Tree Node Implementations
- 10.9. Composite-based Expression Tree
- 10.10. Binary Tree Space Requirements
- 10.11. Binary Search Trees
- 10.12. Dictionary Implementation Using a BST
- 10.13. Binary Tree Guided Information Flow
- 10.14. Multiple Binary Trees
- 10.15. A Hard Information Flow Problem
- 10.16. Array Implementation for Complete Binary Trees
- 10.17. Heaps and Priority Queues
- 10.18. Huffman Coding Trees
- 10.19. Trees versus Tries
- 10.20. Proof of Optimality for Huffman Coding
- 10.21. Binary Tree Chapter Summary
