Coding Interview University
I originally created this as a short todo list of study topics for becoming a software engineer, but it grew to the large list you see today. After going through this study plan, I got hired as a Software Development Engineer at Amazon! You probably won't have to study as much as I did. Anyway, everything you need is here.
I studied about 812 hours a day, for several months. This is my story: Why I studied fulltime for 8 months for a Google interview
The items listed here will prepare you well for a technical interview at just about any software company, including the giants: Amazon, Facebook, Google, and Microsoft.
Best of luck to you!
Translations:
Translations in progress:
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Special thanks to:
Dev environments built for the cloud
What is it?
This is my multimonth study plan for going from web developer (selftaught, no CS degree) to software engineer for a large company.
This is meant for new software engineers or those switching from software/web development to software engineering (where computer science knowledge is required). If you have many years of experience and are claiming many years of software engineering experience, expect a harder interview.
If you have many years of software/web development experience, note that large software companies like Google, Amazon, Facebook and Microsoft view software engineering as different from software/web development, and they require computer science knowledge.
If you want to be a reliability engineer or operations engineer, study more from the optional list (networking, security).
Table of Contents
Additional Resources
 Additional Books
 Additional Learning
 Compilers
 Emacs and vi(m)
 Unix command line tools
 Information theory
 Parity & Hamming Code
 Entropy
 Cryptography
 Compression
 Computer Security
 Garbage collection
 Parallel Programming
 Messaging, Serialization, and Queueing Systems
 A*
 Fast Fourier Transform
 Bloom Filter
 HyperLogLog
 LocalitySensitive Hashing
 van Emde Boas Trees
 Augmented Data Structures
 Balanced search trees
 AVL trees
 Splay trees
 Red/black trees
 23 search trees
 234 Trees (aka 24 trees)
 Nary (Kary, Mary) trees
 BTrees
 kD Trees
 Skip lists
 Network Flows
 Disjoint Sets & Union Find
 Math for Fast Processing
 Treap
 Linear Programming
 Geometry, Convex hull
 Discrete math
 Machine Learning
 Additional Detail on Some Subjects
 Video Series
 Computer Science Courses
 Papers
Why use it?
When I started this project, I didn't know a stack from a heap, didn't know BigO anything, anything about trees, or how to traverse a graph. If I had to code a sorting algorithm, I can tell ya it wouldn't have been very good. Every data structure I've ever used was built into the language, and I didn't know how they worked under the hood at all. I've never had to manage memory unless a process I was running would give an "out of memory" error, and then I'd have to find a workaround. I've used a few multidimensional arrays in my life and thousands of associative arrays, but I've never created data structures from scratch.
It's a long plan. It may take you months. If you are familiar with a lot of this already it will take you a lot less time.
How to use it
Everything below is an outline, and you should tackle the items in order from top to bottom.
I'm using GitHub's special markdown flavor, including tasks lists to track progress.
Create a new branch so you can check items like this, just put an x in the brackets: [x]
Fork a branch and follow the commands below
Fork the GitHub repo https://github.com/jwasham/codinginterviewuniversity by clicking on the Fork button
Clone to your local repo
git clone [email protected]:<your_github_username>/codinginterviewuniversity.git
git checkout b progress
git remote add jwasham https://github.com/jwasham/codinginterviewuniversity
git fetch all
Mark all boxes with X after you completed your changes
git add .
git commit m "Marked x"
git rebase jwasham/main
git push setupstream origin progress
git push force
More about GitHubflavored markdown
Don't feel you aren't smart enough
 Successful software engineers are smart, but many have an insecurity that they aren't smart enough.
 The myth of the Genius Programmer
 It's Dangerous to Go Alone: Battling the Invisible Monsters in Tech
About Video Resources
Some videos are available only by enrolling in a Coursera or EdX class. These are called MOOCs. Sometimes the classes are not in session so you have to wait a couple of months, so you have no access.
I'd appreciate your help to add free and alwaysavailable public sources, such as YouTube videos to accompany the online course videos.
I like using university lectures.
Interview Process & General Interview Prep
 How to Pass the Engineering Interview in 2021
 Whiteboarding
 Demystifying Tech Recruiting
 How to Get a Job at the Big 4:
 Cracking The Coding Interview Set 1:
 Cracking the Facebook Coding Interview:
 Prep Course:
 Software Engineer Interview Unleashed (paid course):
 Learn how to make yourself ready for software engineer interviews from a former Google interviewer.
 Python for Data Structures, Algorithms, and Interviews (paid course):
 A Python centric interview prep course which covers data structures, algorithms, mock interviews and much more.
 Intro to Data Structures and Algorithms using Python (Udacity free course):
 A free Python centric data structures and algorithms course.
 Data Structures and Algorithms Nanodegree! (Udacity paid Nanodegree):
 Get handson practice with over 100 data structures and algorithm exercises and guidance from a dedicated mentor to help prepare you for interviews and onthejob scenarios.
 Grokking the Behavioral Interview (Educative free course):
 Many times, it’s not your technical competency that holds you back from landing your dream job, it’s how you perform on the behavioral interview.
 Software Engineer Interview Unleashed (paid course):
Pick One Language for the Interview
You can use a language you are comfortable in to do the coding part of the interview, but for large companies, these are solid choices:
 C++
 Java
 Python
You could also use these, but read around first. There may be caveats:
 JavaScript
 Ruby
Here is an article I wrote about choosing a language for the interview: Pick One Language for the Coding Interview.
You need to be very comfortable in the language and be knowledgeable.
Read more about choices:
 http://www.bytebybyte.com/choosetherightlanguageforyourcodinginterview/
 http://blog.codingforinterviews.com/bestprogramminglanguagejobs/
You'll see some C, C++, and Python learning included below, because I'm learning. There are a few books involved, see the bottom.
Book List
This is a shorter list than what I used. This is abbreviated to save you time.
Interview Prep
 Programming Interviews Exposed: Coding Your Way Through the Interview, 4th Edition
 answers in C++ and Java
 this is a good warmup for Cracking the Coding Interview
 not too difficult, most problems may be easier than what you'll see in an interview (from what I've read)
 Cracking the Coding Interview, 6th Edition
 answers in Java
If you have tons of extra time:
Choose one:
 Elements of Programming Interviews (C++ version)
 Elements of Programming Interviews in Python
 Elements of Programming Interviews (Java version)
Language Specific
You need to choose a language for the interview (see above).
Here are my recommendations by language. I don't have resources for all languages. I welcome additions.
If you read through one of these, you should have all the data structures and algorithms knowledge you'll need to start doing coding problems. You can skip all the video lectures in this project, unless you'd like a review.
Additional languagespecific resources here.
C++
I haven't read these two, but they are highly rated and written by Sedgewick. He's awesome.
 Algorithms in C++, Parts 14: Fundamentals, Data Structure, Sorting, Searching
 Algorithms in C++ Part 5: Graph Algorithms
 Open Data Structures in C++
 Rich and detailed collection of Data Structures and Algorithms
 Great for firsttimers
If you have a better recommendation for C++, please let me know. Looking for a comprehensive resource.
Java
 Algorithms (Sedgewick and Wayne)
 videos with book content (and Sedgewick!) on coursera:
OR:
 Data Structures and Algorithms in Java
 by Goodrich, Tamassia, Goldwasser
 used as optional text for CS intro course at UC Berkeley
 see my book report on the Python version below. This book covers the same topics
Python
 Data Structures and Algorithms in Python
 by Goodrich, Tamassia, Goldwasser
 I loved this book. It covered everything and more
 Pythonic code
 my glowing book report: https://startupnextdoor.com/bookreportdatastructuresandalgorithmsinpython/
 Open Data Structures in Python
Before you Get Started
This list grew over many months, and yes, it kind of got out of hand.
Here are some mistakes I made so you'll have a better experience.
1. You Won't Remember it All
I watched hours of videos and took copious notes, and months later there was much I didn't remember. I spent 3 days going through my notes and making flashcards, so I could review.
Please, read so you won't make my mistakes:
Retaining Computer Science Knowledge.
A course recommended to me (haven't taken it): Learning how to Learn.
2. Use Flashcards
To solve the problem, I made a little flashcards site where I could add flashcards of 2 types: general and code. Each card has different formatting.
I made a mobilefirst website, so I could review on my phone and tablet, wherever I am.
Make your own for free:
 Flashcards site repo
 My flash cards database (old  1200 cards):
 My flash cards database (new  1800 cards):
Keep in mind I went overboard and have cards covering everything from assembly language and Python trivia to machine learning and statistics. It's way too much for what's required.
Note on flashcards: The first time you recognize you know the answer, don't mark it as known. You have to see the same card and answer it several times correctly before you really know it. Repetition will put that knowledge deeper in your brain.
An alternative to using my flashcard site is Anki, which has been recommended to me numerous times. It uses a repetition system to help you remember. It's userfriendly, available on all platforms and has a cloud sync system. It costs $25 on iOS but is free on other platforms.
My flashcard database in Anki format: https://ankiweb.net/shared/info/25173560 (thanks @xiewenya).
3. Start doing coding interview questions while you're learning data structures and algorithms
You need to apply what you're learning to solving problems, or you'll forget. I made this mistake. Once you've learned a topic, and feel comfortable with it, like linked lists, open one of the coding interview books and do a couple of questions regarding linked lists. Then move on to the next learning topic. Then later, go back and do another linked list problem, or recursion problem, or whatever. But keep doing problems while you're learning. You're not being hired for knowledge, but how you apply the knowledge. There are several books and sites I recommend. See here for more: Coding Question Practice.
4. Review, review, review
I keep a set of cheat sheets on ASCII, OSI stack, BigO notations, and more. I study them when I have some spare time.
Take a break from programming problems for a half hour and go through your flashcards.
5. Focus
There are a lot of distractions that can take up valuable time. Focus and concentration are hard. Turn on some music without lyrics and you'll be able to focus pretty well.
What you won't see covered
These are prevalent technologies but not part of this study plan:
 SQL
 Javascript
 HTML, CSS, and other frontend technologies
The Daily Plan
Some subjects take one day, and some will take multiple days. Some are just learning with nothing to implement.
Each day I take one subject from the list below, watch videos about that subject, and write an implementation in:
 C  using structs and functions that take a struct * and something else as args
 C++  without using builtin types
 C++  using builtin types, like STL's std::list for a linked list
 Python  using builtin types (to keep practicing Python)
 and write tests to ensure I'm doing it right, sometimes just using simple assert() statements
 You may do Java or something else, this is just my thing
You don't need all these. You need only one language for the interview.
Why code in all of these?
 Practice, practice, practice, until I'm sick of it, and can do it with no problem (some have many edge cases and bookkeeping details to remember)
 Work within the raw constraints (allocating/freeing memory without help of garbage collection (except Python or Java))
 Make use of builtin types, so I have experience using the builtin tools for realworld use (not going to write my own linked list implementation in production)
I may not have time to do all of these for every subject, but I'll try.
You can see my code here:
You don't need to memorize the guts of every algorithm.
Write code on a whiteboard or paper, not a computer. Test with some sample inputs. Then test it out on a computer.
Prerequisite Knowledge

Learn C
 C is everywhere. You'll see examples in books, lectures, videos, everywhere while you're studying
 The C Programming Language, Vol 2
 This is a short book, but it will give you a great handle on the C language and if you practice it a little you'll quickly get proficient. Understanding C helps you understand how programs and memory work
 Answers to questions

How computers process a program:
Algorithmic complexity / BigO / Asymptotic analysis
 Nothing to implement
 There are a lot of videos here. Just watch enough until you understand it. You can always come back and review
 If some lectures are too mathy, you can jump down to the bottom and watch the discrete mathematics videos to get the background knowledge
 Harvard CS50  Asymptotic Notation (video)
 Big O Notations (general quick tutorial) (video)
 Big O Notation (and Omega and Theta)  best mathematical explanation (video)
 Skiena:
 A Gentle Introduction to Algorithm Complexity Analysis
 Orders of Growth (video)
 Asymptotics (video)
 UC Berkeley Big O (video)
 UC Berkeley Big Omega (video)
 Amortized Analysis (video)
 Illustrating "Big O" (video)
 TopCoder (includes recurrence relations and master theorem):
 Cheat sheet
Data Structures

Arrays
 Implement an automatically resizing vector.
 Description:
 Arrays (video)
 UC Berkeley CS61B  Linear and MultiDim Arrays (video) (Start watching from 15m 32s)
 Dynamic Arrays (video)
 Jagged Arrays (video)
 Implement a vector (mutable array with automatic resizing):
 Practice coding using arrays and pointers, and pointer math to jump to an index instead of using indexing.
 New raw data array with allocated memory
 can allocate int array under the hood, just not use its features
 start with 16, or if starting number is greater, use power of 2  16, 32, 64, 128
 size()  number of items
 capacity()  number of items it can hold
 is_empty()
 at(index)  returns item at given index, blows up if index out of bounds
 push(item)
 insert(index, item)  inserts item at index, shifts that index's value and trailing elements to the right
 prepend(item)  can use insert above at index 0
 pop()  remove from end, return value
 delete(index)  delete item at index, shifting all trailing elements left
 remove(item)  looks for value and removes index holding it (even if in multiple places)
 find(item)  looks for value and returns first index with that value, 1 if not found
 resize(new_capacity) // private function
 when you reach capacity, resize to double the size
 when popping an item, if size is 1/4 of capacity, resize to half
 Time
 O(1) to add/remove at end (amortized for allocations for more space), index, or update
 O(n) to insert/remove elsewhere
 Space
 contiguous in memory, so proximity helps performance
 space needed = (array capacity, which is >= n) * size of item, but even if 2n, still O(n)

Linked Lists
 Description:
 C Code (video)  not the whole video, just portions about Node struct and memory allocation
 Linked List vs Arrays:
 why you should avoid linked lists (video)
 Gotcha: you need pointer to pointer knowledge: (for when you pass a pointer to a function that may change the address where that pointer points) This page is just to get a grasp on ptr to ptr. I don't recommend this list traversal style. Readability and maintainability suffer due to cleverness.
 Implement (I did with tail pointer & without):
 size()  returns number of data elements in list
 empty()  bool returns true if empty
 value_at(index)  returns the value of the nth item (starting at 0 for first)
 push_front(value)  adds an item to the front of the list
 pop_front()  remove front item and return its value
 push_back(value)  adds an item at the end
 pop_back()  removes end item and returns its value
 front()  get value of front item
 back()  get value of end item
 insert(index, value)  insert value at index, so current item at that index is pointed to by new item at index
 erase(index)  removes node at given index
 value_n_from_end(n)  returns the value of the node at nth position from the end of the list
 reverse()  reverses the list
 remove_value(value)  removes the first item in the list with this value
 Doublylinked List
 Description (video)
 No need to implement

Stack
 Stacks (video)
 Will not implement. Implementing with array is trivial

Queue
 Queue (video)
 Circular buffer/FIFO
 Implement using linkedlist, with tail pointer:
 enqueue(value)  adds value at position at tail
 dequeue()  returns value and removes least recently added element (front)
 empty()
 Implement using fixedsized array:
 enqueue(value)  adds item at end of available storage
 dequeue()  returns value and removes least recently added element
 empty()
 full()
 Cost:
 a bad implementation using linked list where you enqueue at head and dequeue at tail would be O(n) because you'd need the next to last element, causing a full traversal each dequeue
 enqueue: O(1) (amortized, linked list and array [probing])
 dequeue: O(1) (linked list and array)
 empty: O(1) (linked list and array)

Hash table

Videos:

Online Courses:

Implement with array using linear probing
 hash(k, m)  m is size of hash table
 add(key, value)  if key already exists, update value
 exists(key)
 get(key)
 remove(key)

More Knowledge

Binary search
 Binary Search (video)
 Binary Search (video)
 detail
 Implement:
 binary search (on sorted array of integers)
 binary search using recursion

Bitwise operations
 Bits cheat sheet  you should know many of the powers of 2 from (2^1 to 2^16 and 2^32)
 Get a really good understanding of manipulating bits with: &, , ^, ~, >>, <<
 2s and 1s complement
 Count set bits
 Swap values:
 Absolute value:
Trees

Trees  Notes & Background
 Series: Trees (video)
 basic tree construction
 traversal
 manipulation algorithms
 BFS(breadthfirst search) and DFS(depthfirst search) (video)
 BFS notes:
 level order (BFS, using queue)
 time complexity: O(n)
 space complexity: best: O(1), worst: O(n/2)=O(n)
 DFS notes:
 time complexity: O(n)
 space complexity: best: O(log n)  avg. height of tree worst: O(n)
 inorder (DFS: left, self, right)
 postorder (DFS: left, right, self)
 preorder (DFS: self, left, right)
 BFS notes:

Binary search trees: BSTs
 Binary Search Tree Review (video)
 Introduction (video)
 MIT (video)
 C/C++:
 Binary search tree  Implementation in C/C++ (video)
 BST implementation  memory allocation in stack and heap (video)
 Find min and max element in a binary search tree (video)
 Find height of a binary tree (video)
 Binary tree traversal  breadthfirst and depthfirst strategies (video)
 Binary tree: Level Order Traversal (video)
 Binary tree traversal: Preorder, Inorder, Postorder (video)
 Check if a binary tree is binary search tree or not (video)
 Delete a node from Binary Search Tree (video)
 Inorder Successor in a binary search tree (video)
 Implement:
 insert // insert value into tree
 get_node_count // get count of values stored
 print_values // prints the values in the tree, from min to max
 delete_tree
 is_in_tree // returns true if given value exists in the tree
 get_height // returns the height in nodes (single node's height is 1)
 get_min // returns the minimum value stored in the tree
 get_max // returns the maximum value stored in the tree
 is_binary_search_tree
 delete_value
 get_successor // returns nexthighest value in tree after given value, 1 if none

Heap / Priority Queue / Binary Heap
 visualized as a tree, but is usually linear in storage (array, linked list)
 Heap
 Introduction (video)
 Naive Implementations (video)
 Binary Trees (video)
 Tree Height Remark (video)
 Basic Operations (video)
 Complete Binary Trees (video)
 Pseudocode (video)
 Heap Sort  jumps to start (video)
 Heap Sort (video)
 Building a heap (video)
 MIT: Heaps and Heap Sort (video)
 CS 61B Lecture 24: Priority Queues (video)
 Linear Time BuildHeap (maxheap)
 Implement a maxheap:
 insert
 sift_up  needed for insert
 get_max  returns the max item, without removing it
 get_size()  return number of elements stored
 is_empty()  returns true if heap contains no elements
 extract_max  returns the max item, removing it
 sift_down  needed for extract_max
 remove(i)  removes item at index x
 heapify  create a heap from an array of elements, needed for heap_sort
 heap_sort()  take an unsorted array and turn it into a sorted array inplace using a max heap or min heap
Sorting

Notes:
 Implement sorts & know best case/worst case, average complexity of each:
 no bubble sort  it's terrible  O(n^2), except when n <= 16
 Stability in sorting algorithms ("Is Quicksort stable?")
 Which algorithms can be used on linked lists? Which on arrays? Which on both?
 I wouldn't recommend sorting a linked list, but merge sort is doable.
 Merge Sort For Linked List
 Implement sorts & know best case/worst case, average complexity of each:

For heapsort, see Heap data structure above. Heap sort is great, but not stable

UC Berkeley:

Merge sort code:

Quick sort code:

Implement:
 Mergesort: O(n log n) average and worst case
 Quicksort O(n log n) average case
 Selection sort and insertion sort are both O(n^2) average and worst case
 For heapsort, see Heap data structure above

Not required, but I recommended them:
As a summary, here is a visual representation of 15 sorting algorithms. If you need more detail on this subject, see "Sorting" section in Additional Detail on Some Subjects
Graphs
Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.

Notes:
 There are 4 basic ways to represent a graph in memory:
 objects and pointers
 adjacency matrix
 adjacency list
 adjacency map
 Familiarize yourself with each representation and its pros & cons
 BFS and DFS  know their computational complexity, their trade offs, and how to implement them in real code
 When asked a question, look for a graphbased solution first, then move on if none
 There are 4 basic ways to represent a graph in memory:

MIT(videos):

Skiena Lectures  great intro:
 CSE373 2012  Lecture 11  Graph Data Structures (video)
 CSE373 2012  Lecture 12  BreadthFirst Search (video)
 CSE373 2012  Lecture 13  Graph Algorithms (video)
 CSE373 2012  Lecture 14  Graph Algorithms (con't) (video)
 CSE373 2012  Lecture 15  Graph Algorithms (con't 2) (video)
 CSE373 2012  Lecture 16  Graph Algorithms (con't 3) (video)

Graphs (review and more):
 6.006 SingleSource Shortest Paths Problem (video)
 6.006 Dijkstra (video)
 6.006 BellmanFord (video)
 6.006 Speeding Up Dijkstra (video)
 Aduni: Graph Algorithms I  Topological Sorting, Minimum Spanning Trees, Prim's Algorithm  Lecture 6 (video)
 Aduni: Graph Algorithms II  DFS, BFS, Kruskal's Algorithm, Union Find Data Structure  Lecture 7 (video)
 Aduni: Graph Algorithms III: Shortest Path  Lecture 8 (video)
 Aduni: Graph Alg. IV: Intro to geometric algorithms  Lecture 9 (video)

CS 61B 2014 (starting at 58:09) (video)  CS 61B 2014: Weighted graphs (video)
 Greedy Algorithms: Minimum Spanning Tree (video)
 Strongly Connected Components Kosaraju's Algorithm Graph Algorithm (video)

Full Coursera Course:

I'll implement:
 DFS with adjacency list (recursive)
 DFS with adjacency list (iterative with stack)
 DFS with adjacency matrix (recursive)
 DFS with adjacency matrix (iterative with stack)
 BFS with adjacency list
 BFS with adjacency matrix
 singlesource shortest path (Dijkstra)
 minimum spanning tree
 DFSbased algorithms (see Aduni videos above):
 check for cycle (needed for topological sort, since we'll check for cycle before starting)
 topological sort
 count connected components in a graph
 list strongly connected components
 check for bipartite graph
Even More Knowledge

Recursion
 Stanford lectures on recursion & backtracking:
 When it is appropriate to use it?
 How is tail recursion better than not?

Dynamic Programming
 You probably won't see any dynamic programming problems in your interview, but it's worth being able to recognize a problem as being a candidate for dynamic programming.
 This subject can be pretty difficult, as each DP soluble problem must be defined as a recursion relation, and coming up with it can be tricky.
 I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.
 Videos:
 the Skiena videos can be hard to follow since he sometimes uses the whiteboard, which is too small to see
 Skiena: CSE373 2012  Lecture 19  Introduction to Dynamic Programming (video)
 Skiena: CSE373 2012  Lecture 20  Edit Distance (video)
 Skiena: CSE373 2012  Lecture 21  Dynamic Programming Examples (video)
 Skiena: CSE373 2012  Lecture 22  Applications of Dynamic Programming (video)
 Simonson: Dynamic Programming 0 (starts at 59:18) (video)
 Simonson: Dynamic Programming I  Lecture 11 (video)
 Simonson: Dynamic programming II  Lecture 12 (video)
 List of individual DP problems (each is short): Dynamic Programming (video)
 Yale Lecture notes:
 Coursera:

ObjectOriented Programming
 Optional: UML 2.0 Series (video)
 SOLID OOP Principles: SOLID Principles (video)

Design patterns
 Quick UML review (video)
 Learn these patterns:
 strategy
 singleton
 adapter
 prototype
 decorator
 visitor
 factory, abstract factory
 facade
 observer
 proxy
 delegate
 command
 state
 memento
 iterator
 composite
 flyweight
 Chapter 6 (Part 1)  Patterns (video)
 Chapter 6 (Part 2)  AbstractionOccurrence, General Hierarchy, PlayerRole, Singleton, Observer, Delegation (video)
 Chapter 6 (Part 3)  Adapter, Facade, Immutable, ReadOnly Interface, Proxy (video)
 Series of videos (27 videos)
 Head First Design Patterns
 I know the canonical book is "Design Patterns: Elements of Reusable ObjectOriented Software", but Head First is great for beginners to OO.
 Handy reference: 101 Design Patterns & Tips for Developers
 Design patterns for humans

Combinatorics (n choose k) & Probability
 Math Skills: How to find Factorial, Permutation and Combination (Choose) (video)
 Make School: Probability (video)
 Make School: More Probability and Markov Chains (video)
 Khan Academy:
 Course layout:
 Just the videos  41 (each are simple and each are short):

NP, NPComplete and Approximation Algorithms
 Know about the most famous classes of NPcomplete problems, such as traveling salesman and the knapsack problem, and be able to recognize them when an interviewer asks you them in disguise.
 Know what NPcomplete means.
 Computational Complexity (video)
 Simonson:
 Skiena:
 Complexity: P, NP, NPcompleteness, Reductions (video)
 Complexity: Approximation Algorithms (video)
 Complexity: FixedParameter Algorithms (video)
 Peter Norvig discusses nearoptimal solutions to traveling salesman problem:
 Pages 1048  1140 in CLRS if you have it.

Caches

Processes and Threads
 Computer Science 162  Operating Systems (25 videos):
 for processes and threads see videos 111
 Operating Systems and System Programming (video)
 What Is The Difference Between A Process And A Thread?
 Covers:
 Processes, Threads, Concurrency issues
 Difference between processes and threads
 Processes
 Threads
 Locks
 Mutexes
 Semaphores
 Monitors
 How they work?
 Deadlock
 Livelock
 CPU activity, interrupts, context switching
 Modern concurrency constructs with multicore processors
 Paging, segmentation and virtual memory (video)
 Interrupts (video)
 Process resource needs (memory: code, static storage, stack, heap, and also file descriptors, i/o)
 Thread resource needs (shares above (minus stack) with other threads in the same process but each has its own pc, stack counter, registers, and stack)
 Forking is really copy on write (readonly) until the new process writes to memory, then it does a full copy.
 Context switching
 How context switching is initiated by the operating system and underlying hardware?
 Processes, Threads, Concurrency issues
 threads in C++ (series  10 videos)
 concurrency in Python (videos):
 Computer Science 162  Operating Systems (25 videos):

Testing
 To cover:
 how unit testing works
 what are mock objects
 what is integration testing
 what is dependency injection
 Agile Software Testing with James Bach (video)
 Open Lecture by James Bach on Software Testing (video)
 Steve Freeman  TestDriven Development (that’s not what we meant) (video)
 Dependency injection:
 How to write tests
 To cover:

Scheduling
 In an OS, how it works?
 Can be gleaned from Operating System videos

String searching & manipulations
 Sedgewick  Suffix Arrays (video)
 Sedgewick  Substring Search (videos)
 Search pattern in text (video)
If you need more detail on this subject, see "String Matching" section in Additional Detail on Some Subjects.

Tries
 Note there are different kinds of tries. Some have prefixes, some don't, and some use string instead of bits to track the path
 I read through code, but will not implement
 Sedgewick  Tries (3 videos)
 Notes on Data Structures and Programming Techniques
 Short course videos:
 The Trie: A Neglected Data Structure
 TopCoder  Using Tries
 Stanford Lecture (real world use case) (video)
 MIT, Advanced Data Structures, Strings (can get pretty obscure about halfway through) (video)

Floating Point Numbers

Unicode

Endianness
 Big And Little Endian
 Big Endian Vs Little Endian (video)
 Big And Little Endian Inside/Out (video)
 Very technical talk for kernel devs. Don't worry if most is over your head.
 The first half is enough.

Networking
 if you have networking experience or want to be a reliability engineer or operations engineer, expect questions
 Otherwise, this is just good to know
 Khan Academy
 UDP and TCP: Comparison of Transport Protocols (video)
 TCP/IP and the OSI Model Explained! (video)
 Packet Transmission across the Internet. Networking & TCP/IP tutorial. (video)
 HTTP (video)
 SSL and HTTPS (video)
 SSL/TLS (video)
 HTTP 2.0 (video)
 Video Series (21 videos) (video)
 Subnetting Demystified  Part 5 CIDR Notation (video)
 Sockets:
System Design, Scalability, Data Handling
You can expect system design questions if you have 4+ years of experience.
 Scalability and System Design are very large topics with many topics and resources, since there is a lot to consider when designing a software/hardware system that can scale. Expect to spend quite a bit of time on this
 Considerations:
 Scalability
 Distill large data sets to single values
 Transform one data set to another
 Handling obscenely large amounts of data
 System design
 features sets
 interfaces
 class hierarchies
 designing a system under certain constraints
 simplicity and robustness
 tradeoffs
 performance analysis and optimization
 Scalability
 START HERE: The System Design Primer
 System Design from HiredInTech
 How Do I Prepare To Answer Design Questions In A Technical Interview?
 8 Things You Need to Know Before a System Design Interview
 Database Normalization  1NF, 2NF, 3NF and 4NF (video)
 System Design Interview  There are a lot of resources in this one. Look through the articles and examples. I put some of them below
 How to ace a systems design interview
 Numbers Everyone Should Know
 How long does it take to make a context switch?
 Transactions Across Datacenters (video)
 A plain English introduction to CAP Theorem
 MIT 6.824: Distributed Systems, Spring 2020 (20 videos)
 Consensus Algorithms:
 Consistent Hashing
 NoSQL Patterns
 Scalability:
 You don't need all of these. Just pick a few that interest you.
 Great overview (video)
 Short series:
 Scalable Web Architecture and Distributed Systems
 Fallacies of Distributed Computing Explained
 Jeff Dean  Building Software Systems At Google and Lessons Learned (video)
 Introduction to Architecting Systems for Scale
 Scaling mobile games to a global audience using App Engine and Cloud Datastore (video)
 How Google Does PlanetScale Engineering for PlanetScale Infra (video)
 The Importance of Algorithms
 Sharding
 Engineering for the Long Game  Astrid Atkinson Keynote(video)
 7 Years Of YouTube Scalability Lessons In 30 Minutes
 How PayPal Scaled To Billions Of Transactions Daily Using Just 8VMs
 How to Remove Duplicates in Large Datasets
 A look inside Etsy's scale and engineering culture with Jon Cowie (video)
 What Led Amazon to its Own Microservices Architecture
 To Compress Or Not To Compress, That Was Uber's Question
 When Should Approximate Query Processing Be Used?
 Google's Transition From Single Datacenter, To Failover, To A Native Multihomed Architecture
 The Image Optimization Technology That Serves Millions Of Requests Per Day
 A Patreon Architecture Short
 Tinder: How Does One Of The Largest Recommendation Engines Decide Who You'll See Next?
 Design Of A Modern Cache
 Live Video Streaming At Facebook Scale
 A Beginner's Guide To Scaling To 11 Million+ Users On Amazon's AWS
 A 360 Degree View Of The Entire Netflix Stack
 Latency Is Everywhere And It Costs You Sales  How To Crush It
 What Powers Instagram: Hundreds of Instances, Dozens of Technologies
 Salesforce Architecture  How They Handle 1.3 Billion Transactions A Day
 ESPN's Architecture At Scale  Operating At 100,000 Duh Nuh Nuhs Per Second
 See "Messaging, Serialization, and Queueing Systems" way below for info on some of the technologies that can glue services together
 Twitter:
 For even more, see "Mining Massive Datasets" video series in the Video Series section
 Practicing the system design process: Here are some ideas to try working through on paper, each with some documentation on how it was handled in the real world:
 review: The System Design Primer
 System Design from HiredInTech
 cheat sheet
 flow:
 Understand the problem and scope:
 Define the use cases, with interviewer's help
 Suggest additional features
 Remove items that interviewer deems out of scope
 Assume high availability is required, add as a use case
 Think about constraints:
 Ask how many requests per month
 Ask how many requests per second (they may volunteer it or make you do the math)
 Estimate reads vs. writes percentage
 Keep 80/20 rule in mind when estimating
 How much data written per second
 Total storage required over 5 years
 How much data read per second
 Abstract design:
 Layers (service, data, caching)
 Infrastructure: load balancing, messaging
 Rough overview of any key algorithm that drives the service
 Consider bottlenecks and determine solutions
 Understand the problem and scope:
 Exercises:
Final Review
This section will have shorter videos that you can watch pretty quickly to review most of the important concepts.
It's nice if you want a refresher often.
 Series of 23 minutes short subject videos (23 videos)
 Series of 25 minutes short subject videos  Michael Sambol (18 videos):
 Sedgewick Videos  Algorithms I
 Sedgewick Videos  Algorithms II
Coding Question Practice
Now that you know all the computer science topics above, it's time to practice answering coding problems.
Coding question practice is not about memorizing answers to programming problems.
Why you need to practice doing programming problems:
 Problem recognition, and where the right data structures and algorithms fit in
 Gathering requirements for the problem
 Talking your way through the problem like you will in the interview
 Coding on a whiteboard or paper, not a computer
 Coming up with time and space complexity for your solutions
 Testing your solutions
There is a great intro for methodical, communicative problem solving in an interview. You'll get this from the programming interview books, too, but I found this outstanding: Algorithm design canvas
No whiteboard at home? That makes sense. I'm a weirdo and have a big whiteboard. Instead of a whiteboard, pick up a large drawing pad from an art store. You can sit on the couch and practice. This is my "sofa whiteboard". I added the pen in the photo for scale. If you use a pen, you'll wish you could erase. Gets messy quick. I use a pencil and eraser.
Supplemental:
 Mathematics for Topcoders
 Dynamic Programming – From Novice to Advanced
 MIT Interview Materials
 Exercises for getting better at a given language
Read and Do Programming Problems (in this order):
 Programming Interviews Exposed: Coding Your Way Through the Interview, 4th Edition
 answers in C, C++ and Java
 Cracking the Coding Interview, 6th Edition
 answers in Java
See Book List above
Coding exercises/challenges
Once you've learned your brains out, put those brains to work. Take coding challenges every day, as many as you can.
Coding Interview Question Videos:
 IDeserve (88 videos)
 Tushar Roy (5 playlists)
 Super for walkthroughs of problem solutions
 Nick White  LeetCode Solutions (187 Videos)
 Good explanations of solution and the code
 You can watch several in a short time
 FisherCoder  LeetCode Solutions
Challenge sites:
 LeetCode
 My favorite coding problem site. It's worth the subscription money for the 12 months you'll likely be preparing
 LeetCode solutions from FisherCoder
 See Nick White Videos above for short codethroughs
 HackerRank
 TopCoder
 InterviewCake
 Geeks for Geeks
 InterviewBit
 Project Euler (mathfocused)
 Code Exercises
Languagelearning sites, with challenges:
Challenge repos:
Mock Interviews:
 Gainlo.co: Mock interviewers from big companies  I used this and it helped me relax for the phone screen and onsite interview
 Pramp: Mock interviews from/with peers  peertopeer model of practice interviews
 Refdash: Mock interviews and expedited interviews  also help candidates fast track by skipping multiple interviews with tech companies
 interviewing.io: Practice mock interview with senior engineers  anonymous algorithmic/systems design interviews with senior engineers from FAANG anonymously.
Once you're closer to the interview
 Cracking The Coding Interview Set 2 (videos):
Best way to find perfect career opportunities for you
 If you are trying to find new job opportunities, or if you are trying to find internships, if want to know about the salary of a job role in any companies or want to just know about the company reputation before applying to that company, here are the list of platforms which can help you find the above answers and more.
 Best Websites for Careers & Jobs
Your Resume
 See Resume prep items in Cracking The Coding Interview and back of Programming Interviews Exposed
 Very Important thing to remember while creating your resume, if you applying for big companies is that make it ATS Compliant.
 How to Create or Check if your Resume is ATS Compliant
Be thinking of for when the interview comes
Think of about 20 interview questions you'll get, along with the lines of the items below. Have 23 answers for each. Have a story, not just data, about something you accomplished.

Why do you want this job?

What's a tough problem you've solved?

Biggest challenges faced?

Best/worst designs seen?

Ideas for improving an existing product

How do you work best, as an individual and as part of a team?

Which of your skills or experiences would be assets in the role and why?

What did you most enjoy at [job x / project y]?

What was the biggest challenge you faced at [job x / project y]?

What was the hardest bug you faced at [job x / project y]?

What did you learn at [job x / project y]?

What would you have done better at [job x / project y]?

If you find hard to come up with good answers of this type interview questions, you can refer below link for some answer templates and have some idea.
Have questions for the interviewer
Some of mine (I already may know answer to but want their opinion or team perspective):
 How large is your team?
 What does your dev cycle look like? Do you do waterfall/sprints/agile?
 Are rushes to deadlines common? Or is there flexibility?
 How are decisions made in your team?
 How many meetings do you have per week?
 Do you feel your work environment helps you concentrate?
 What are you working on?
 What do you like about it?
 What is the work life like?
 How is work/life balance?
Once You've Got The Job
Congratulations!
Keep learning.
You're never really done.
*****************************************************************************************************
*****************************************************************************************************
Everything below this point is optional.
By studying these, you'll get greater exposure to more CS concepts, and will be better prepared for
any software engineering job. You'll be a much more wellrounded software engineer.
*****************************************************************************************************
*****************************************************************************************************
Additional Books
These are here so you can dive into a topic you find interesting.

The Unix Programming Environment
 An oldie but a goodie

The Linux Command Line: A Complete Introduction
 A modern option

 A gentle introduction to design patterns

Design Patterns: Elements of Reusable ObjectOriented Software
 AKA the "Gang Of Four" book, or GOF
 The canonical design patterns book

Algorithm Design Manual (Skiena)
 As a review and problem recognition
 The algorithm catalog portion is well beyond the scope of difficulty you'll get in an interview
 This book has 2 parts:
 Class textbook on data structures and algorithms
 Pros:
 Is a good review as any algorithms textbook would be
 Nice stories from his experiences solving problems in industry and academia
 Code examples in C
 Cons:
 Can be as dense or impenetrable as CLRS, and in some cases, CLRS may be a better alternative for some subjects
 Chapters 7, 8, 9 can be painful to try to follow, as some items are not explained well or require more brain than I have
 Don't get me wrong: I like Skiena, his teaching style, and mannerisms, but I may not be Stony Brook material
 Pros:
 Algorithm catalog:
 This is the real reason you buy this book
 About to get to this part. Will update here once I've made my way through it
 Class textbook on data structures and algorithms
 Can rent it on Kindle
 Answers:
 Errata

Write Great Code: Volume 1: Understanding the Machine
 The book was published in 2004, and is somewhat outdated, but it's a terrific resource for understanding a computer in brief
 The author invented HLA, so take mentions and examples in HLA with a grain of salt. Not widely used, but decent examples of what assembly looks like
 These chapters are worth the read to give you a nice foundation:
 Chapter 2  Numeric Representation
 Chapter 3  Binary Arithmetic and Bit Operations
 Chapter 4  FloatingPoint Representation
 Chapter 5  Character Representation
 Chapter 6  Memory Organization and Access
 Chapter 7  Composite Data Types and Memory Objects
 Chapter 9  CPU Architecture
 Chapter 10  Instruction Set Architecture
 Chapter 11  Memory Architecture and Organization

 Important: Reading this book will only have limited value. This book is a great review of algorithms and data structures, but won't teach you how to write good code. You have to be able to code a decent solution efficiently
 AKA CLR, sometimes CLRS, because Stein was late to the game

Computer Architecture, Sixth Edition: A Quantitative Approach
 For a richer, more uptodate (2017), but longer treatment

 The first couple of chapters present clever solutions to programming problems (some very old using data tape) but that is just an intro. This a guidebook on program design and architecture
Additional Learning
I added them to help you become a wellrounded software engineer, and to be aware of certain
technologies and algorithms, so you'll have a bigger toolbox.

Compilers

Emacs and vi(m)
 Familiarize yourself with a unixbased code editor
 vi(m):
 emacs:

Unix command line tools

Information theory (videos)
 Khan Academy
 More about Markov processes:
 See more in MIT 6.050J Information and Entropy series below

Parity & Hamming Code (videos)
 Intro
 Parity
 Hamming Code:
 Error Checking

Entropy
 Also see videos below
 Make sure to watch information theory videos first
 Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (video)

Cryptography
 Also see videos below
 Make sure to watch information theory videos first
 Khan Academy Series
 Cryptography: Hash Functions
 Cryptography: Encryption

Compression
 Make sure to watch information theory videos first
 Computerphile (videos):
 Compressor Head videos
 (optional) Google Developers Live: GZIP is not enough!

Computer Security

Garbage collection

Parallel Programming

Messaging, Serialization, and Queueing Systems

A*

Fast Fourier Transform

Bloom Filter
 Given a Bloom filter with m bits and k hashing functions, both insertion and membership testing are O(k)
 Bloom Filters (video)
 Bloom Filters  Mining of Massive Datasets  Stanford University (video)
 Tutorial
 How To Write A Bloom Filter App

HyperLogLog

LocalitySensitive Hashing
 Used to determine the similarity of documents
 The opposite of MD5 or SHA which are used to determine if 2 documents/strings are exactly the same
 Simhashing (hopefully) made simple

van Emde Boas Trees

Augmented Data Structures

Balanced search trees

Know at least one type of balanced binary tree (and know how it's implemented):

"Among balanced search trees, AVL and 2/3 trees are now passé, and redblack trees seem to be more popular. A particularly interesting selforganizing data structure is the splay tree, which uses rotations to move any accessed key to the root."  Skiena

Of these, I chose to implement a splay tree. From what I've read, you won't implement a balanced search tree in your interview. But I wanted exposure to coding one up and let's face it, splay trees are the bee's knees. I did read a lot of redblack tree code
 Splay tree: insert, search, delete functions
If you end up implementing red/black tree try just these:
 Search and insertion functions, skipping delete

I want to learn more about BTree since it's used so widely with very large data sets

AVL trees
 In practice: From what I can tell, these aren't used much in practice, but I could see where they would be: The AVL tree is another structure supporting O(log n) search, insertion, and removal. It is more rigidly balanced than red–black trees, leading to slower insertion and removal but faster retrieval. This makes it attractive for data structures that may be built once and loaded without reconstruction, such as language dictionaries (or program dictionaries, such as the opcodes of an assembler or interpreter)
 MIT AVL Trees / AVL Sort (video)
 AVL Trees (video)
 AVL Tree Implementation (video)
 Split And Merge

Splay trees
 In practice: Splay trees are typically used in the implementation of caches, memory allocators, routers, garbage collectors, data compression, ropes (replacement of string used for long text strings), in Windows NT (in the virtual memory, networking and file system code) etc
 CS 61B: Splay Trees (video)
 MIT Lecture: Splay Trees:
 Gets very mathy, but watch the last 10 minutes for sure.
 Video

Red/black trees
 These are a translation of a 23 tree (see below).
 In practice: Red–black trees offer worstcase guarantees for insertion time, deletion time, and search time. Not only does this make them valuable in timesensitive applications such as realtime applications, but it makes them valuable building blocks in other data structures which provide worstcase guarantees; for example, many data structures used in computational geometry can be based on red–black trees, and the Completely Fair Scheduler used in current Linux kernels uses red–black trees. In the version 8 of Java, the Collection HashMap has been modified such that instead of using a LinkedList to store identical elements with poor hashcodes, a RedBlack tree is used
 Aduni  Algorithms  Lecture 4 (link jumps to starting point) (video)
 Aduni  Algorithms  Lecture 5 (video)
 RedBlack Tree
 An Introduction To Binary Search And Red Black Tree

23 search trees
 In practice: 23 trees have faster inserts at the expense of slower searches (since height is more compared to AVL trees).
 You would use 23 tree very rarely because its implementation involves different types of nodes. Instead, people use Red Black trees.
 23Tree Intuition and Definition (video)
 Binary View of 23Tree
 23 Trees (student recitation) (video)

234 Trees (aka 24 trees)
 In practice: For every 24 tree, there are corresponding red–black trees with data elements in the same order. The insertion and deletion operations on 24 trees are also equivalent to colorflipping and rotations in red–black trees. This makes 24 trees an important tool for understanding the logic behind red–black trees, and this is why many introductory algorithm texts introduce 24 trees just before red–black trees, even though 24 trees are not often used in practice.
 CS 61B Lecture 26: Balanced Search Trees (video)
 Bottom Up 234Trees (video)
 Top Down 234Trees (video)

Nary (Kary, Mary) trees
 note: the N or K is the branching factor (max branches)
 binary trees are a 2ary tree, with branching factor = 2
 23 trees are 3ary
 KAry Tree

BTrees
 Fun fact: it's a mystery, but the B could stand for Boeing, Balanced, or Bayer (coinventor).
 In Practice: BTrees are widely used in databases. Most modern filesystems use Btrees (or Variants). In addition to its use in databases, the Btree is also used in filesystems to allow quick random access to an arbitrary block in a particular file. The basic problem is turning the file block i address into a disk block (or perhaps to a cylinderheadsector) address
 BTree
 BTree Datastructure
 Introduction to BTrees (video)
 BTree Definition and Insertion (video)
 BTree Deletion (video)
 MIT 6.851  Memory Hierarchy Models (video)  covers cacheoblivious BTrees, very interesting data structures  the first 37 minutes are very technical, may be skipped (B is block size, cache line size)


kD Trees
 Great for finding number of points in a rectangle or higher dimension object
 A good fit for knearest neighbors
 Kd Trees (video)
 kNN Kd tree algorithm (video)

Skip lists
 "These are somewhat of a cult data structure"  Skiena
 Randomization: Skip Lists (video)
 For animations and a little more detail

Network Flows

Disjoint Sets & Union Find

Math for Fast Processing

Treap
 Combination of a binary search tree and a heap
 Treap
 Data Structures: Treaps explained (video)
 Applications in set operations

Linear Programming (videos)

Geometry, Convex hull (videos)

Discrete math

Machine Learning
 Why ML?
 Google's Cloud Machine learning tools (video)
 Google Developers' Machine Learning Recipes (Scikit Learn & Tensorflow) (video)
 Tensorflow (video)
 Tensorflow Tutorials
 Practical Guide to implementing Neural Networks in Python (using Theano)
 Courses:
 Great starter course: Machine Learning  videos only  see videos 1218 for a review of linear algebra (14 and 15 are duplicates)
 Neural Networks for Machine Learning
 Google's Deep Learning Nanodegree
 Google/Kaggle Machine Learning Engineer Nanodegree
 SelfDriving Car Engineer Nanodegree
 Metis Online Course ($99 for 2 months)
 Resources:
Additional Detail on Some Subjects
I added these to reinforce some ideas already presented above, but didn't want to include them
above because it's just too much. It's easy to overdo it on a subject.
You want to get hired in this century, right?

SOLID
 Bob Martin SOLID Principles of Object Oriented and Agile Design (video)
 S  Single Responsibility Principle  Single responsibility to each Object
 O  Open/Closed Principle  On production level Objects are ready for extension but not for modification
 L  Liskov Substitution Principle  Base Class and Derived class follow ‘IS A’ Principle
 I  Interface segregation principle  clients should not be forced to implement interfaces they don't use
 D Dependency Inversion principle  Reduce the dependency In composition of objects.

UnionFind

More Dynamic Programming (videos)
 6.006: Dynamic Programming I: Fibonacci, Shortest Paths
 6.006: Dynamic Programming II: Text Justification, Blackjack
 6.006: DP III: Parenthesization, Edit Distance, Knapsack
 6.006: DP IV: Guitar Fingering, Tetris, Super Mario Bros.
 6.046: Dynamic Programming & Advanced DP
 6.046: Dynamic Programming: AllPairs Shortest Paths
 6.046: Dynamic Programming (student recitation)

Advanced Graph Processing (videos)

MIT Probability (mathy, and go slowly, which is good for mathy things) (videos):

String Matching
 RabinKarp (videos):
 KnuthMorrisPratt (KMP):
 Boyer–Moore string search algorithm
 Coursera: Algorithms on Strings
 starts off great, but by the time it gets past KMP it gets more complicated than it needs to be
 nice explanation of tries
 can be skipped

Sorting
 Stanford lectures on sorting:
 Shai Simonson, Aduni.org:
 Steven Skiena lectures on sorting:
Video Series
Sit back and enjoy.

List of individual Dynamic Programming problems (each is short)

Excellent  MIT Calculus Revisited: Single Variable Calculus

CSE373  Analysis of Algorithms (25 videos)

UC Berkeley CS 152: Computer Architecture and Engineering (20 videos) 
Carnegie Mellon  Computer Architecture Lectures (39 videos)

MIT 6.042J: Mathematics for Computer Science, Fall 2010 (25 videos)

MIT 6.050J: Information and Entropy, Spring 2008 (19 videos)
Computer Science Courses
Algorithms implementation
Papers
 Love classic papers?
 1978: Communicating Sequential Processes
 2003: The Google File System
 replaced by Colossus in 2012
 2004: MapReduce: Simplified Data Processing on Large Clusters
 mostly replaced by Cloud Dataflow?
 2006: Bigtable: A Distributed Storage System for Structured Data
 2006: The Chubby Lock Service for LooselyCoupled Distributed Systems
 2007: Dynamo: Amazon’s Highly Available Keyvalue Store
 The Dynamo paper kicked off the NoSQL revolution
 2007: What Every Programmer Should Know About Memory (very long, and the author encourages skipping of some sections)
 2012: AddressSanitizer: A Fast Address Sanity Checker:
 2013: Spanner: Google’s GloballyDistributed Database:
 2014: Machine Learning: The HighInterest Credit Card of Technical Debt
 2015: Continuous Pipelines at Google
 2015: HighAvailability at Massive Scale: Building Google’s Data Infrastructure for Ads
 2015: TensorFlow: LargeScale Machine Learning on Heterogeneous Distributed Systems
 2015: How Developers Search for Code: A Case Study
 More papers: 1,000 papers