The best way to prepare for your final is to:
The final will be made available on BrightSpace around noon on the Monday of finals week and must be submitted by 11:59pm on the Friday of finals week. You will have 120 minutes to do the exam. Choose any two-hour period in the window to take the exam. You must complete the exam in one sitting. All problems will be multiple choice, multiple select, or matching. Submit on time; the penalty for late submissions is 5 points per minute.
Expect 20-30 questions. There will be no nasty all-of-the-above or none-of-the-above options. There will, however, occasionally be questions for which one answer is solid and correct, while others may make true, or nearly-always-true, statements but do not actually fit 100% with the asked question.
The intent of the questions is not to trick you. The intent is to ensure that students that have gained a fairly deep understanding of computer science fundamentals (linguistic and theoretical concepts) without having to do a one-hour web search, will be rewarded with a higher grade. All students can be this kind of student. If you have read the materials (and there was indeed a ton of reading!) and have participated in the writing of your term project (a real compiler!), you are that kind of student.
As always, you MAY use books, notes, and web searches to look things up. You will not be spied on: there is no browser lock down and hence no need to hide a mobile device in a bag of potato chips. However, you MAY NOT solicit answers in any way. There is to be no asking for help, no posting on forums, no communication with other humans or chat bots in any way; you can only “look things up,” you may never “ask.” You also MAY NOT post answers or help any other test taker either. You are bound by an honor code to follow these rules.
Review the learning objectives from the syllabus now. If there is any unmet objective, let the instructor know. For reference, the objectives are repeated here:
There are several things that all recipients of a computer science degree are expected to know. Ideally, these would be verified by an oral exam with a pass/fail outcome. Don’t worry, such an oral exam is not feasible, and may even be subject to the examinee failing due to nerves, so you’re going to test yourself in the comfort of your own space. Make sure you know the following:
Do you know how to answer each and every question? Can you articulate good answers to each? If so, great! Congratulations! This is the minimal criteria for passing.
You learned some theory and some bits of knowledge. But hopefully, you got so much more. You should have:
Why did you write a compiler?
Back to the academic side of things.
Review the course notes covered during lectures.
Here is a rough outline of the course material.
THEORIES OF COMPUTER SCIENCE
What is a theory?
Why do we have theories?
Historical path to computer science as a discipline
The four major theories of computation and their concerns
Language Theory
Concerned with how computations are expressed
Automata Theory
Concerned with how computations are performed
Sneak peek: Turing Machines
The stunning notion of computational universality
Computation Theory
Concerned with what can and cannot be computed
Sneak peek: the halting problem is undecidable
Complexity Theory
Concerned with how efficiently computations can be performed
Sneak peek: P vs. NP
LANGUAGE THEORY
Concerned with how computations are expressed
Why study language theory?
Information representation
Formal Language Theory
Symbols, Alphabets, Strings, Languages
Operators on languages: Union, Intersection, Concatenation, Kleene Star
How to formally define a language?
Grammars
Role of variables
Grammar notation
How strings are generated
Lots of example grammars
Parse Trees (aka Derivation Trees)
Ambiguity
Formal Definition
Restrictions
CFG: LHS is only one variable
RLG: LHS is only one variable and RHS is symbols + at most one variable
Type-1: RHS never shrinks (only one exception if empty string is in the language)
Language Recognition
Automata can be used for this
Analytic Grammars
Language Classification
Chomsky Hierarchy for Formal Languages (original version)
Regular (Type 3)
Context-Free (Type 2)
Type-1 (aka ”Context sensitive”)
Unrestricted
Larger Chomsky Hierarchy
Finite
Regular
Context-Free
“Context-Sensitive”
Recursive
Recursively Enumerable (r.e.)
Finitely Describable
Programming Language Theory
How PLT differs from formal language theory
Concerns (Just a list for now)
Syntax
Semantics
Type Theory
Static Analysis
Translation
Runtime Systems
Verification
Metaprogramming
Classification
SYNTAX
Motivation (there is a structure underlying all programs)
Many ways to express this structure as a string
Definition of Syntax
Syntax Diagrams
Lexical vs. Phrase Syntax
Why this is massively important
Ways to represent the difference
Tokens
Parse Trees
The frontier of the parse tree is the token stream
Dealing with Ambiguity
Precedence (and how to capture it in a grammar)
Associativity (and how to capture it in a grammar)
Parsing (sneak peek only)
Hand-crafted, recursive descent
Parser generators
Analytic Grammars
PEGs
Ohm
The Problem of Context
Things you cannot capture in a context-free grammar, incomplete list:
No redeclare within scope
No use of possibly uninitialized variables
Type checking
Correct number of arguments must appear in a call
Access modifiers must be correct
All execution paths through a function must end in a return
All abstract methods must be implemented or declared abstract
All declared local variables must be used
All private methods in a class must be used
Is this stuff syntax or semantics?
People can disagree
Side note: can be formalized in theory but why bother
Type inference
Abstract Syntax
What ASTs look Like
Difference between CSTs (Parse trees) and ASTs
Tree grammars to formally define ASTs
Esprima
Examples in JavaScript
Examples in Java
Aside: Different syntax formalisms in the real world
LANGUAGE DESIGN
Things to know
Major features of existing programming languages
Historical Issues
What Bret Victor says about the 1960s and 1970s
What Alan Kay thinks
The process of language design
Big picture and big questions
Starter set of features
Design your abstract syntax
Sketch and Prototype with Ohm!!!
Start working on lower-level syntax
What kind of sugar do you want?
Differences between syntax, semantics, pragmatics
Ohm for language design
Ohm grammar notation
Ohm details
Examples of Ohm grammars
Case study: Astro
Case study: Bella
Case study: Carlos
COMPILERS
Translators vs. interpreters
Compilers, assemblers, transpilers
AOT vs. JIT
Overall structure of translation
Analysis -> Generation
Analysis -> Optimization -> Generation
Parsing -> Static Analysis -> Optimization -> Code Generation
Lexical Analysis
characters to tokens
Syntax Analysis = Parsing
Tokens to CSTs
Semantic Analysis = Static Analysis
CSTs to ASTs
But ASTs are not really trees
Type checking and other semantic analysis
Intermediate Representations
Why have them?
Sneak peek: later phases of the compiler
Control Flow Analysis
Data Flow Analysis
Optimization of decorated AST
Production of high-level language code
Production of abstract intermediate structures
Production of bytecode
Production of abstract assembly language
Machine independent optimization
Modern compilers are not just one-shot translators
How to architect a compiler using Ohm
parser.js
analyzer.js
Representing context
Checks, especially type checking
optimizer.js
generator.js
core.js
compiler.js
<your-language-name>.js
Tests for compiler, parser, analyzer, optimizer, generator
Why you should write a compiler
AUTOMATA THEORY
Concerned with how computations can be carried out
Broad classification of automata
Transducers vs Recognizers/Deciders
Tapes vs Registers
State Machines vs Instruction Lists
Harvard vs von Neumann Architecture
Turing Machines
How they work
Many Examples
Variations that neither restrict nor expand computing power
Multi-track
Multi-head
Multi-tape
Queue
Variations that restrict computing power
LBAs: Bounded tape
PDAs: Input is read-only, read left-to-right once, memory is a stack
FAs: Input is read-only, read left-to-right once, no memory
Register Machines
Counter machines
RAMs
Other “Automata-like” Formalisms
String rewriting systems
λ-Calculus
Brainf**k
Recursive Functions (not covered in class)
Applications to Intermediate Representations
Why have them?
Analysis/Synthesis is inherent to translation
Break down complex problem
Retargetability
For machine independent optimizations
High-level vs. Medium-level vs. Low-level
Styles
Abstract assembly language (instructions called tuples)
Stack code
List of well-known IRs
JVM
CLR
LLVM
SIL
CIL
Tuples
Applications to Virtual Machines and Real Machines
Machine Architecture
How machines work (review)
Intel 64 architecture
Review of x86-64 Assembly Language
Registers and instructions
Calling conventions
Parallel instructions
Code Generation
Goals
Translation to JavaScript
Translation to Assembly Language
Naïve
Interpretive
Code generator generators
Generation of real assembly language
Address assignment
Instruction selection
Register allocation
Low-level optimization
Understanding the runtime system for block-structured languages
Stack frames
Dynamic links
Static links
Register save area
Register spilling
PARSING THEORY
What is parsing?
Lexical vs Syntactic parsing
Regular expressions
In theory (type-3)
In practice
Common notation for Regexes in modern languages
( ) [ ] { } ^ $ . \ ? * + |
Uses: validation, search, extraction, replace
Groups
Quantifiers
Eager: * + ? {}
Reluctant: *? +? ?? {}?
Possessive: *+ ++ ?+ {}+
Backreferences \1 \2 ...
Anchors: ^ $ \A \Z \b \B
Lookarounds: ?= ?! ?<= ?<!
Performance concerns
Approaches to parsing
Top-down, LL, Expand-Match
Bottom-up, LR, Shift-Reduce
Recursive Descent
PEGs
Parsing in the Real world
COMPUTABILITY THEORY
Concerned with what can and cannot be computed
History: Hilbert, Gödel, Church, Turing
Bernhardt book
Wadler video
So many equivalent models, all Turing-complete, hence the Church-Turing Thesis
Of course there are uncomputable functions: Diagonalization
Halting Problem is undecidable
Limits
Non-computable functions = Non-recognizable languages
Non-decidable problems = Non-decidable languages
Reductions
Rice’s Theorem
Chomsky Hierarchy: The Full Version
Finite = S->a|b|c = Non-looping FAs
Regular = Right Linear Grammar = Finite Automata
Deterministic Context Free = LR = DPDA
Context Free = CFG = (N)PDA
Type-1 = Linear Bounded Automata
Decidable (Recursive) = Turing Machines that always Halt
Recognizable = r.e. = Turing Machines
Finitely Describable (no machine out here)
Beyond Finitely Describable 🤯
COMPLEXITY THEORY
Concerned with how expensive certain computations are
Time complexity
Space complexity
Theory
Big-O, Big-Theta, Big-Omega
Little-O, Little-Theta, Little-Omega
Asymptotic Notation
P vs. NP
NP-Completeness
The Complexity Zoo
Practice: Optimization in Compilers
Code Optimization
Machine independent vs. machine dependent
Constant folding
Strength reductions
Algebraic simplifications
Operand reordering
Unreachable code elimination
Dead code elimination
Copy propagation
CSE
Loop unrolling
Special purpose instructions
e.g. muladd, range, conditional jump
Loop invariant factoring
Tail recursion elimination
Induction variable simplification
Static frame allocation
Stack frame simplification
Low-level optimizations
Special instructions
Alignment
Cache
Removing conditional jumps
Scheduling to remove load delays and similar things
On the course practice page, do the reinforcement problems related to the portions of the course that the exam will cover.
Here are things you should be able to do before taking the final. Quiz yourself. Quiz each other.
Do the Practice Final on BrightSpace! It is not as long as the real final, but making sure you know how to use BrightSpace as a platform for taking tests will help make sure you don't have a bad day and get frustrated by the technology during the real exam.
You have to put in the time for effortful self-study. Although the exam is open resources, you will not have time to look everything up. Those who come in with a strong comfort level with the material will finish on time. I am assessing your fluency and your proficiency with the material, not your Google-Fu.
An education is a long-term life journey. Education goes way beyond your chosen field and way beyond academics in general. That said, there is much to be gained by immersing oneself in the history, theory, and practice, of computer science. Our culture is primarily literary, so to that end, you have been assigned a great deal of reading? Were you able to read or skim everything? I hope so, but if not, find time to catch up (or at least please consider catching up in the near future). Among the readings that will be helpful in your journey to becoming a computer scientist, review: