Lexical Analysis (flex, hand-written DFA)
Lexical Analysis (flex, hand-written DFA) in Compiler Design: implementation patterns, named pitfalls, and the autograder cases that catch them.
Computer Science Foundations
Lexer and parser construction with flex plus bison or ANTLR, abstract syntax tree design, type checking with explicit inference rules, LLVM IR emission, and dataflow optimization passes. A common parser-lab failure is shift-reduce conflict resolution in a yacc grammar, the parsing collision our tutors fix with explicit precedence declarations. Verified CS graduates, starting at $20 per task, 12-hour average turnaround.
Why Compiler Design
Lexer and parser construction with flex plus bison or ANTLR, abstract syntax tree design, type checking with explicit inference rules, LLVM IR emission, and dataflow optimization passes. A common parser-lab failure is shift-reduce conflict resolution in a yacc grammar, the parsing collision our tutors fix with explicit precedence declarations. Verified CS graduates, starting at $20 per task, 12-hour average turnaround.
Topics covered
Lexical Analysis (flex, hand-written DFA) in Compiler Design: implementation patterns, named pitfalls, and the autograder cases that catch them.
Regular Expressions to NFA/DFA in Compiler Design: implementation patterns, named pitfalls, and the autograder cases that catch them.
Context-Free Grammars in Compiler Design: implementation patterns, named pitfalls, and the autograder cases that catch them.
LL(1) Recursive Descent in Compiler Design: implementation patterns, named pitfalls, and the autograder cases that catch them.
LR(1) and LALR(1) Parsing in Compiler Design: implementation patterns, named pitfalls, and the autograder cases that catch them.
Bison and ANTLR Parser Generators in Compiler Design: implementation patterns, named pitfalls, and the autograder cases that catch them.
Full overview
Compilers turn programmer-friendly source code into machine-executable output through a 6-stage pipeline. Compiler courses cover 8 named topic areas: lexical analysis (regular expressions to deterministic finite automata via Thompson construction plus subset construction, implemented with flex or hand-written DFA), syntax analysis (context-free grammars and parsing with LL recursive descent, LR shift-reduce, LALR via bison, or PEG via packrat parsing), semantic analysis (type checking with unification-based inference per Hindley-Milner, scope resolution with symbol tables, attribute grammars for context-sensitive checks), intermediate representation (three-address code, static single assignment form, LLVM IR), code generation (instruction selection with tree-pattern matching, register allocation via graph coloring or linear scan, instruction scheduling), optimization (constant folding, dead code elimination, loop-invariant code motion, common subexpression elimination, function inlining, vectorization), runtime systems (garbage collection with mark-sweep or generational, exception handling with table-based unwinding, dynamic dispatch tables), and target-specific concerns (calling conventions, ABI compliance, debug information in DWARF format). A typical compiler course spends 13 to 15 weeks on these topics with Aho-Lam-Sethi-Ullman (the Dragon Book) or Appel as the textbook.
Project-based courses ship a multi-stage project building a complete teaching-language compiler (Cool, Decaf, MiniJava, or Xi) in C++, Java, or OCaml. More ambitious sequences add a register allocator and 4 optimization labs targeting x86-64. The assessment landscape is 80-20 projects over written exams because compiler correctness requires implementation, and graders use extensive test suites of pathological inputs.
CSHH tutor matching for this subject draws from CS graduates with PL implementation depth: former LLVM contributors, GCC plugin developers, and Rust compiler contributors with direct lab experience. Our tutors deliver lexers with flex specifications passing the course test suite, parsers with explicit grammar conflict resolution (precedence declarations, %left and %right and %nonassoc directives), type checkers with explicit inference rules in the textbook notation, LLVM IR generators producing valid bitcode that opt can verify, and optimization passes implemented as LLVM ModulePass or FunctionPass subclasses with regression tests. Languages supported: C and C++ for traditional compiler implementations, Java for academic-style compilers, Python for scripting and prototyping, OCaml for type-system implementations (the canonical PL research language).
Where Students Get Stuck
Thompson construction builds an epsilon-NFA from a regex. Subset construction converts the NFA to a DFA with state-set tracking. Hopcroft minimization reduces the DFA to canonical form. flex automates all 3 steps. We trace each conversion on a worked example (e.g., (a|b)*abb regex) showing the NFA, the subset-derived DFA, and the minimized form.
LL(1) recursive descent is simple to implement and debug but cannot handle left-recursive grammars (infinite recursion). LALR via bison handles left-recursion but produces shift-reduce conflicts on ambiguous grammars. We pick LL(1) for languages with predominantly right-recursive grammars (function-call-style syntax), LALR for languages with left-recursive expression grammars.
The classic dangling-else ambiguity (if-then-else vs if-then) produces a shift-reduce conflict in bison. Resolution: %right ELSE makes else bind to the nearest if (the C semantics). Operator precedence conflicts resolved with %left, %right, %nonassoc, and explicit precedence levels. We trace each conflict in the bison verbose output and apply the correct directive.
Visitor pattern in Java keeps node classes free of operation code but requires double dispatch boilerplate. Sum types in OCaml or Haskell give exhaustive pattern matching but require recompilation when adding nodes. Class hierarchy with virtual methods in C++ is straightforward but couples nodes to operations. We pick based on the language and the expected pattern of changes (more node types vs more operations).
A stack of scope dictionaries pushed on block entry and popped on exit. Lookup walks the stack from innermost to outermost. Definition writes to the topmost scope. Class inheritance adds a class-table-chain layer between the instance scope and the enclosing function scope. We implement with explicit push and pop operations matching the AST traversal.
Algorithm W unifies type variables during a single AST pass. Let-polymorphism generalizes free type variables in the let-bound expression but not in lambda parameters. The occurs-check prevents infinite types (e.g., t1 unified with list of t1). We implement unification with union-find for efficiency and explicit generalization at let bindings.
Assignment Types
flex specifications and hand-written DFAs that apply the maximal-munch rule with rule-order tie-breaking. Named pitfall: executing an NFA directly without subset construction, which matches the wrong longest prefix on ambiguous input.
LL(1) recursive descent and LALR bison parsers with conflict resolution via precedence declarations. Named pitfall: feeding a left-recursive grammar to a recursive-descent parser, which recurses infinitely instead of parsing.
bison grammars debugged through the verbose conflict report with %left, %right, and %nonassoc directives. Named pitfall: the dangling-else ambiguity left unresolved, so else binds to the wrong if without an explicit %right ELSE.
AST representations (visitor pattern, sum types, or class hierarchy) with stack-based lexical scoping. Named pitfall: a single-pass symbol resolver that cannot handle forward references, which a two-pass approach fixes.
Type checkers written in judgment notation with Hindley-Milner unification and let-polymorphism. Named pitfall: skipping the occurs-check, which lets a type variable unify with a type containing itself and produces an infinite type.
SSA-form IR emitted via the IRBuilder API with basic blocks per control-flow construct and phi nodes at merges. Named pitfall: violating the single-definition invariant in loop bodies, which fails verification before optimization runs.
Dataflow passes, graph-coloring register allocation, and peephole optimizations with before-and-after IR. Named pitfall: spill code that itself consumes registers, so a naive allocator runs out of colors mid-spill.
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