CS540
Language Processors and Programming Environments (Fall 2009)

Meeting Time and Location:
Thursday 4:30-7:10pm, Innovation Hall 208

Instructor: Prof. Yutao Zhong.
Email: yzhong (at) cs (dot) gmu (dot) edu
Office: Engineering Building 5328
Office hour: Wednesday 5:30pm -6:30pm, or by appointment

Class Website: http://cs.gmu.edu/~yzhong/cs540_f09/

Overview
This course will cover the theoretical and implementation aspects of language processing. Emphasis will be on the techniques and the issues that arise in the design and construction of compilers. There are several substantial programming assignments associated with this course.  Main topics of the course include:

• Compiler Design
• Lexical Analysis
• Syntax Analysis - grammars, LL(1) parsers, LR(1) parsers
• Semantic Processing
• Code generation and optimization

Prerequisites: MATH 125, CS 265/365/367, CS 310 and 330
Students are expected to be acquainted with formal models and languages(CS330), computer structures and assembly languages(CS2/365 or CS367), and data structures(CS310).  Students should have strong programming skills in C/C++/Java.

Textbook

• Compilers: Principles, Techniques and Tools, Aho, Lam, Sethi, and Ullman (aka Dragon book), 2/E,  Addison-Wesley, August, 2006.  Errata Page.

Other Useful Resources

• lex & yacc, Levine, Mason, and Brown, published by O'Reilly, 2nd Edition October 1992.  Errata Page by the publisher.
• Engineering a Compiler, Cooper and Torczon, published by Morgan Kaufmann, an imprint of Elsevier Science, October 2003. Errata Page by the authors.
• Course website of a previous version of this class by Prof. E. White.

Grading Policies

• Tentative plans call for 3 to 4 programming assignments, together worth 50% of your grade.  These are to be individual efforts, meaning no sharing of code allowed with anyone but the instructor or the TA.
• There will also be in-class exercises and/or quizzes, an in-class midterm exam, and a final exam. End-of-semester numeric scores will be weighted as follows:
  • 55% Exercises
  • 50% programming assignments
  • 5% quizzes and/or in-class exercises
  • 45% Exams
    • 20% midterm
    • 25% final
• Both the final and midterm are closed-book and closed-notes. Missed exams must be arranged with the instructor BEFORE the exam.  You must have a written excuse (doctor's note, for example) to miss an exam.   No early exams will be given and make-up exams are strongly discouraged.  I reserve the right to give oral makeup exams in lieu of written.
• Letter grades will be assigned by subjectively identifying brackets in the numeric scores.  A total score of 49 or less will result in F.

Late Policy

• Every student has a total of THREE(3) late days for grace periods of the entire semester.
• You may use these late days on a single assignment or spread over multiple assignments. Use them wisely.
• No partial late day is in effect. You need one whole late day to cover a one-hour-late submission.
• Late submissions will not be graded if you have used up all of your late days.
• You must explicitly indicate that you want to use a late day in your submission or via an email. Otherwise late submissions will not be graded.
• You may not give your extra late days to another student; you don't get any extra credit for having late days left at the end of the semester.

Extra Credit
There may be opportunities for extra credit on some of the projects or exams.  To receive any extra credit for a project, all base functionality of a project must be implemented; extra credit portions of a project can not count in place of required features.  Extra extensions other than your late days will not be given for completion of extra credit.

Honor Code
You are expected to abide by the honor code.  All assignments and exams are individual efforts. Collaboration on any programming assignment is unacceptable.  Please refer to GMU Honor Code and Computer Science Department Honor Code.  Any violation of the honor code will result in a zero of the assignment/exam, and may result in an F for the course.

I will probably be using similarity detection software such as MOSS to detect plagiarism in programming assignments.