CS 7100 Advanced Programming Languages

Course Objectives

To provide a solid foundation for studying advanced topics in Programming Language Specification and Design.

Specifically, the student learning outcomes include:

• Demonstration of knowledge of programming language design.
• Creation and analysis of algebraic specification of abstract data types.
• Development of interpreter-based specification (operational semantics) of programming languages.
• Demonstration of knowledge of attribute grammar framework and axiomatic-basis for computer programming.
• Exploration of contemporary multi-paradigm languages  (time permitting)

Prerequisite CS 3180/5180 Comparative Languages

Course Description

This course introduces standard concepts related to the specification and design of high-level programming languages. It discusses different programming paradigms, algebraic specification and implementation of data types, abstract syntax trees, and develops interpreters in Racket (Scheme) for specifying (operationally) various programming language features/constructs (spanning simple expression language to class-based object-oriented language). It also introduces attribute grammar framework that is convenient for automatic translation and axiomatic semantics formalism that assists in program verification. The programming assignments will be coded using Racket IDE.

Course Load

The course load includes programming assignments, a midterm exam, and a final exam.

(1) Programming assignments must be submitted but will not be scored/graded. You may work in teams of two/three and share your work with team members to improve your understanding of the material. Do not plagiarize from online sources. 
(2) The exams will be conducted in-person in the class. No electronic device usage is allowed during the exams. The exams will provide helpful information and code snippets from the text book or course notes if necessary.
(3) The entire class is expected to take the exams simulaneously though different versions of the same exam may be given to minimize cheating. (Exceptions may be made only under extraordinary circumstances such as a documented medical need or family emergency.) 
(4) Any academic impropriety (which includes copying from other students or plagiarizing from online resources are prohibited) during an exam (or on a graded assigment) will have a minimum penalty of an 'F' grade, plus additional disciplinary action for unethical behavior. See http://www.wright.edu/students/judicial/integrity.html for details.

Tentatively, both the midterm exam and the final exam are expected to be weighted equally for determining the final grade. If this policy is modified for any reason, you will be informed about it in the class.

Reference

1. Friedman, Wand and Haynes: Essentials of Programming Languages. 2nd (preferred) or 3rd Edition. MIT Press, 2001. ISBN 0-262-06217-8  (code.zip)
2. R. Kent Dybvig: The Scheme Programming Language, 4th Edition. The MIT Press, 2009.

1. Guttag, J.V., "Abstract Data Types and the Development of Data Structures,"  CACM, vol. 20, No. 6, June 1977, pp. 396-404.
2. Chapter 1 of Guttag, J. V., et al, Larch: Languages and Tools for Formal Specification, Springer-Verlag, NY, 1993.
3. H. Abelson and G. J. Sussman, Structure and Interpretation of Computer Programs, 2nd Ed., MIT Press, 1996.
4. M. Felleisen, R. B. Findler, M. Flatt, and S. Krishnamurthi, How to Design Programs, 2nd Ed., MIT Press, 2018.
5. Scheme : Language Reference Manual
6. The Teaching About Programming Languages Project
7. Racket Download Site (http://racket-lang.org/)
8. K. Thirunarayan, G. Kniesel, and H. Hampapuram, Simulating Multiple Inheritance and Generics in Java, In : Computer Languages, Vol. 25(4), pp. 189-210, 2001.

Grading

The final letter grade are normally determined based on the cumulative score on the midterm exam (weighted 50%) and the final exam (weighted 50%) using the following scale: A[90-100], B[80-90), C[70-80), D[60-70), and F[0-60). However, I reserve the right to adjust the scale somewhat to utilize the gaps in the distribution for a small class. For a large class such as yours, I will describe my approach to fitting a Gaussian in a separate email. 

Applicable University Policy on Generative AI

You are not permitted to use generative AI tools for any work for this course. This includes the use of popular tools like ChatGPT, Midjourney, GitHub Co-Pilot, as well as all other tools built on generative AI technologies. Due to the nature of this course, the professor can only fairly and accurately evaluate work that is not assisted by generative AI.  Use of generative AI for assigned work in this course will be considered a violation of the university’s academic integrity policies. In general, if you have any questions about whether or not use of a particular tool or technology is allowed, check with your instructor first. (Also note that the output generated by these tools can be invalid (due to hallucination) and you will not be able to detect it without first knowing what the "correct" answer is, which is what this course is trying to provide.)

Tentative Syllabus, Class Schedule, and Linked Lecture Slide Decks

Assignments (Fall 2023)

1. Assignment 1 . (asg1.ppt)

2. Assignment 2 .
   

3. Assignment 3 
   

• Midterm (pdf).(pdf)
• Final (pdf).(pdf) (pdf).