Skip to content

Latest commit

 

History

History
259 lines (199 loc) · 10.7 KB

SYLLABUS.md

File metadata and controls

259 lines (199 loc) · 10.7 KB
Raw


At a glance... | Syllabus | Models | Code | Lecturer

Syllabus

CSC 591-001 (13007)
CSC 791-001 (7046)

NcState, ComSci Fall 2015

EE I, Roon 1007, Tuesday, Thursday, 5:20 to 6:35.

Overview

Synopsis: What is the next "big thing" after "big data"? Well, after "data collection" comes "model construction" so the next big thing after big data will be "big modeling". In this subject, students will learn how to represent, execute, and reason about models. Our case studies will come from software engineering but the principles of this subject could be applied to models in general.

Objectives: By the end of the course, students should be able to:

  • Read and understand the state of the art in research on Automated Software Engineering
  • Analyze and critique core principles of software engineering.
  • Build models that execute those core principles.
  • Predict and explain and optimize the behavior of those models.
  • Build and evaluate SBSE tools.
  • Analyze, critique, and communicate clearly the core theory and algorithms of multi-objective optimization

Lecturer: Tim Menzies

  • Office Hours: Thursday, 3:30-5:00 and by request
  • Location of Office Hours: EE II room 3298
  • E-Mail: tim.menzies@gmail.com
    • Only use this email for private matters. All other class communication should be via the class news group, listed below.
  • Phone: 304-376-2859 + Do not use this number, except in the most dire of circumstances (best way to contact me is via email).

GTA: Rahul Krishna

  • E-mail: rkrish11@ncsu.edu
    • Only use this email for private matters. All other class communication should be via the class news group, listed below.
  • Office mours: Friday 12pm to 2pm
  • Office location: rm 3231, EE II

Group mailing list: During term time, a group mailing list will be established:

Topics: Overview, state of the research, automated software engineering; AI and software engineering; principles of model-based reasoning with a heavy focus on models about software engineering; search-based and evolutionary inference; representing and reasoning about models; handling uncertainty; decision making and model-based reasoning.

Project: Students will implement and reason about a large model of their own choosing (ideally, some model relating to software engineering). Note that:

  • CSC 791 Ph.d. student will each develop a large model-based SE application.
  • CSC 591 masters students will work in groups of three and may either do a large SE model-based app or three not-so-small mini-projects.

Prerequisite: Note that this is a programming-intensive subject. A programming background is required in a contemporary language such as Java or C/C++ or Python. Hence,he prerequisite for this class is 510, Software Engineering. Significant software industry experience may be substituted, at the instructor’s discretion. Students in this class will work in Python, but no background knowledge of that language will be assumed.

Suggested texts: (optional) Think Python: How to Think Like a Computer Scientist

  • Note: for low-level systems reasons, we will use Python 2.7 for this subject. So do not get the absolute latest version of this book.

Expected Workload: Some lectures will be pre-recorded, then discussed in the class room. It is the responsibility of each student to:

  • View those recordings;
  • Come to class with notes on
    • The concepts in those lecturers;
    • Plus their questions that arise from that material.

Sometimes, the lecturer/tutor will require you to attend a review session during their consultation time. There, students may be asked to review code, concepts, or comment on the structure of the course. Those sessions are mandatory and failure to attend will result in marks being deducted.

Also, this is tools-based subject and it is required that students learn and use those tools (Python, repositories, etc). Students MUST be prepared to dedicate AT LEAST 5-8 working hours a week to this class (excluding the time spent in the classroom). Laboratory instruction is not included in this subject (but the first three weeks will be spent on some in-depth programming tutorials). Note that the workload for masters and Ph.D. students will be different (see above).

Grading: The following grade scale will be used:

  • A+ (97-100), A (93-96), A-(90-92)
  • B+ (87-89), B (83-86), B-(80-82)
  • C+ (77-79), C (73-76), C-(70-72)
  • D+ (67-69), D (63-66), D-(60-62)
  • F (below 60).

Grades will be added together using:

  • Homeworks: 18
  • Mid-term/final exam: 22/25
  • Paper (on the ASE literature): 15
  • Big programming project (on model-based SE): 20

Homeworks

  • All deliverables are group-based (one deliverable per group)
    • 591 students: groups of three
    • 791 students: groups of one
  • Homeworks will be written into a public Github repo which students will create.
  • Students will shorten the url (using something like goo.gl) of the the main file of each homework submission
  • All homeworks will be marked 1'', or 0'';
  • Students cannot do homework i+1 till homework i gets at least a ``1''.
  • Homeworks can be submitted multiple times
    • No late penalties
    • No points taken off for repeat submissions
  • We will not mark more than four (coding) homeworks plus four (lit review) homeworks per month.

Attendance

Attendance is extremely important for your learning experience in this class. Once you reach three unexcused absences, each additional absence will reduce your attendance grade by 10%.

Academic Integrity

Cheating will be punished to the full extent permitted. Cheating includes plagerism of other people's work. All students will be working on public code repositories and informed reuse is encouraged where someone else's product is:

  • Imported and clearly acknowledged (as to where it came from);
  • The imported project is understood, and
  • The imported project is significantly extended.

Students are encouraged to read each others code and repor uninformed reuse to the lecturer. The issue will be explored and, if uncovered, cheating will be reported to the university and marks will be deducted if the person who is doing the reuse:

  • Does not acknowledge the source of the product;
  • Does not exhibit comprehension of the product when asked about it;
  • Does not significantly extend the product.

All students are expected to maintain traditional standards of academic integrity by giving proper credit for all work. All suspected cases of academic dishonesty will be aggressively pursued. You should be aware of the University policy on academic integrity found in the Code of Student Conduct.

The exams will be done individually. Academic integrity is important. Do not work together on the exams: cheating on either will be punished to the full extent permitted.

Disabilities

Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students at 1900 Student Health Center, Campus Box 7509, 919-515-7653. For more information on NC State's policy on working with students with disabilities, please see the Academic Accommodations for Students with Disabilities Regulation(REG 02.20.01).

Students are responsible for reviewing the PRRs which pertain to their course rights and responsibilities. These include: http://policies.ncsu.edu/policy/pol-04-25-05 (Equal Opportunity and Non-Discrimination Policy Statement), http://oied.ncsu.edu/oied/policies.php (Office for Institutional Equity and Diversity),http://policies.ncsu.edu/policy/pol-11-35-01 (Code of Student Conduct), and http://policies.ncsu.edu/regulation/reg-02-50-03 (Grades and Grade Point Average).

Non-Discrimination Policy

NC State University provides equality of opportunity in education and employment for all students and employees. Accordingly, NC State affirms its commitment to maintain a work environment for all employees and an academic environment for all students that is free from all forms of discrimination. Discrimination based on race, color, religion, creed, sex, national origin, age, disability, veteran status, or sexual orientation is a violation of state and federal law and/or NC State University policy and will not be tolerated. Harassment of any person (either in the form of quid pro quo or creation of a hostile environment) based on race, color, religion, creed, sex, national origin, age, disability, veteran status, or sexual orientation also is a violation of state and federal law and/or NC State University policy and will not be tolerated.

  • Note that, as a lecturer, I am legally required to report all such acts to the campus policy.

Retaliation against any person who complains about discrimination is also prohibited. NC State's policies and regulations covering discrimination, harassment, and retaliation may be accessed at http://policies.ncsu.edu/policy/pol-04-25-05 or http://www.ncsu.edu/equal_op/. Any person who feels that he or she has been the subject of prohibited discrimination, harassment, or retaliation should contact the Office for Equal Opportunity (OEO) at 919-515-3148.

Other Information

Non-scheduled class time for field trips or out-of-class activities are NOT required for this class. No such trips are currently planned. However, if they do happen then students are required to purchase liability insurance. For more information, see http://www2.acs.ncsu.edu/insurance/

Copyright © 2015 Tim Menzies. This is free and unencumbered software released into the public domain.
For more details, see the license.