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Fall 2016
CSC 591-007 (11636)
CSC 791-007 (6451)
EE I, Roon 1005, Tuesday, Thursday, 4:30 to 5:45pm
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: Wednesday, 3:00-5:00 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 Slack group.
- 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: George Mathew
- E-mail: george2@ncsu.edu
- Only use this email for private matters. All other class communication should be via the class news group, listed below.
- Office hours: 2-4 on Wednesdays and by appointment on other days.
- Office location: rm 3231, EE II
Group mailing list: During term time, all communication will be via the Slack group https://ase16.slack.com. . Students are strongly encouraged to contribute their questions and answers to that shared resource.
- Note that, for communication of a more private nature, contact the lecturer on the email shown above.
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 work alone 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: 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 : 24 = 8 * 3
- Mid-term/final exam: 10/21
- Paper (on the software science): 15
- Big project (on automated SE): 23 (essay) 7 (presentation)
The mid-term exam will be on Tues Oct 11 (1 hour, rest of class canceled).
The final exam will be held on Tuesday December 13 during class time check.
- 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 tiny.cc) of the the main file of each homework submission
- That url wull be pasted into the spreadsheet http://tiny.cc/ase16give
- Homeworks will be of two forms:
- Coding: worth 2 marks (and marked 0,1,2)
- Reading: with 1 mark
- Students cannot do homework i+1 till homework i get full marks.
- 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.
- Students must submit (or resubmit) at least one item per week (or lose 2 marks)
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%.
Except for officially allowed reasons, your presence in the class if required from day one. Late-comers will have to work in their own solo groups (to avoid disruptions to existing groups).
Note that absences for weddings (your own, or someone else's, is not an offically allowed reason).
Exceptions: this subject will support students who are absent for any of the following officially allowed reasons:
- Anticipated Absences (cleared with the instructor before the absence).
Examples of anticipated situations include
- representing an official university function, e.g., participating in a professional meeting, as part of a judging team, or athletic team;
- required court attendance as certified by the Clerk of Court;
- religious observances as verified by the Division of Academic and Student Affairs (DASA).
- Required military duty as certified by the student's commanding officer.
- Unanticipated Absences. Excuses must be reported to the instructor not more than one week after the return to class. Examples of unanticipated absences are: - Short-term illness or injury affecting the ability to attend or to be productive academically while in class, or that could jeopardize the health of the individual or the health of the classmates attending. Students must notify instructors prior to the class absence, if possible, that they are temporarily unable to attend class or complete assignments on time. - Death or serious illnesses in the family when documented appropriately. An attempt to verify deaths or serious illness will be made by the Division of Academic and Student Affairs.
That support will include changing the schedule of deliverables and/or (in extreme case) different grading arrangements.
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.
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).
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.
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/