Syllabus

Foundations of software science
NCSU, CS, Fall 2018

CSC 591-023 (16403)
CSC 791-023 (17051)
Tues/Thurs 4:30 to 5:45
EE III, Room 2232

Synopsis

Most software companies now learn their policies via data-driven methods. Modern practitioners treat every planned feature as an experiment, of which only a few are expected to survive. Key performance metrics are carefully monitored and analyzed to judge the progress of a feature. Even simple design decisions such as the color of a link are chosen by the outcome of software experiments.

This subject will explore methods for designing data collection experiments; collecting that data; exploring that data; then presenting that data in such a way to support business-level decision making for software projects.

Objectives

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

• Build and modify and refactor and remix and repurpose AI software (data miners, optimizers, theorem provers);
• Report on complex technical issues (spoken talk)
• Report on complex technical issues (written talk)
• Discuss issues associated with applying the above to help society, and customers, achieve their goals better, faster, and cheaper.

Staff

Lecturer

Tim Menzies (Prof)

• Office Hours: Tuesday, 2:00-4:00 and by request
• Location of Office Hours: EE II room 3298
• Slack name: timm
• E-Mail: timm@ieee.org
• Only use this email for private matters. All other class communication should be via the class Slack group http://found18.slack.com.
• Phone: 304-376-2859
  • Do not use this number, except in the most dire of circumstances (best way to contact me is via email).

Teaching assistant

TBD

• Office Hours: TBD
• Location of Office Hours: TBD
• Slack name: TBD

Group mailing list

During term time, all communication will be via the Slack group https://found18.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.

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

none

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).

Grades

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:

• Weekly solo homeworks (till October): 8 marks (8 homeworks 1 mark each)
  • Homeworks can be resubmitted till you get full marks on them. But:
    • No homeworks will be accepted after October 15.
    • Once you submit homework i+1, your marks for homework 1..i will freeze.
    • Only 2 homeworks (max) will be marked per student per week (this includes resubmissions)
    • You lose 0.5 marks for any week where you do not submit a homework (including resubmissions)
• Mid-term Oct 23 (on terminology): 22 marks
• Solo poster (on current directions in foundations of software science): 10 marks
  • 2 page paper
    • Posters are 2 pages pdf in ACM format
    • Sample posters can be found here
    • Topic: Posters will explain some interesting aspect on some paper on some work NOT authored by Menzies. Something on data mining and/or search-based SE and/or thorem proving as applied to some SE task
    • To find examples of that work, see papers after 2012 from top SE-venues
      • Google scholar, top SE-venues
      • In particular, ICSE, TSE, JSS, IST, MSR, ESE, ASE, TOSEM, ICSM (now ICSME)
    • Poster may burrow definitions and graphics from the paper they are reviewing. BUT. Anything the student must be able to explain in further detail anything they put into their poster.
  • Presentation (5 mins talk, 5 mins questions) 10 marks
    • No slides
    • Instead, place your 2 page paper under the podium camera, then talk to the content.
    • IMPORTANT : Prior to class, post your paper to the submit site. Otherwise, lose 1 mark.
• Big project (due end November) on automated SE): 25 (essay) 15 (presentation) 10 (code review)
  • Project must present two implementations
    • An initial implementation which you will critize using our baseline criteria
    • A second implementation where the you strive to improve that implementation, according to any of the baseline criteria.
    • Note that merely doing some existing standard data mining project will NOT be sufficcient
  • Paper are 8+ pages pdf in ACM format
  • The code review will be the lecturer reading the code checking that the implementation is above a minimum level of effort.
    • Lecturer may call students/ teams at any time to his office for such reviews.
  • Presentations will start mid-November
  • Paper is not due till Dec 1.

Note that:

• Master students will do the big project in groups of 3. All other work is solo.
• Submissions will be posted to the submit site. Take long urls and shorten them with (e.g.) tiny.cc before psiting.

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%.

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.

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/