(Don't read this page. It is a work in progress for a Fall'19 graduate automated SE subject at NC State. Come back in mid-October!)

Start 1.preface 2.why se 4 ai? 3.tools 4.ethics: how

Tools baselines Data mining: discretization basic advanced Optimizers: landscapes basic advanced optimizing+data mining Theorem provers: basic advanced

Process requirements collect cleanse label train eval deploy monitor

Code config tests

Exercises 1 2 3a 3b 3c 3d 4

Quiz2: SI and AI

• Quiz2: SI and AI

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Quiz2: SI and AI

Find papers in the recent SE literature that use:

• TERMS= “software engineering and data mining”
• TERMS= “software engineering and optimization”
• TERMS= “software engineering and prover”

Method1: see if you can find your term in this paper. If so, use references from this papers.

Method2:

• For TERM in TERMS, go to Google Scholar Advanced Search
  • For VENUE in one of {ICSE, FSE, ASE, “transactions software engineering”, “Empirical Software Engineering”, “Journal of Systems and Software”, “Information Software Technologies”} do
    • Set “Return articles published in” to VENUE
    • Set “with all at least one the words” to TERM
    • Set “with the exact phrase” set to “software engineering” to TERM
    • Set “Return articles dated between” to the last ten years
    • Find a paper that uses TERM in an interesting way. Hint: only read paper with “enough” citations citations (for the last two years, “enough”=0; for anything before that, then “enough” for data mining =12 cites/year, “enough” for optimization is 6 cites/year, and for prover, 10 cites ever )
    • Sumamrize the paper under the following headings (usually, 2-3 lines each).

Note that most of the time for this exercise will be spent reading N dull papers before you find one you can read, and that your like.

Headings:

• Your name():
• Term:
• Write a ten line summary of what this does? Input? Output? Processing? Indications for when to use it? Not use it?
• Paper Title:
• Year:
• Venue:
• Authors:
• URL for download (if you can find it):
• Requirements (if found): what was the goal of the work? anything declared out of scope?
• Data collection (if mentioned): how was it done?
• Data cleaning (if done): any sanity checks on the data? repairs to strange fields?
• Data labelling (if needed): any need to assign values to the data before anything else (e.g. “good project”, “this is a bad poject”). How was it done? How were those labels checked? What was the effort in that labelling process?
• Training (if done): how was it done? Anything mentioned about issues with training? (e.g. long CPU times)
• Evaluation (if done): what metrics were applied (define each one)? how were the results summarized (list statistical tests and visualizations used). If cool visualizations, include them here.
• Deployment (if done): how was this scaled up? Delivered to many users?
• Monitoring (if done): if deployed, how did the developers watch over this development?

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