We will be periodically hosting what we call hackathons, where we systematically look through software engineering conferences and look for papers with associated datasets. If you are interested in joining one, either remotely with Google Hangouts or in person in Engineering Building II at North Carolina State University, fill out this Google interest form and check out the instructions on how to help during the hackathon. The next hackathon has not yet been scheduled. Check back later for updates.

Interested in participating in a hackathon? Click here to sign up!

We will be periodically hosting hackathons to track down and add SE data and papers. The next hackathon has not yet been scheduled. Check back later for updates.

Keep in mind that, in most hackathons, we will only be concerned with steps 3 through 5.

When working on a specific issue, it is vital that you assign yourself to that issue so that others are not replicating your work.

How to fish for software engineering data

1. Browse conferences

Go find a recent SE conference with papers that might have data related to empirical SE experiments.
Create one GitHub issue for each conference, and add the “Conference” label to each
- Leave a comment with a hyperlink to the conference

2. Chunk each conference

Divide the conference into chunks based on its table of contents
Create one GitHub issue for each chunk, and add the “Chunk” label to each
- Leave a comment with the page numbers relevant to the chunk, and include the name of all the papers in the chunk.

3. Find each paper that maybe has data

For each chunk assigned to you, search through the papers, searching for those that may have downloadable data associated with them
Create one GitHub issue for each paper that may have data, and add the “Maybe has data” label to each
- Add each issue to the “Content retrieval hangout” milestone
- Leave a comment with a hyperlink to the specific paper from the conference

4. Filter for target papers

For each “Maybe has data” paper, check if there is actually data to download. In some cases, there will be many downloads available. Probably only one of them is important. Figure out which download is important. You might have to email the paper owner.
- If the paper has no downloadable data:
  - Remove the “Maybe has data” label, add the “No data” label, and close the issue.
- If the paper’s data is data that is already in Terapromise:
  - Remove the “Maybe has data” label, add the “Already have dataset” label, and close the issue.
- If the paper does have downloadable data:
  - If, after downloading all relevant files and compressing it all together, the size of the compressed file is smaller than 100 MB:
    - Leave a comment with a hyperlink to the relevant data downloads. You’ll need to spend some time figuring out the appropriate links.
    - Remove the “Maybe has data” label, and add the “Target paper” label.
  - If the compressed file is larger than 100 MB but smaller than 500 MB:
    - Remove label “Maybe has data” and add label “Maybe too big”.
  - If the compressed file is larger than 500 MB:
    - Remove label “Maybe has data”, add label “Definitely too big”, and close the issue.

5. Summarize the data

Create context notes for the data and add them as a comment to the GitHub issue.
Remove the “Target paper” tag and add the “Summarized” tag.

6. Submit the job

Carter Pape and Mitch Rees-Jones will then add the data to the big repo, add the context notes to our small repo, and email the owners with the url for both.
Relabel the issue to “Submitted” and close the issue.

If you are a researcher emailing us your data, please include the following sections of information about your data/research in the email.

If you are participating in a hackathon, please comment on the GitHub issue associated with the data, including the following sections of information in your comment.

Context notes

1. Data source

Example: paper title, if this data is closely related to some original paper.

2. Link to the material associated with the dataset (if available)

Examples:
- A link to the ACM digital library (for example, https://dl.acm.org/citation.cfm?id=2635868.2635905)
- A link to the IEEE digital library (for example, https://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6982619)
- Any other documentation

3. Attribution list for the material

Include at least one contact email
Example: author list for a paper

4. BibTeX reference

NOTE: This will be how others will cite the data.
Example: BibTeX for the paper

5. Link to the datasets

If multiple files come with it, include links to all files, not just the parent file
- Red flags should go up if, after downloading all the data, it is larger than 100 MB (compressed).

6. PROMISE repo category (effort, requirements, model, defect, etc.)

Categorize the data into one of the categories in the navbar on the OpenScience website.
- If it doesn’t fit nicely into one of these categories, put “other” and propose a category name.

7. General overview of the data

Note that, if the data is closely related to a paper, this will not be the abstract of the paper.
If you can find an overview of the dataset in the author’s own words, just copy those words. If not, make your own overview. This should be at least a paragraph long.

Data Categories

Help us find datasets for tera-PROMISE