Blackbox Mini - Getting Started With Blackbox Data Analysis
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Abstract
The Blackbox project collects programming activity data from users of BlueJ, a Java IDE aimed at novices. The Blackbox data set has grown very large, with several terabytes of source code data. This is a double-edged sword; it provides large amounts of data for analysis, but it can be difficult for newcomer researchers to get started with analysis. In this workshop we will introduce attendees to analysing the data by using Blackbox Mini: a new curated subset of the Blackbox data set, designed to help researchers try out their source code analyses on a smaller data set.
The workshop will be run by the designers and maintainers of the Blackbox (and Blackbox Mini) data sets. Attendees at the workshop will learn how to work with the Blackbox Mini data set, including basic source code analysis. The Blackbox Mini data set can provide useful publishable research results itself, or the analyses can be carried over and run on larger subsets of the full Blackbox data set.
Index Terms
- Blackbox Mini - Getting Started With Blackbox Data Analysis
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Information & Contributors
Information
Published In
February 2020
1502 pages
ISBN:9781450367936
DOI:10.1145/3328778
- General Chairs:
- Jian Zhang,
- Mark Sherriff,
- Program Chairs:
- Sarah Heckman,
- Pamela Cutter,
- Alvaro Monge
Copyright © 2020 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 February 2020
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SIGCSE '20
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SIGCSE '20: The 51st ACM Technical Symposium on Computer Science Education
March 11 - 14, 2020
OR, Portland, USA
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Overall Acceptance Rate 1,595 of 4,542 submissions, 35%
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SIGCSE Virtual 2024
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