Proceedings Article10.1109/VLHCC.2017.8103446
Exploring exploratory programming
Mary Beth Kery,Brad A. Myers +1 more
- 01 Oct 2017
pp 25-29
111
TL;DR: This work provides an organized description of what exploratory programming has meant historically and a framework of four dimensions for studying exploratory Programming tasks: applications, required code quality, ease or difficulty of exploration, and the exploratory process.
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Abstract: In open-ended tasks where a program's behavior cannot be specified in advance, exploratory programming is a key practice in which programmers actively experiment with different possibilities using code. Exploratory programming is highly relevant today to a variety of professional and end-user programmer domains, including prototyping, learning through play, digital art, and data science. However, prior research has largely lacked clarity on what exploratory programming is, and what behaviors are characteristic of this practice. Drawing on this data and prior literature, we provide an organized description of what exploratory programming has meant historically and a framework of four dimensions for studying exploratory programming tasks: (1) applications, (2) required code quality, (3) ease or difficulty of exploration, and (4) the exploratory process. This provides a basis for better analyzing tool support for exploratory programming.
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References
Exploration and Exploitation in Organizational Learning
TL;DR: In this paper, the authors consider the relation between the exploration of new possibilities and the exploitation of old certainties in organizational learning and examine some complications in allocating resources between the two, particularly those introduced by the distribution of costs and benefits across time and space.
19.7K
•Proceedings Article
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek,Hugo Larochelle,Ryan P. Adams +2 more
- 03 Dec 2012
TL;DR: This work describes new algorithms that take into account the variable cost of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation and shows that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms.
•Journal Article
Exploratory data analysis
TL;DR: A description of some typical EDA procedures is given and some of the principles of EDA are discussed.
6.6K
Scratch: programming for all
Mitchel Resnick,John Maloney,Andrés Monroy-Hernández,Natalie Rusk,Evelyn Eastmond,Karen Brennan,Amon Millner,Eric Rosenbaum,Jay Silver,Brian Silverman,Yasmin B. Kafai +10 more
TL;DR: "Digital fluency" should mean designing, creating, and remixing, not just browsing, chatting, and interacting.
4K