Exploring exploratory programming

TL;DR: In the creative coding example, the coder might explore several options using different combinations of colors, shapes, textures, and animation speeds.

Abstract: Data scientists typically practice exploratory programming using computational notebooks, to comprehend new data and extract insights. To do this they iteratively refine their code, actively trying to re-use and re-purpose solutions created by other data scientists, in real time. However, recent studies have shown that a vast majority of publicly available notebooks cannot be executed out of the box. One of the prominent reasons is the deprecation of data science APIs used in such notebooks, due to the rapid evolution of data science libraries. In this work we propose RELANCER, an automatic technique that restores the executability of broken Jupyter Notebooks, in near real time, by upgrading deprecated APIs. Relancer employs an iterative runtime-error-driven approach to identify and fix one API issue at a time. This is supported by a machine-learned model which uses the runtime error message to predict the kind of API repair needed - an update in the API or package name, a parameter, or a parameter value. Then Relancer creates a search space of candidate repairs by combining knowledge from API migration examples on GitHub as well as the API documentation and employs a second machine-learned model to rank this space of candidate mappings. An evaluation of Relancer on a curated dataset of 255 un-executable Jupyter Notebooks from Kaggle shows that RELANCER can successfully restore the executability of 56% of the subjects, while baselines relying on just GitHub examples and just API documentation can only fix 38% and 36% of the subjects respectively. Further, pursuant to its real-time use case, Relancer can restore execution to 49% of subjects, within a 5 minute time limit, while a baseline lacking its machine learning models can only fix 24%.

TL;DR: Branching in CAD is still in its early stages, but has the potential to revolutionize hardware product development. This paper explores user perspectives on branching in CAD and identifies opportunities for improvement.

TL;DR: In this paper, a hierarchical attention mechanism was proposed to consider the relevant code cells and code tokens information when generating the documentation, which outperformed other baseline models on a new corpus constructed from well-documented Kaggle notebooks.

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.

TL;DR: A description of some typical EDA procedures is given and some of the principles of EDA are discussed.

TL;DR: "Digital fluency" should mean designing, creating, and remixing, not just browsing, chatting, and interacting.