Code Code Evolution: Understanding How People Change Data Science Notebooks Over Time
| dc.contributor.author | Raghunandan, Deepthi | |
| dc.contributor.author | Roy, Aayushi | |
| dc.contributor.author | Shi, Shenzhi | |
| dc.contributor.author | Elmqvist, Niklas | |
| dc.contributor.author | Battle, Leilani | |
| dc.date.accessioned | 2023-09-14T17:02:25Z | |
| dc.date.available | 2023-09-14T17:02:25Z | |
| dc.date.issued | 2023-04 | |
| dc.description.abstract | Sensemaking is the iterative process of identifying, extracting, and explaining insights from data, where each iteration is referred to as the “sensemaking loop.” However, little is known about how sensemaking behavior evolves from exploration and explanation during this process. This gap limits our ability to understand the full scope of sensemaking, which in turn inhibits the design of tools that support the process. We contribute the first mixed-method to characterize how sensemaking evolves within computational notebooks. We study 2,574 Jupyter notebooks mined from GitHub by identifying data science notebooks that have undergone significant iterations, presenting a regression model that automatically characterizes sensemaking activity, and using this regression model to calculate and analyze shifts in activity across GitHub versions. Our results show that notebook authors participate in various sensemaking tasks over time, such as annotation, branching analysis, and documentation. We use our insights to recommend extensions to current notebook environments. | |
| dc.description.uri | https://doi.org/10.1145/3544548.3580997 | |
| dc.identifier | https://doi.org/10.13016/dspace/vlce-9evi | |
| dc.identifier.citation | Deepthi Raghunandan, Aayushi Roy, Shenzhi Shi, Niklas Elmqvist, and Leilani Battle. 2023. Code Code Evolution: Understanding How People Change Data Science Notebooks Over Time. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 12 pages. | |
| dc.identifier.uri | http://hdl.handle.net/1903/30482 | |
| dc.language.iso | en_US | |
| dc.publisher | Association for Computer Machinery (ACM) | |
| dc.relation.isAvailableAt | College of Computer, Mathematical & Natural Sciences | en_us |
| dc.relation.isAvailableAt | Computer Science | en_us |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_us |
| dc.relation.isAvailableAt | University of Maryland (College Park, MD) | en_us |
| dc.subject | computational notebooks | |
| dc.subject | machine learning | |
| dc.subject | sensemaking | |
| dc.subject | data science | |
| dc.subject | data exploration | |
| dc.subject | analysis | |
| dc.title | Code Code Evolution: Understanding How People Change Data Science Notebooks Over Time | |
| dc.type | Article | |
| local.equitableAccessSubmission | No |