LEO: an LLM-Powered EDA Overview
Name
zheng-sjzheng-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
2.11 MB
Format
Adobe PDF
Checksum (MD5)
b0506acef31fe4cd1f2cc0006e332c74
Author(s)
Zheng, Sophia
Advisor(s)
Satyanarayan, Arvind
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Computational notebooks impose a linear structure that impedes data analysts’ sensemaking process with overwritten cells, dead-end code, and fragmented logic. This challenge is especially pronounced when analysts either encounter a notebook authored by someone else or revisit a self-authored notebook after significant time has passed. In both cases, understanding the analysis code becomes convoluted and laborious. To address these barriers, we introduce LEO, a computational notebook tool that operationalizes notebook summarization by leveraging large language models to (1) cluster analysis patterns and (2) trace variable use. LEO organizes code into a two-level hierarchy–General Level Sections and Code Level Actions—integrated with in-line textual summaries filtered on the variable-level, further supporting task-driven exploration. We evaluate the system’s effectiveness in a user study with five computational notebook users across two realistic use cases. Participants reported that LEO streamlined code comprehension and navigation of undocumented notebooks by allowing them to query variables and traverse code cells with greater ease.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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