A Benchmark and Framework for Evaluating Next Action Predictions in Spreadsheets
Predictive code completion greatly accelerates how quickly developers work. In spreadsheets, despite being much more common, such auto-completion features are virtually non-existent. To address this gap, we introduce a benchmark for systems that observe a sequence of user actions in a spreadsheet and predict future actions. Two challenges are (1) the absence of edit histories in public spreadsheet corpora and (2) the complex space of spreadsheet actions (spatial, temporal, composite). To address (1), we manually curate 52 sequences of 12K actions that recreate spreadsheets from public corpora, seeded by parametrized heuristics and LLM refinement. To address (2), we propose an online evalu...
| Authors | Tejas Agrawal, Vu Le, Sumit Gulwani et al. |
|---|---|
| Primary category | cs.SE |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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