Bibliographic record
Abstract
Computational notebooks are increasingly used in the fields of data science, computer science, classrooms, the software industry, and various fields. However, users often encounter errors, bugs, and vulnerabilities related to modularized code, unexecuted cells, and outdated library versions. This paper presents a tool, Bugspyter, designed to detect and repair code bugs in Jupyter Notebooks using LLMs. We develop an agent-based model to test the performance of the LLM in identifying the bug types and the root causes of these bugs in the notebooks, along with enhancing the model with static analysis results. Our results show that Bugspyter can identify buggy notebooks and has a high accuracy for identifying implementation bug types in notebooks. Additionally, it can identify coding errors as the root cause of bugs in a notebook but fails to perform well in other root causes of bugs. Furthermore, we see an improvement in the performance of LLMs when identifying bugs in executed notebooks but no change in performance with the inclusion of static analysis results. This study contributes valuable insights into enhancing the reliability of computational notebooks, as it helps to reduce the need for many manual evaluations to fix these issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".