[{"file":[{"file_size":1862440,"file_name":"2301.04419.pdf","file_id":"40304","access_level":"open_access","date_updated":"2023-01-26T10:48:40Z","date_created":"2023-01-26T10:48:40Z","creator":"ashwin","relation":"main_file","content_type":"application/pdf"}],"status":"public","abstract":[{"lang":"eng","text":"Jupyter notebooks enable developers to interleave code snippets with rich-text and in-line visualizations. Data scientists use Jupyter notebook as the de-facto standard for creating and sharing machine-learning based solutions, primarily written in Python. Recent studies have demonstrated, however, that a large portion of Jupyter notebooks available on public platforms are undocumented and lacks a narrative structure. This reduces the readability of these notebooks. To address this shortcoming, this paper presents HeaderGen, a novel tool-based approach that automatically annotates code cells with categorical markdown headers based on a taxonomy of machine-learning operations, and classifies and displays function calls according to this taxonomy. For this functionality to be realized, HeaderGen enhances an existing call graph analysis in PyCG. To improve precision, HeaderGen extends PyCG's analysis with support for handling external library code and flow-sensitivity. The former is realized by facilitating the resolution of function return-types. Furthermore, HeaderGen uses type information to perform pattern matching on code syntax to annotate code cells.\r\nThe evaluation on 15 real-world Jupyter notebooks from Kaggle shows that HeaderGen's underlying call graph analysis yields high accuracy (96.4% precision and 95.9% recall). This is because HeaderGen can resolve return-types of external libraries where existing type inference tools such as pytype (by Google), pyright (by Microsoft), and Jedi fall short. The header generation has a precision of 82.2% and a recall rate of 96.8% with regard to headers created manually by experts. In a user study, HeaderGen helps participants finish comprehension and navigation tasks faster. All participants clearly perceive HeaderGen as useful to their task."}],"type":"conference","language":[{"iso":"eng"}],"file_date_updated":"2023-01-26T10:48:40Z","ddc":["000"],"keyword":["static analysis","python","code comprehension","annotation","literate programming","jupyter notebook"],"user_id":"15249","_id":"36522","citation":{"mla":"Shivarpatna Venkatesh, Ashwin Prasad, et al. <i>Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis</i>. IEEE SANER 2023 (International Conference on Software Analysis, Evolution and Reengineering), 2023, doi:<a href=\"https://doi.org/10.48550/ARXIV.2301.04419\">10.48550/ARXIV.2301.04419</a>.","short":"A.P. Shivarpatna Venkatesh, J. Wang, L. Li, E. Bodden, in: IEEE SANER 2023 (International Conference on Software Analysis, Evolution and Reengineering), 2023.","bibtex":"@inproceedings{Shivarpatna Venkatesh_Wang_Li_Bodden_2023, title={Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis}, DOI={<a href=\"https://doi.org/10.48550/ARXIV.2301.04419\">10.48550/ARXIV.2301.04419</a>}, publisher={IEEE SANER 2023 (International Conference on Software Analysis, Evolution and Reengineering)}, author={Shivarpatna Venkatesh, Ashwin Prasad and Wang, Jiawei and Li, Li and Bodden, Eric}, year={2023} }","apa":"Shivarpatna Venkatesh, A. P., Wang, J., Li, L., &#38; Bodden, E. (2023). <i>Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis</i>. IEEE SANER 2023 (International Conference on Software Analysis, Evolution and Reengineering). <a href=\"https://doi.org/10.48550/ARXIV.2301.04419\">https://doi.org/10.48550/ARXIV.2301.04419</a>","ama":"Shivarpatna Venkatesh AP, Wang J, Li L, Bodden E. Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis. In: IEEE SANER 2023 (International Conference on Software Analysis, Evolution and Reengineering); 2023. doi:<a href=\"https://doi.org/10.48550/ARXIV.2301.04419\">10.48550/ARXIV.2301.04419</a>","chicago":"Shivarpatna Venkatesh, Ashwin Prasad, Jiawei Wang, Li Li, and Eric Bodden. “Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis.” IEEE SANER 2023 (International Conference on Software Analysis, Evolution and Reengineering), 2023. <a href=\"https://doi.org/10.48550/ARXIV.2301.04419\">https://doi.org/10.48550/ARXIV.2301.04419</a>.","ieee":"A. P. Shivarpatna Venkatesh, J. Wang, L. Li, and E. Bodden, “Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis,” presented at the IEEE SANER 2023 (International Conference on Software Analysis, Evolution and Reengineering), 2023, doi: <a href=\"https://doi.org/10.48550/ARXIV.2301.04419\">10.48550/ARXIV.2301.04419</a>."},"year":"2023","has_accepted_license":"1","conference":{"name":"IEEE SANER 2023 (International Conference on Software Analysis, Evolution and Reengineering)"},"doi":"10.48550/ARXIV.2301.04419","title":"Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis","author":[{"id":"66637","full_name":"Shivarpatna Venkatesh, Ashwin Prasad","last_name":"Shivarpatna Venkatesh","first_name":"Ashwin Prasad"},{"last_name":"Wang","full_name":"Wang, Jiawei","first_name":"Jiawei"},{"first_name":"Li","last_name":"Li","full_name":"Li, Li"},{"first_name":"Eric","full_name":"Bodden, Eric","id":"59256","last_name":"Bodden","orcid":"0000-0003-3470-3647"}],"date_created":"2023-01-13T08:03:26Z","oa":"1","publisher":"IEEE SANER 2023 (International Conference on Software Analysis, Evolution and Reengineering)","date_updated":"2025-04-07T10:18:03Z"},{"user_id":"72582","_id":"23388","language":[{"iso":"eng"}],"keyword":["Static Analysis","Callgraph Analysis","Python","Qualitative Analysis","Quantitative Analysis","Empirical Evaluation"],"publication":"Proceedings of the 2021 International Conference on Code Quality (ICCQ)","type":"conference","status":"public","abstract":[{"text":"As one of the most popular programming languages, PYTHON has become a relevant target language for static analysis tools. The primary data structure for performing an inter-procedural static analysis is call-graph (CG), which links call sites to potential call targets in a program. There exists multiple algorithms for constructing callgraphs, tailored to specific languages. However, comparatively few implementations target PYTHON. Moreover, there is still lack of empirical evidence as to how these few algorithms perform in terms of precision and recall. This paper thus presents EVAL_CG, an extensible framework for comparative analysis of Python call-graphs. We conducted two experiments which run the CG algorithms on different Python programming constructs and real-world applications. In both experiments, we evaluate three CG generation frameworks namely, Code2flow, Pyan, and Wala. We record precision, recall, and running time, and identify sources of unsoundness of each framework. Our evaluation shows that none of the current CG construction frameworks produce a sound CG. Moreover, the static CGs contain many spurious edges. Code2flow is also comparatively slow. Hence, further research is needed to support CG generation for Python programs.","lang":"eng"}],"author":[{"first_name":"Sriteja","id":"72582","full_name":"Kummita, Sriteja","last_name":"Kummita"},{"orcid":"0000-0003-4424-5838","last_name":"Piskachev","id":"41936","full_name":"Piskachev, Goran","first_name":"Goran"},{"first_name":"Johannes","last_name":"Spaeth","full_name":"Spaeth, Johannes"},{"first_name":"Eric","last_name":"Bodden","orcid":"0000-0003-3470-3647","full_name":"Bodden, Eric","id":"59256"}],"date_created":"2021-08-12T14:00:54Z","date_updated":"2022-01-06T06:55:52Z","conference":{"location":"Virtual","start_date":"2021-03-27","name":"International Conference on Code Quality (ICCQ)"},"doi":"10.1109/ICCQ51190.2021.9392986","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9392986"}],"title":"Qualitative and Quantitative Analysis of Callgraph Algorithms for PYTHON","publication_identifier":{"isbn":["978-1-7281-8477-7"]},"publication_status":"published","citation":{"mla":"Kummita, Sriteja, et al. “Qualitative and Quantitative Analysis of Callgraph Algorithms for PYTHON.” <i>Proceedings of the 2021 International Conference on Code Quality (ICCQ)</i>, 2021, doi:<a href=\"https://doi.org/10.1109/ICCQ51190.2021.9392986\">10.1109/ICCQ51190.2021.9392986</a>.","bibtex":"@inproceedings{Kummita_Piskachev_Spaeth_Bodden_2021, title={Qualitative and Quantitative Analysis of Callgraph Algorithms for PYTHON}, DOI={<a href=\"https://doi.org/10.1109/ICCQ51190.2021.9392986\">10.1109/ICCQ51190.2021.9392986</a>}, booktitle={Proceedings of the 2021 International Conference on Code Quality (ICCQ)}, author={Kummita, Sriteja and Piskachev, Goran and Spaeth, Johannes and Bodden, Eric}, year={2021} }","short":"S. Kummita, G. Piskachev, J. Spaeth, E. Bodden, in: Proceedings of the 2021 International Conference on Code Quality (ICCQ), 2021.","apa":"Kummita, S., Piskachev, G., Spaeth, J., &#38; Bodden, E. (2021). Qualitative and Quantitative Analysis of Callgraph Algorithms for PYTHON. In <i>Proceedings of the 2021 International Conference on Code Quality (ICCQ)</i>. Virtual. <a href=\"https://doi.org/10.1109/ICCQ51190.2021.9392986\">https://doi.org/10.1109/ICCQ51190.2021.9392986</a>","ama":"Kummita S, Piskachev G, Spaeth J, Bodden E. Qualitative and Quantitative Analysis of Callgraph Algorithms for PYTHON. In: <i>Proceedings of the 2021 International Conference on Code Quality (ICCQ)</i>. ; 2021. doi:<a href=\"https://doi.org/10.1109/ICCQ51190.2021.9392986\">10.1109/ICCQ51190.2021.9392986</a>","ieee":"S. Kummita, G. Piskachev, J. Spaeth, and E. Bodden, “Qualitative and Quantitative Analysis of Callgraph Algorithms for PYTHON,” in <i>Proceedings of the 2021 International Conference on Code Quality (ICCQ)</i>, Virtual, 2021.","chicago":"Kummita, Sriteja, Goran Piskachev, Johannes Spaeth, and Eric Bodden. “Qualitative and Quantitative Analysis of Callgraph Algorithms for PYTHON.” In <i>Proceedings of the 2021 International Conference on Code Quality (ICCQ)</i>, 2021. <a href=\"https://doi.org/10.1109/ICCQ51190.2021.9392986\">https://doi.org/10.1109/ICCQ51190.2021.9392986</a>."},"year":"2021"}]