PyRadar: Towards Automatically Retrieving and Validating Source Code Repository Information for PyPI Packages
A package’s source code repository records the development history of the package, providing indispensable information for the use and risk monitoring of the package. However, a package release often misses its source code repository due to the separation of the package’s development platform from its distribution platform. To establish the link, existing tools retrieve the release’s repository information from the release’s metadata, which, however, suffers from two limitations: the metadata may not contain or contain the wrong information. Our analysis shows that existing tools can only retrieve repository information for up to 70.5% of PyPI releases (63.1% of packages). To address the limitations, this paper proposes \textsc{PyRadar}, a novel framework that utilizes both the release’s metadata and source code to automatically retrieve and validate the repository information for PyPI package releases. We start with an empirical study to compare four existing metadata-based tools on 4,227,425 PyPI releases and analyze phantom files (files appearing in the release distribution but not in the release’s repository) in 14,375 correct package-repository links and 2,064 incorrect links. Based on the findings, we design \textsc{PyRadar} with three components, i.e., Metadata-based Retriever, Source Code Repository Validator, and Source Code-based Retriever, that progressively retrieves correct source code repository information for PyPI releases. In particular, the Metadata-based Retriever combines the best practices of existing tools and successfully retrieves repository information from the metadata for 72.1% of PyPI releases. The Source Code Repository Validator applies machine learning models on six crafted features and achieves an AUC of up to 0.995. The Source Code-based Retriever queries \textit{World of Code} with the SHA-1 hash of Python files in the release’s source distribution and retrieves repository information for 90.2% of packages in our dataset with an accuracy of 0.970. Both practitioners and researchers can employ the \textsc{PyRadar} to better use PyPI packages.
Fri 19 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
10:30 - 11:00 | |||
10:30 30mPoster | Understanding the Impact of APIs Behavioral Breaking Changes on Client Applications Posters Dhanushka Jayasuriya University of Auckland, Valerio Terragni University of Auckland, Jens Dietrich Victoria University of Wellington, Kelly Blincoe University of Auckland | ||
10:30 30mPoster | Your Code Secret Belongs to Me: Neural Code Completion Tools Can Memorize Hard-coded Credentials Posters Yizhan Huang The Chinese University of Hong Kong, Yichen LI The Chinese University of Hong Kong, Weibin Wu Sun Yat-sen University, Jianping Zhang The Chinese University of Hong Kong, Michael Lyu The Chinese University of Hong Kong | ||
10:30 30mPoster | Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models Posters Yan Wang Central University of Finance and Economics, Xiaoning Li Central University of Finance and Economics, Tien N. Nguyen University of Texas at Dallas, Shaohua Wang Central University of Finance and Economics, Chao Ni School of Software Technology, Zhejiang University, Ling Ding Central University of Finance and Economics | ||
10:30 30mPoster | PyRadar: Towards Automatically Retrieving and Validating Source Code Repository Information for PyPI Packages Posters Kai Gao University of Science and Technology Beijing, Weiwei Xu Peking University, Wenhao Yang Peking University, Minghui Zhou Peking University | ||
10:30 30mPoster | "The Law Doesn’t Work Like a Computer": Exploring Software Licensing Issues Faced by Legal Practitioners Posters Nathan Wintersgill William & Mary, Trevor Stalnaker William & Mary, Laura A. Heymann William & Mary, Oscar Chaparro William & Mary, Denys Poshyvanyk William & Mary | ||
10:30 30mPoster | RavenBuild: Context, Relevance, and Dependency Aware Build Outcome Prediction Posters Gengyi Sun University of Waterloo, Sarra Habchi Ubisoft Montréal, Shane McIntosh University of Waterloo | ||
10:30 30mPoster | MirrorFair: Fixing Fairness Bugs in Machine Learning Software via Counterfactual Predictions Posters Ying Xiao King's College London / Southern University of Science and Technology, Jie M. Zhang King's College London, Yepang Liu Southern University of Science and Technology, Mohammad Reza Mousavi King's College London, Sicen Liu Southern University of Science and Technology, Dingyuan Xue Southern University of Science and Technology | ||
10:30 30mPoster | Do Code Generation Models Think Like Us? - A Study of Attention Alignment between Large Language Models and Human Programmers Posters Bonan Kou Purdue University, Shengmai Chen Purdue University, Zhijie Wang University of Alberta, Lei Ma The University of Tokyo & University of Alberta, Tianyi Zhang Purdue University | ||
10:30 30mPoster | Dependency-Induced Waste in Continuous Integration: An Empirical Study on NPM Dependencies Posters Nimmi Weeraddana University of Waterloo, Mahmoud Alfadel University of Waterloo, Shane McIntosh University of Waterloo | ||
10:30 30mPoster | A Miss Is as Good as A Mile: Metamorphic Testing for Deep Learning Operators Posters Jinyin Chen Zhejiang University of Technology, Chengyu Jia Zhejiang University of Technology, Yunjie Yan Zhejiang University of Technology, Jie Ge Zhejiang University of Technology, haibin zheng Zhejiang University of Technology, Yao Cheng TÜV SÜD Asia Pacific Pte. Ltd. | ||
10:30 30mPoster | Investigating Documented Privacy Changes in Android OS Posters Chuan Yan University of Queensland, Mark Huasong Meng National University of Singapore, Fuman Xie University of Queensland, Guangdong Bai University of Queensland | ||
10:30 30mPoster | Analyzing Quantum Programs with LintQ: A Static Analysis Framework for Qiskit Posters | ||
10:30 30mPoster | Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions Posters Tao Xiao Nara Institute of Science and Technology, Hideaki Hata Shinshu University, Christoph Treude Singapore Management University, Kenichi Matsumoto Nara Institute of Science and Technology | ||
10:30 30mPoster | Bloat beneath Python's Scales: A Fine-Grained Inter-Project Dependency Analysis Posters Georgios-Petros Drosos ETH Zurich, Thodoris Sotiropoulos ETH Zurich, Diomidis Spinellis Athens University of Economics and Business & Delft University of Technology, Dimitris Mitropoulos University of Athens | ||
This room is conjoined with the Foyer to provide additional space for the coffee break, and hold poster presentations throughout the event.