Bloat beneath Python's Scales: A Fine-Grained Inter-Project Dependency Analysis
Modern programming languages promote software reuse via package managers that facilitate the integration of inter-dependent software libraries. Software reuse comes with the challenge of dependency bloat, which refers to unneeded and excessive code that is incorporated into a project through reused libraries. The presence of bloated dependency code exhibits security risks and maintenance costs, increases storage requirements, and slows down application load times. In this work, we conduct a large-scale, fine-grained analysis for understanding bloated dependency code in the PyPI ecosystem. Our analysis is the first to focus on different granularity levels, including bloated dependencies, bloated files, and bloated methods. This allows us to identify the specific parts of a library that contribute to the bloat. To do so, we analyze the source code of 1,302 popular Python projects and their 3,232 transitive dependencies. For each project, we employ a state-of-the-art static analyzer and incrementally construct the fine-grained project dependency graph (FPDG), a representation that captures all inter-project dependencies at method-level.
Our reachability analysis on the FPDG enables the assessment of bloated dependency code in terms of several aspects, including its prevalence in the PyPI ecosystem, its relation to software vulnerabilities, its root causes, and developer perception. Our key finding suggests that PyPI exhibits significant resource underutilization: more than 50% of dependencies are bloated. This rate gets worse when considering bloated dependency code at a more subtle level, such as bloated files and bloated methods. Our fine-grained analysis also indicates that there are numerous vulnerabilities that reside in bloated areas of utilized packages (15% of the defects existing in PyPI). Other major observations suggest that bloated code primarily stems from omissions during code refactoring processes and that developers are willing to debloat their code: Out of the 36 submitted pull requests, developers accepted and merged 28, removing a total of 33 bloated dependencies. We believe that our findings can help researchers and practitioners come up with new debloating techniques and development practices to detect and avoid bloated code, ensuring that dependency resources are utilized efficiently.
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.