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About 1,920 papers for “Python repositories”
Peter Suber · Front Matter · 2006
Rufus Pollock, Accessing open access repositories using the python oaipmh package, <em> miscellaneous factZ </em> , October 6, 2006. Excerpt:
Cited by 0Open access
Spadini, Davide, Aniche, Mauricio, Bacchelli, Alberto · Zenodo (CERN European Organization for Nuclear Research) · 2018
Software repositories contain historical and valuable information about the overall development of software systems. Mining software repositories (MSR) is nowadays considered one of the most interesting growing fields within software engineering. MSR focuses on extracting and analyzing data available in software repositories to uncover interesting, useful, and actionable information about the system. Even though MSR plays an important role in software engineering research, few tools have been created and made public to support developers in extracting information from Git repository. In this paper, we present PyDriller, a Python Framework that eases the process of mining Git. We compare our tool against the state-of-the-art Python Framework GitPython, demonstrating that PyDriller can achieve the same results with, on average, 50% less LOC and significantly lower complexity.<br> <br> URL: https://github.com/ishepard/pydriller<br> <br> Materials: https://doi.org/10.5281/zenodo.1327363<br> <br> Pre-print: https://doi.org/10.5281/zenodo.1327411
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Dong, Honghua, Yang, Jiacheng, Deng, Xun et al. · ArXiv.org · 2025
Type inference for dynamic languages like Python is a persistent challenge in software engineering. While large language models (LLMs) have shown promise in code understanding, their type inference capabilities remain underexplored. We introduce TypyBench, a benchmark designed to evaluate LLMs' type inference across entire Python repositories. TypyBench features two novel metrics: TypeSim, which captures nuanced semantic relationships between predicted and ground truth types, and TypeCheck, which assesses type consistency across codebases. Our evaluation of various LLMs on a curated dataset of 50 high-quality Python repositories reveals that, although LLMs achieve decent TypeSim scores, they struggle with complex nested types and exhibit significant type consistency errors. These findings suggest that future research should shift focus from improving type similarity to addressing repository-level consistency. TypyBench provides a foundation for this new direction, offering insights into model performance across different type complexities and usage contexts. Our code and data are available at https://github.com/typybench/typybench.
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Gudrun Schappacher-Tilp · IEEE DataPort · 2025
"This dataset contains the complete set of mined association rules extracted from Pylint static analysis outputs across six widely used Python codebases: NumPy, Matplotlib, scikit-learn, Flask, PyMeasures, and TheAlgorithms. The repos where cloned on July 12, 2025. Each source file is treated as a transaction and each Pylint message code as an item, enabling the discovery of high-lift co-occurrence patterns, directional relationships, and cross-project regularities in warning behavior. The dataset supports research on software quality assessment, linter interpretation, and mining recurrent design or style issues at scale. All rules include support, confidence, lift, and Jaccard similarity, allowing downstream analysis or reproduction of the full study."
Cited by 0Open access
Stefano Dalla Palma, Dario Di Nucci, Damian A. Tamburri · arXiv (Cornell University) · 2021
Data originating from open-source software projects provide valuable\ninformation to enhance software quality. In the scope of Software Defect\nPrediction, one of the most challenging parts is extracting valid data about\nfailure-prone software components from these repositories, which can help\ndevelop more robust software. In particular, collecting data, calculating\nmetrics, and synthesizing results from these repositories is a tedious and\nerror-prone task, which often requires understanding the programming languages\ninvolved in the mined repositories, eventually leading to a proliferation of\nlanguage-specific data-mining software. This paper presents RepoMiner, a\nlanguage-agnostic tool developed to support software engineering researchers in\ncreating datasets to support any study on defect prediction. RepoMiner\nautomatically collects failure data from software components, labels them as\nfailure-prone or neutral, and calculates metrics to be used as ground truth for\ndefect prediction models. We present its implementation and provide examples of\nits application.\n
Cited by 2Open access
Pankajeshwara Sharma, Bastin Tony Roy Savarimuthu, Nigel Stanger · Journal of Software Evolution and Process · 2023
Abstract Group decision‐making (GDM) processes shape the evolution of open source software (OSS) products, thus playing an important role in the governance of open source software communities. While these GDM processes have attracted the attention of researchers, the rationale behind decisions, that is, how decisions are made that enhance the OSS, have not received much attention. This work bridges this gap by extracting these rationales from a large open source repository comprising 1.55 million emails available in Python development archives. This work makes a methodological contribution by presenting a heuristics‐based rationale extraction system called Rationale Miner that employs information retrieval, natural language processing, and heuristics‐based techniques. Using these techniques, it extracts the rationale behind specific decisions (for example, whether a new module was added based on core developer consensus or a benevolent dictator's pronouncement). This work unearths 11 such rationales behind decisions in the Python community and thus makes a knowledge contribution. It also analyzes the prevalence of these rationales across all PEPs and three sub‐types of PEPs: Process, Informational, and Standard Track PEPs. The effectiveness of our contributions has been positively evaluated using quantitative and qualitative approaches (e.g., comparison against baselines for rationale identification showed up to 47% improvement in the most conservative case, and feedback from the Python steering committee showed the accurate identification of rationales respectively). The approach proposed in this work can be used and extended to discover the rationale behind decisions that remain hidden in communication repositories of other OSS projects, which will make the decision‐making (DM) process transparent to stakeholders and encourage decision‐makers to be more accountable.
Cited by 0Open access
Alexandre Ribas Semeler, Arthur Longoni Oliveira, Fabiana Andrade Pereira et al. · Encontros Bibli Revista Eletrônica de Biblioteconomia e Ciência da Informação · 2023
Objective: Research data repositories are an evolution of document repositories that aim to access and preserve all materials used before, during, and after scientific research. In this context, this study aims to conduct an exploratory and descriptive investigation of the international scenario of data repositories by monitoring the descriptive metadata of the international register of this type of repositories in the Registry of Research Data Repositories (re3data.org). Methods: The process requires applying knowledge inherent to the techniques and technologies used for descriptive data analysis, information retrieval, manipulation, analysis, and data visualization. Consequently, three scripts in Python 3.11 are provided for collecting metadata from re3data and scripts and converting the metadata to enable visualization in software such as VOSviewer, a dataset with metadata descriptions of repositories and conversions for visualization of networks. The datasets produced in this study can be found in the ZENODO Data Repository (https://doi.org/10.5281/zenodo.7903109). In a collection on (05/05/2023), 3108 links to the repository descriptions were retrieved. Data and scripts were created for this methodological experiment and shared at (DOI: doi.org/10.5281/zenodo.7903109). The dataset contains a root directory with three subdirectories: (scripts) with (.py) Python codes, another directory called (data) with textual files containing tab-separated values (.TSV), and the file (Information Systems Research, RIS). The third directory (env) contains the Python libraries required to run the scripts. Potential for reuse: The research method applied to manipulate this dataset is based on automated re3data metadata extraction and network visualization; after the data collection and analysis process, it is possible to trigger a study based on the descriptions extracted from the Registry of Research Data Repositories (re3data), researchers can visualize the international scenario of research data repositories, verified by re3data, which allows ethical monitoring of the number of research data repositories that are registered in re3data, what are their areas, institutions, countries, the language of research data, the typology of repositories and deposited data, their themes, areas of knowledge, types of access, licenses and software used. In addition, other issues can be raised while interpreting the data. The community of Librarianship and Information Science professionals need to share data and the extraction technique these research data. Finally, it can be concluded whether information about research data repositories allows us to state that they are heterogeneous data sources that enable access and preservation of a wide range of research data types
Cited by 1Open access
Christopher K. I. Williams, Rosa Filgueira · St Andrews Research Repository (St Andrews Research Repository) · 2023
This work presents RepoGraph, an integrated semantic code exploration web tool that combines information extraction, knowledge graphs, and deep learning models. It offers new capabilities for software developers (from academia and industry) to represent and query Python repositories. Unlike existing tools, RepoGraph not only provides a novel search interface powered by deep learning techniques but also exposes the underlying features and representations of repositories to users. Additionally, it offers several interactive visualizations. We also introduce RepoPyOnto, a new ontology that captures the features of Python code repositories and is used by RepoGraph for representing the captured knowledge. Finally, we successfully evaluate RepoGraph against several criteria, including function summarization performance, the correctness and relevance of search results, as well as the processing time for constructing graphs of various sizes.
Cited by 1Open access
PDSEN CI Bot, Padams, Jordan, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
PDSEN CI Bot, Padams, Jordan, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
PDSEN CI Bot, Padams, Jordan, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
PDSEN CI Bot, Padams, Jordan, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
Padams, Jordan, PDSEN CI Bot, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
PDSEN CI Bot, Padams, Jordan, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
PDSEN CI Bot, Padams, Jordan, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
PDSEN CI Bot, Padams, Jordan, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
PDSEN CI Bot, Padams, Jordan, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
PDSEN CI Bot · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
Padams, Jordan, PDSEN CI Bot, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access
PDSEN CI Bot, Padams, Jordan, Kelly, Sean et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023
Template for new NASA PDS repositories. For Python software, see https://github.com/nasa-pds/pds-template-repo-python
Cited by 0Open access