Is this GitHub project maintained? Measuring the level of maintenance activity of open-source projects

Coelho, Jailton; Valente, M.; Milen, Luciano O.; Silva, Luciana Lourdes

doi:10.1016/j.infsof.2020.106274

Cited by 58 publications

(50 citation statements)

References 45 publications

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“…Although these metrics do not necessarily reflect software quality, Coleman et al [11] demonstrated that quantitative metrics such as lines of code can be useful in evaluating software maintainability. Similarly, a recent study [7] showed that such code commit-related attributes can be used to evaluate the maintenance level of open-source projects.…”

Section: Code Maintenance Activities Analysismentioning

confidence: 99%

Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild

Choi¹,

Choi²,

Aiken³

et al. 2022

Preprint

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Since Bitcoin appeared in 2009, over 6,000 different cryptocurrency projects have followed. The cryptocurrency world may be the only technology where a massive number of competitors offer similar services yet claim unique benefits, including scalability, fast transactions, and security. But are these projects really offering unique features and significant enhancements over their competitors? To answer this question, we conducted a large-scale empirical analysis of code maintenance activities, originality and security across 592 crypto projects. We found that about half of these projects have not been updated for the last six months; over two years, about three-quarters of them disappeared, or were reported as scams or inactive. We also investigated whether 11 security vulnerabilities patched in Bitcoin were also patched in other projects. We found that about 80% of 510 C-language-based cryptocurrency projects have at least one unpatched vulnerability, and the mean time taken to fix the vulnerability is 237.8 days. Among those 510 altcoins, we found that at least 157 altcoins are likely to have been forked from Bitcoin, about a third of them containing only slight changes from the Bitcoin version from which they were forked. As case studies, we did a deep dive into 20 altcoins (e.g., Litecoin, FujiCoin, and Feathercoin) similar to the version of Bitcoin used for the fork. About half of them did not make any technically meaningful change -failing to comply with the promises (e.g., about using Proof of Stake) made in their whitepapers.

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Section: Code Maintenance Activities Analysismentioning

confidence: 99%

Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild

Choi¹,

Choi²,

Aiken³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This adaptability is conducive to collaboration, the creation of mutually supportive user/developer communities and rapid evolution, making open source software ecosystems fast moving and highly diverse. It is impossible to discuss all software options that could be used for geographic transport planning: there are literally thousands of software projects written in dozens of programming languages, many of which are no longer actively maintained (Coelho et al 2020). Transport planners should use solutions that are future proof and actively maintained.…”

Section: Defining Open Sourcementioning

confidence: 99%

Open source tools for geographic analysis in transport planning

Lovelace

2021

J Geogr Syst

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Geographic analysis has long supported transport plans that are appropriate to local contexts. Many incumbent ‘tools of the trade’ are proprietary and were developed to support growth in motor traffic, limiting their utility for transport planners who have been tasked with twenty-first century objectives such as enabling citizen participation, reducing pollution, and increasing levels of physical activity by getting more people walking and cycling. Geographic techniques—such as route analysis, network editing, localised impact assessment and interactive map visualisation—have great potential to support modern transport planning priorities. The aim of this paper is to explore emerging open source tools for geographic analysis in transport planning, with reference to the literature and a review of open source tools that are already being used. A key finding is that a growing number of options exist, challenging the current landscape of proprietary tools. These can be classified as command-line interface, graphical user interface or web-based user interface tools and by the framework in which they were implemented, with numerous tools released as R, Python and JavaScript packages, and QGIS plugins. The review found a diverse and rapidly evolving ‘ecosystem’ tools, with 25 tools that were designed for geographic analysis to support transport planning outlined in terms of their popularity and functionality based on online documentation. They ranged in size from single-purpose tools such as the QGIS plugin AwaP to sophisticated stand-alone multi-modal traffic simulation software such as MATSim, SUMO and Veins. Building on their ability to re-use the most effective components from other open source projects, developers of open source transport planning tools can avoid ‘reinventing the wheel’ and focus on innovation, the ‘gamified’ A/B Street https://github.com/dabreegster/abstreet/#abstreet simulation software, based on OpenStreetMap, a case in point. The paper, the source code of which can be found at https://github.com/robinlovelace/open-gat, concludes that, although many of the tools reviewed are still evolving and further research is needed to understand their relative strengths and barriers to uptake, open source tools for geographic analysis in transport planning already hold great potential to help generate the strategic visions of change and evidence that is needed by transport planners in the twenty-first century.

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“…Las técnicas utilizadas para clasificar los repositorios son Random Forest y Naive Bayes. De la misma forma, en Jiang et al ( 2021) y Coelho et al (2020) se aplican técnicas de machine learning basadas en redes neuronales para hacer recomendación de repositorios a partir de las etiquetas que contienen elementos del repositorio, tales como code, title, commits. Siguiendo en la misma línea, en Kallis, Di Sorbo, Canfora y Panichella (2020) se propone Ticket Tagger, una aplicación de GitHub que analiza el título y la descripción del repositorio mediante técnicas de machine learning para reconocer automáticamente los tipos de informes enviados en GitHub y asignar etiquetas a cada repositorio en consecuencia, aplicando algoritmos de clasificación de texto basados en clasificadores lineales, además de un enfoque de Deep Learning usando redes neuronales.…”

Section: Trabajos Relacionadosunclassified

Recuperación y clasificación de arquitecturas software en GitHub para reutilización, soportado por ontologías

Ordóñez

Buchelli

2021

Rev. Cient.

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Para definir una arquitectura de un nuevo proyecto de software es clave reutilizar componentes existentes en proyectos previos. Estos proyectos previos pueden ser de la organización o estar disponibles en internet a través de GitHub. Los componentes reutilizados permiten tomar decisiones arquitecturales y así economizar tiempo y recursos. Sin embargo, actualmente buscar componentes de software en GitHub es ineficiente, pues las búsquedas son por cadenas de texto. En este trabajo se presenta OntoGitHubSearch como un modelo de búsqueda que permite la recuperación y clasificación de arquitecturas de software almacenadas en GitHub. El modelo recupera todos los repositorios que coincidan con los conceptos de arquitectura buscados; para darle semántica y contexto a la búsqueda, el modelo implementa la ontología de dominio específico Service-Oriented Architecture Ontology Version 2.0. Adicionalmente, se implementa procesamiento de lenguaje natural para analizar el texto del repositorio. Los repositorios son identificados y clasificados según el contexto que se expresa en el texto y en los conceptos encontrados en las descripciones del mismo. Para el proceso de evaluación del modelo propuesto se desarrolló una aplicación web denominada WebOntoGitHubSearch, la cual permite la interacción con usuarios en el proceso de consulta. La evaluación se realizó con la participación de desarrolladores y arquitectos de software de varias empresas colombianas, se utilizaron métricas de recuperación de información tales como Precision at k. Los resultados obtenidos en el proceso de evaluación son promisorios y permiten verificar la eficacia del modelo propuesto.

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Is this GitHub project maintained? Measuring the level of maintenance activity of open-source projects

Cited by 58 publications

References 45 publications

Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild

Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild

Open source tools for geographic analysis in transport planning

Recuperación y clasificación de arquitecturas software en GitHub para reutilización, soportado por ontologías

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