6,236 research outputs found
TechMiner: Extracting Technologies from Academic Publications
In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Mining Knowledge in Astrophysical Massive Data Sets
Modern scientific data mainly consist of huge datasets gathered by a very
large number of techniques and stored in very diversified and often
incompatible data repositories. More in general, in the e-science environment,
it is considered as a critical and urgent requirement to integrate services
across distributed, heterogeneous, dynamic "virtual organizations" formed by
different resources within a single enterprise. In the last decade, Astronomy
has become an immensely data rich field due to the evolution of detectors
(plates to digital to mosaics), telescopes and space instruments. The Virtual
Observatory approach consists into the federation under common standards of all
astronomical archives available worldwide, as well as data analysis, data
mining and data exploration applications. The main drive behind such effort
being that once the infrastructure will be completed, it will allow a new type
of multi-wavelength, multi-epoch science which can only be barely imagined.
Data Mining, or Knowledge Discovery in Databases, while being the main
methodology to extract the scientific information contained in such MDS
(Massive Data Sets), poses crucial problems since it has to orchestrate complex
problems posed by transparent access to different computing environments,
scalability of algorithms, reusability of resources, etc. In the present paper
we summarize the present status of the MDS in the Virtual Observatory and what
is currently done and planned to bring advanced Data Mining methodologies in
the case of the DAME (DAta Mining & Exploration) project.Comment: Pages 845-849 1rs International Conference on Frontiers in
Diagnostics Technologie
Analyzing the Evolution and Maintenance of ML Models on Hugging Face
Hugging Face (HF) has established itself as a crucial platform for the
development and sharing of machine learning (ML) models. This repository mining
study, which delves into more than 380,000 models using data gathered via the
HF Hub API, aims to explore the community engagement, evolution, and
maintenance around models hosted on HF, aspects that have yet to be
comprehensively explored in the literature. We first examine the overall growth
and popularity of HF, uncovering trends in ML domains, framework usage, authors
grouping and the evolution of tags and datasets used. Through text analysis of
model card descriptions, we also seek to identify prevalent themes and insights
within the developer community. Our investigation further extends to the
maintenance aspects of models, where we evaluate the maintenance status of ML
models, classify commit messages into various categories (corrective,
perfective, and adaptive), analyze the evolution across development stages of
commits metrics and introduce a new classification system that estimates the
maintenance status of models based on multiple attributes. This study aims to
provide valuable insights about ML model maintenance and evolution that could
inform future model development strategies on platforms like HF.Comment: Accepted at the 2024 IEEE/ACM 21th International Conference on Mining
Software Repositories (MSR
Comparing sentiment analysis tools on gitHub project discussions
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThe context of this work is situated in the rapidly evolving sphere of Natural Language Processing (NLP) within the scope of software engineering, focusing on sentiment analysis in software repositories. Sentiment analysis, a subfield of NLP, provides a potent method to parse, understand, and categorize these sentiments expressed in text. By applying sentiment analysis to software repositories, we can decode developers’ opinions and sentiments, providing key insights into team dynamics, project health, and potential areas of conflict or collaboration. However, the application of sentiment analysis in software engineering comes with its unique set of challenges. Technical jargon, code-specific ambiguities, and the brevity of software-related communications demand tailored NLP tools for effective analysis.
The study unfolds in two primary phases. In the initial phase, we embarked on a meticulous investigation into the impacts of expanding the training sets of two prominent sentiment analysis tools, namely, SentiCR and SentiSW. The objective was to delineate the correlation between the size of the training set and the resulting tool performance, thereby revealing any potential enhancements in performance. The subsequent phase of the research encapsulates a practical application of the enhanced tools. We employed these tools to categorize discussions drawn from issue tickets within a varied array of Open-Source projects. These projects span an extensive range, from relatively small repositories to large, well-established repositories, thus providing a rich and diverse sampling ground.O contexto deste trabalho situa-se na esfera em rápida evolução do Processamento de Linguagem Natural (PLN) no âmbito da engenharia de software, com foco na análise de sentimentos em repositórios de software. A análise de sentimentos, um subcampo do PLN, fornece um método poderoso para analisar, compreender e categorizar os sentimentos expressos em texto. Ao aplicar a análise de sentimentos aos repositórios de software, podemos decifrar as opiniões e sentimentos dos desenvolvedores, fornecendo informações importantes sobre a dinâmica da equipe, a saúde do projeto e áreas potenciais de conflito
ou colaboração. No entanto, a aplicação da análise de sentimentos na engenharia de software apresenta desafios únicos. Jargão técnico, ambiguidades específicas do código e a breviedade das comunicações relacionadas ao software exigem ferramentas de PLN personalizadas para uma análise eficaz. O estudo se desenvolve em duas fases principais. Na fase inicial, embarcamos em uma investigação meticulosa sobre os impactos da expansão dos conjuntos de treinamento de duas ferramentas proeminentes de análise de sentimentos, nomeadamente, SentiCR e SentiSW. O objetivo foi delinear a correlação entre o tamanho do conjunto de treinamento e o desempenho da ferramenta resultante, revelando assim possíveis aprimoramentos no desempenho.
A fase subsequente da pesquisa engloba uma aplicação prática das ferramentas aprimoradas. Utilizamos essas ferramentas para categorizar discussões retiradas de bilhetes de problemas em uma variedade diversificada de projetos de código aberto. Esses projetos abrangem uma ampla gama, desde repositórios relativamente pequenos até repositórios grandes e bem estabelecidos, fornecendo assim um campo de amostragem rico e diversificado
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