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Large-scale datasets are essential to modern day deep learning. Advocates argue that understanding these methods requires dataset transparency (e.g. "dataset curation, motivation, composition, collection process, etc..."). However, almost…
This paper illustrates an empirical study of the working efficiency of machine learning techniques in classifying code review text by semantic meaning. The code review comments from the source control repository in GitHub were extracted for…
The availability of audio data on sound sharing platforms such as Freesound gives users access to large amounts of annotated audio. Utilising such data for training is becoming increasingly popular, but the problem of label noise that is…
Code search and comprehension have become more difficult in recent years due to the rapid expansion of available source code. Current tools lack a way to label arbitrary code at scale while maintaining up-to-date representations of new…
Bug-fix benchmarks are essential for evaluating methodologies in automatic program repair (APR) and fault localization (FL). However, existing benchmarks, exemplified by Defects4J, need to evolve to incorporate recent bug-fixes aligned with…
Catalog Services play a vital role on Data Grids by allowing users and applications to discover and locate the data needed. On large Data Grids, with hundreds of geographically distributed sites, centralized Catalog Services do not provide…
GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution. To promote the development of the open-source software…
In many academic disciplines, software is created during the research process or for a research purpose. The crucial role of software for research is increasingly acknowledged. The application of software engineering to research software…
Selecting third-party software packages in open-source ecosystems like Python is challenging due to the large number of alternatives and limited transparent evidence for comparison. Generative AI tools are increasingly used in development…
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning, semi-supervised learning and active learning, aim to be…
Dataset Search -- the process of finding appropriate datasets for a given task -- remains a critical yet under-explored challenge in data science workflows. Assessing dataset suitability for a task (e.g., training a classification model) is…
Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases. Changes in data distribution, also known as concept drift, cause the existing classification models to…
Modern machine learning relies on datasets to develop and validate research ideas. Given the growth of publicly available data, finding the right dataset to use is increasingly difficult. Any research question imposes explicit and implicit…
Most network security datasets do not have comprehensive label assignment criteria, hindering the evaluation of the datasets, the training of models, the results obtained, the comparison with other methods, and the evaluation in real-life…
Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical dependency, and the…
We introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of…
Software defect datasets, which are collections of software bugs, are essential resources to facilitate empirical research and enable standardized benchmarking for a wide range of software engineering techniques, including emerging areas…
Quantum computing is rapidly advancing, but quantum software development faces significant challenges, including a steep learning curve, high hardware error rates, and a lack of mature engineering practices. This study conducts a…
Data sharing is essential in the numerical simulations research. We introduce a data repository, DataVault, that is designed for data sharing, search and analysis. A comparative study of existing repositories is performed to analyze…
Artificial Neural Networks (ANNs) replaced conventional software systems in various domains such as machine translation, natural language processing, and image processing. So, why do we need an repository for artificial neural networks?…