Related papers: Artifact Sharing for Information Retrieval Researc…
In recent years, the research community has raised serious questions about the reproducibility of scientific work. In particular, since many studies include some kind of computing work, reproducibility is also a technological challenge, not…
Although computer science papers are often accompanied by software artifacts, connecting research papers to their software artifacts and vice versa is not always trivial. First of all, there is a lack of well-accepted standards for how such…
Software development relies heavily on traceability links between various software artifacts to ensure quality and facilitate maintenance. While automated traceability recovery techniques have advanced for different artifact pairs, the…
Sharing and reusing research artifacts, such as datasets, publications, or methods is a fundamental part of scientific activity, where heterogeneity of resources and metadata and the common practice of capturing information in unstructured…
The substance of this paper is the description of the use of Retrieval-Augmented Generation (RAG) for specific digital collections of cultural assets. The collections are provided by institutions operating in the cultural sector. The…
In recent years, funding agencies and journals increasingly advocate for open science practices (e.g. data and method sharing) to improve the transparency, access, and reproducibility of science. However, quantifying these practices at…
Reproducibility is an ideal that no researcher would dispute "in the abstract", but when aspirations meet the cold hard reality of the academic grind, reproducibility often "loses out". In this essay, I share some personal experiences…
Large language models trained on source code can support a variety of software development tasks, such as code recommendation and program repair. Large amounts of data for training such models benefit the models' performance. However, the…
The reproducibility of scientific articles is central to the advancement of science. Despite this importance, evaluating reproducibility remains challenging due to the scarcity of ground truth data. Predictive models can address this…
Small to medium-scale data science experiments often rely on research software developed ad-hoc by individual scientists or small teams. Often there is no time to make the research software fast, reusable, and open access. The consequence…
Computer science is also an experimental science. This is particularly the case for parallel computing, which is in a total state of flux, and where experiments are necessary to substantiate, complement, and challenge theoretical modeling…
Objective: In this study, we aim to replicate an artefact-based study on software testing to address the gap. We focus on (a) providing a step by step guide of the replication, reflecting on challenges when replicating artefact-based…
Without sufficient information about research data practices occurring in a particular research organisation, there is a risk of mismatching research data service efforts with the needs of its researchers. This study describes how data…
The drive for reproducibility in the computational sciences has provoked discussion and effort across a broad range of perspectives: technological, legislative/policy, education, and publishing. Discussion on these topics is not new, but…
A plethora of scholarly knowledge is being published on distributed scholarly infrastructures. Querying a single infrastructure is no longer sufficient for researchers to satisfy information needs. We present a GraphQL-based federated query…
This paper proposes a Bibliographic system intends to exchange bibliographic information of survey/review articles by relying on Web service technology. It allows researchers and university students to interact with system via single…
Many research groups aspire to make data and code FAIR and reproducible, yet struggle because the data and code life cycles are disconnected, executable environments are often missing from published work, and technical skill requirements…
An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed - leading to a diverse landscape of ML. These ML innovations…
The concept of traceability between artifacts is considered an enabler for software project success. This concept has received plenty of attention from the research community and is by many perceived to always be available in an industrial…
The reproducibility of scientific experiment is vital for the advancement of disciplines based on previous work. To achieve this goal, many researchers focus on complex methodology and self-invented tools which have difficulty in practical…