Naming the Pain in Developing Scientific Software

Wiese, Igor; Polato, Ivanilton; Pinto, Gustavo

doi:10.1109/ms.2019.2899838

Cited by 21 publications

(11 citation statements)

References 10 publications

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“…Second, regarding Culture , the vast majority of respondents indicated that the level of funding for research software was “insufficient and creates barriers to their work”. A wide-scale survey providing further strong evidence for the utility of theory-software translation research explicitly examined problems within research software engineering, providing a taxonomy of ‘pains’ covering technical, scientific and social issues 26 . This work highlights that many common software engineering challenges, such as requirements gathering, communication difficulties and debugging, are particularly challenging— and often qualitatively different—within science, due to the nature of the research process, and the environment in which the work is conducted.…”

Section: Discussionmentioning

confidence: 99%

The challenges of theory-software translation

et al. 2020

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Background: Software is now ubiquitous within research. In addition to the general challenges common to all software development projects, research software must also represent, manipulate, and provide data for complex theoretical constructs. Ensuring this process of theory-software translation is robust is essential to maintaining the integrity of the science resulting from it, and yet there has been little formal recognition or exploration of the challenges associated with it. Methods: We thematically analyse the outputs of the discussion sessions at the Theory-Software Translation Workshop 2019, where academic researchers and research software engineers from a variety of domains, and with particular expertise in high performance computing, explored the process of translating between scientific theory and software. Results: We identify a wide range of challenges to implementing scientific theory in research software and using the resulting data and models for the advancement of knowledge. We categorise these within the emergent themes of design, infrastructure, and culture, and map them to associated research questions. Conclusions: Systematically investigating how software is constructed and its outputs used within science has the potential to improve the robustness of research software and accelerate progress in its development. We propose that this issue be examined within a new research area of theory-software translation, which would aim to significantly advance both knowledge and scientific practice.

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Section: Discussionmentioning

confidence: 99%

The challenges of theory-software translation

et al. 2020

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“…Most scientists lack formal training in software development and tend not to know about tools and practices that could increase their productivity (Kelly, 2007;Basili et al, 2008;Faulk et al, 2009;Hannay et al, 2009;Hwang et al, 2017;AlNoamany and Borghi, 2018;Pinto et al, 2018;Kellogg et al, 2018). Incentives also play a role: the academic system rewards publication of new results rather than production of high-quality, reusable software (though credit mechanisms for software are now starting to emerge) (LeVeque, 2009;Howison and Herbsleb, 2011;Morin et al, 2012;Turk, 2013;Ahalt et al, 2014;Poisot, 2015;Hwang et al, 2017;Wiese et al, 2019). The combination of incentive structure and lack of training in best practices can lead to inflexible, hard-to-maintain software Johanson and Hasselbring, 2018).…”

Section: Growing Painsmentioning

confidence: 99%

CSDMS: a community platform for numerical modeling of Earth surface processes

et al. 2022

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Abstract. Computational modeling occupies a unique niche in Earth and environmental sciences. Models serve not just as scientific technology and infrastructure but also as digital containers of the scientific community's understanding of the natural world. As this understanding improves, so too must the associated software. This dual nature – models as both infrastructure and hypotheses – means that modeling software must be designed to evolve continually as geoscientific knowledge itself evolves. Here we describe design principles, protocols, and tools developed by the Community Surface Dynamics Modeling System (CSDMS) to promote a flexible, interoperable, and ever-improving research software ecosystem. These include a community repository for model sharing and metadata, interface and ontology standards for model interoperability, language-bridging tools, a modular programming library for model construction, modular software components for data access, and a Python-based execution and model-coupling framework. Methods of community support and engagement that help create a community-centered software ecosystem are also discussed.

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“…Most scientists lack formal training in software development, and tend not to know about tools and practices that could increase their productivity (Kelly, 2007;Basili et al, 2008;Faulk et al, 2009;Hannay et al, 2009;Hwang et al, 2017;AlNoamany and Borghi, 2018;Pinto et al, 2018;Kellogg et al, 2018). Incentives also play a role: the academic system rewards publication of new results rather than production of high-quality, reusable software (though credit mechanisms for software are now starting to emerge) (LeVeque, 2009;Howison and Herbsleb, 2011;Morin et al, 2012;Turk, 2013;Ahalt et al, 2014;Poisot, 2015;Hwang et al, 2017;Wiese et al, 2019). The combination of incentive structure and lack of training in best practices can lead to inflexible, hard-to-maintain software (Brown et al, 2014;Johanson and Hasselbring, 2018).…”

Section: Growing Painsmentioning

confidence: 99%

CSDMS: A community platform for numerical modeling of Earth-surface processes

Tucker

Hutton

Piper

et al. 2021

Preprint

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Abstract. Computational modelling occupies a unique niche in Earth and environmental sciences. Models serve not just as scientific technology and infrastructure, but also as digital containers of the scientific community's understanding of the natural world. As this understanding improves, so too must the associated software. This dual nature–models as both infrastructure and hypotheses–means that modelling software must be designed to evolve continually as geoscientific knowledge itself evolves. Here we describe design principles, protocols, and tools developed by the Community Surface Dynamics Modeling System (CSDMS) to promote a flexible, interoperable, and ever-improving research software ecosystem. These include a community repository for model sharing and metadata, interface and ontology standards for model interoperability, language bridging tools, a modular programming library for model construction, modular software components for data access, and a Python-based execution and model-coupling framework. Methods of community support and engagement that help create a community-centered software ecosystem are also discussed.

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Naming the Pain in Developing Scientific Software

Cited by 21 publications

References 10 publications

The challenges of theory-software translation

The challenges of theory-software translation

CSDMS: a community platform for numerical modeling of Earth surface processes

CSDMS: A community platform for numerical modeling of Earth-surface processes

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