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Register a new userMapping the research software sustainability space
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Added by
Stephan Druskat
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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A growing number of largely uncoordinated initiatives focus on research software sustainability. A comprehensive mapping of the research software sustainability space can help identify gaps in their efforts, track results, and avoid duplication of work. To this end, this paper suggests enhancing an existing schematic of activities in research software sustainability, and formalizing it in a directed graph model. Such a model can be further used to define a classification schema which, applied to research results in the field, can drive the identification of past activities and the planning of future efforts.
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Research software is essential to modern research, but it requires ongoing human effort to sustain: to continually adapt to changes in dependencies, to fix bugs, and to add new features. Software sustainability institutes, amongst others, develop, maintain, and disseminate best practices for research software sustainability, and build community around them. These practices can both reduce the amount of effort that is needed and create an environment where the effort is appreciated and rewarded. The UK SSI is such an institute, and the US URSSI and the Australian AuSSI are planning to become institutes, and this extended abstract discusses them and the strengths and weaknesses of this approach.
Many science advances have been possible thanks to the use of research software, which has become essential to advancing virtually every Science, Technology, Engineering and Mathematics (STEM) discipline and many non-STEM disciplines including social sciences and humanities. And while much of it is made available under open source licenses, work is needed to develop, support, and sustain it, as underlying systems and software as well as user needs evolve. In addition, the changing landscape of high-performance computing (HPC) platforms, where performance and scaling advances are ever more reliant on software and algorithm improvements as we hit hardware scaling barriers, is causing renewed tension between sustainability of software and its performance. We must do more to highlight the trade-off between performance and sustainability, and to emphasize the need for sustainability given the fact that complex software stacks dont survive without frequent maintenance; made more difficult as a generation of developers of established and heavily-used research software retire. Several HPC forums are doing this, and it has become an active area of funding as well. In response, the authors organized and ran a panel at the SC18 conference. The objectives of the panel were to highlight the importance of sustainability, to illuminate the tension between pure performance and sustainability, and to steer SC community discussion toward understanding and addressing this issue and this tension. The outcome of the discussions, as presented in this paper, can inform choices of advance compute and data infrastructures to positively impact future research software and future research.
A growing number of researchers suggest that software process must be tailored to a projects context to achieve maximal performance. Researchers have studied context in an ad-hoc way, with focus on those contextual factors that appear to be of significance. The result is that we have no useful basis upon which to contrast and compare studies. We are currently researching a theoretical basis for software context for the purpose of tailoring and note that a deeper consideration of the meaning of the term context is required before we can proceed. In this paper, we examine the term and present a model based on insights gained from our initial categorisation of contextual factors from the literature. We test our understanding by analysing a further six documents. Our contribution thus far is a model that we believe will support a theoretical operationalisation of software context for the purpose of process tailoring.
Empirical Standards are natural-language models of a scientific communitys expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for research methods commonly used in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, will improve research quality and make peer review more effective, reliable, transparent and fair.
Managing and growing a successful cyberinfrastructure such as nanoHUB.org presents a variety of opportunities and challenges, particularly in regard to software. This position paper details a number of those issues and how we have approached them.
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