Paper submitted to WSSSPE2: “Can I Implement Your Algorithm?”: A Model for Reproducible Research Software

Yesterday, me, Ben Hall and Samin Ishtiaq (both Microsoft Research Cambridge) submitted a paper to WSSSPE2, the 2nd Workshop on Sustainable Software for Science: Practice and Experiences to be held in conjunction with SC14 in New Orleans in November. As per the aims of the workshop: progress in scientific research is dependent on the quality and accessibility of software at all levels and it is critical to address challenges related to the development, deployment and maintenance of reusable software as well as education around software practices.

As discussed in our paper, we feel this multitude of research software engineering problems are not just manifest in computer science, but also across the computational science and engineering domains (particularly with regards to benchmarking and availability of code). We highlight a number of recommendations to address these issues, as well as proposing a new open platform for scientific software development. You can download our arXiv pre-print; the abstract is as follows:

The reproduction and replication of novel scientific results has become a major issue for a number of disciplines. In computer science and related disciplines such as systems biology, the issues closely revolve around the ability to implement novel algorithms and approaches. Taking an approach from the literature and applying it in a new codebase frequently requires local knowledge missing from the published manuscripts and project websites. Alongside this issue, benchmarking, and the development of fair, and widely available benchmark sets present another barrier. In this paper, we outline several suggestions to address these issues, driven by specific examples from a range of scientific domains. Finally, based on these suggestions, we propose a new open platform for scientific software development which effectively isolates specific dependencies from the individual researcher and their workstation and allows faster, more powerful sharing of the results of scientific software engineering.

 
(see GitHub repo)

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