Translation proofing
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Communities use many different dialects to document their data. We need to be able to translate between these dialects and to understand how much is lost in translation
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Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
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Translation proofing
- 1. Translation Proofing – Quantitative Tools for Connecting Metadata Dialects Ted Habermann Director of Earth Science The HDF Group thabermann@hdfgroup.org 1
- 2. Metadata in Multiple Dialects Documentation Repository ISO 19115, 19115-2, 19119 and extensions THREDDS HDF, netCDF (NcML) FGDC, Data.Gov SensorML WCS, WMS, WFS, SOS Open Provenance Model, PROV DIF, ECS, ECHO KML
- 3. Translation Lossiness Documentation dialects generally have significant overlap because the concepts that are being documented (who, where, what, when, and why?) are shared cross many communities and dialects. At the same time, there are differences… A B AB More Lossy Less Lossy We are familiar with the idea of lossiness with data compression. How can we quantify the lossiness of a translation?
- 4. Characterizing the Source The distribution of elements in any metadata collection reflects the requirements of the data providers and users. Some elements are more common (important?) than others. This heterogeneity needs to be considered when evaluating the translation. 448 CSDGM Records 161,151 Elements and Attributes 10,713 Place Keywords 1 /metadata/USGSErp/MetadataNotes 264 elements occur < 100 times
- 5. Lossiness = Distribution + Crosswalk + Actual Distribution (collection & community) Reference Crosswalk In order to calculate the lossiness of a translation we need the actual distribution of elements in the source and a reference crosswalk that gives the destinations that the source elements are mapped to. Source Destination
- 6. Three Examples January 8-10, 2014 ESIP Winter 2014 6 Element # % Translated? % Translated A 134 66% 1 66% B 50 25% 1 25% C 20 10% 1 10% 204 1 100% Element A occurs 134 times and makes up 66% of the source Element B occurs 50 times and makes up 25% of the source Element C occurs 20 times and makes up 10% of the source Element # % Translated? % Translated A 134 66% 1 66% B 50 25% 0 0% C 20 10% 1 10% 204 1 75% Element # % Translated? % Translated A 134 66% 1 66% B 50 25% 1 25% C 20 10% 0 0% 204 1 91% 100% elements translated: lossiness = 0% 75% elements translated: lossiness = 25% 91% elements translated: lossiness = 9%
- 7. Calculating Lossiness + Number of Occurrences Total Number of Elements * 1 if in crosswalk 0 if not n = 1 number of elements =Lossiness Actual Distribution (collection & community) Reference Crosswalk 1- Source Destination
- 9. Acknowledgements This work was partially supported by contract number NNG10HP02C from NASA. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author and do not necessarily reflect the views of NASA or The HDF Group.