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Elastic sheet-defined functions: Generalising spreadsheet functions to variable-size input arrays

Published online by Cambridge University Press:  21 August 2020

MATT MCCUTCHEN Open the ORCID record for MATT MCCUTCHEN [Opens in a new window] ,
JUDITH BORGHOUTS Open the ORCID record for JUDITH BORGHOUTS [Opens in a new window] ,
ANDREW D. GORDON Open the ORCID record for ANDREW D. GORDON [Opens in a new window] ,
SIMON PEYTON JONES Open the ORCID record for SIMON PEYTON JONES [Opens in a new window]  and
ADVAIT SARKAR Open the ORCID record for ADVAIT SARKAR [Opens in a new window]
MATT MCCUTCHEN
Affiliation:
Massachusetts Institute of Technology, (e-mail: [email protected])
JUDITH BORGHOUTS
Affiliation:
University of California Irvine, (e-mail: [email protected])
ANDREW D. GORDON
Affiliation:
Microsoft Research and University of Edinburgh, (e-mail: [email protected])
SIMON PEYTON JONES
Affiliation:
Microsoft Research, (e-mail: [email protected])
ADVAIT SARKAR
Affiliation:
Microsoft Research and University of Cambridge, (e-mail: [email protected])
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Abstract

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Sheet-defined functions (SDFs) bring modularity and abstraction to the world of spreadsheets. Alas, end users naturally write SDFs that work over fixed-size arrays, which limits their reusability. To help end user programmers write more reusable SDFs, we describe a principled approach to generalising such functions to become elastic SDFs that work over inputs of arbitrary size. We prove that under natural, checkable conditions, our algorithm returns the principal generalisation of an input SDF. We describe a formal semantics and several efficient implementation strategies for elastic SDFs. A user study with spreadsheet users compares the human experience of programming with elastic SDFs to the alternative of relying on array-processing combinators. Our user study finds that the cognitive load of elastic SDFs is lower than for SDFs with map/reduce array combinators, the closest alternative solution.

Type
Research Article
Information
Journal of Functional Programming , Volume 30 , 2020 , e26
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Footnotes

1

Most of the contribution of these authors was made while they were interning at Microsoft Research.

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