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Roi Gabay

dblp:280/5362 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Program synthesis and code generation · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program synthesis and code generation › search-based program synthesis
bottom-up synthesis
0.412020
Programming with a read-eval-synth loop · Proc. ACM Program. Lang. 2020
Program synthesis and code generation
programming by example
0.412020
Programming with a read-eval-synth loop · Proc. ACM Program. Lang. 2020
Program synthesis and code generation › syntax-guided synthesis
sketch-based synthesis
0.412020
Programming with a read-eval-synth loop · Proc. ACM Program. Lang. 2020

Methods — techniques the papers use, named apart from their topics

user study · 0.4observational equivalence · 0.4
YearPublicationVenuePosition
2020 Programming with a read-eval-synth loop
abstract
A frequent programming pattern for small tasks, especially expressions, is to repeatedly evaluate the program on an input as its editing progresses. The Read-Eval-Print Loop (REPL) interaction model has been a successful model for this programming pattern. We present the new notion of Read-Eval-Synth Loop (RESL) that extends REPL by providing in-place synthesis on parts of the expression marked by the user. RESL eases programming by synthesizing parts of a required solution. The underlying synthesizer relies on a partial solution from the programmer and a few examples. RESL hinges on bottom-up synthesis with general predicates and sketching, generalizing programming by example. To make RESL practical, we present a formal framework that extends observational equivalence to non-example specifications. We evaluate RESL by conducting a controlled within-subjects user-study on 19 programmers from 8 companies, where programmers are asked to solve a small but challenging set of competitive programming problems. We find that programmers using RESL solve these problems with far less need to edit the code themselves and by browsing documentation far less. In addition, they are less likely to leave a task unfinished and more likely to be correct.
Hila Peleg, Roi Gabay, Shachar Itzhaky, Eran Yahav
Proc. ACM Program. Lang.2