Patrick LaFontaine

dblp:405/3196 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0009-4470-3174ORCID · reported

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

Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021

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
Software testing · 50% Debugging and program repair · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Debugging and program repair
program repair
0.912025
We've Got You Covered: Type-Guided Repair of Incomplete Input Generators · Proc. ACM Program. Lang. 2025
Software testing › random testing
property-based testing
0.912025
We've Got You Covered: Type-Guided Repair of Incomplete Input Generators · Proc. ACM Program. Lang. 2025

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

enumerative synthesis · 0.9coverage types · 0.9
YearPublicationVenuePosition
2025 We've Got You Covered: Type-Guided Repair of Incomplete Input Generators
abstract
Property-based testing (PBT) is a popular technique for automatically testing semantic properties of a program, specified as a pair of pre- and post-conditions. The efficacy of this approach depends on being able to quickly generate inputs that meet the precondition, in order to maximize the set of program behaviors that are probed. For semantically rich preconditions, purely random generation is unlikely to produce many valid inputs; when this occurs, users are forced to manually write their own specialized input generators. One common problem with handwritten generators is that they may be incomplete , i.e., they are unable to generate some values meeting the target precondition. This paper presents a novel program repair technique that patches an incomplete generator so that its range includes every valid input. Our approach uses a novel enumerative synthesis algorithm that leverages the recently developed notion of coverage types to characterize the set of missing test values as well as the coverage provided by candidate repairs. We have implemented a repair tool for OCaml generators, called Cobb, and used it to repair a suite of benchmarks drawn from the PBT literature.
Patrick LaFontaine, Ashish Mishra 0002, Suresh Jagannathan, Benjamin Delaware
Proc. ACM Program. Lang.1