We’ve Got You Covered: Type-Guided Repair of Incomplete Input Generators

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.

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Publication Details

Journal
Proceedings of the ACM on Programming Languages
Published
2025-10-09
DOI
https://doi.org/10.1145/3763158
Primary Topic
Software Testing and Debugging Techniques
Type
article
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article

We’ve Got You Covered: Type-Guided Repair of Incomplete Input Generators

Suresh Jagannathan, Benjamin Delaware, Ashish Mishra, Z. Zhou et al.
Proceedings of the ACM on Programming Languages
Software Testing and Debugging Techniques
article

We’ve Got You Covered: Type-Guided Repair of Incomplete Input Generators

Suresh Jagannathan, Benjamin Delaware, Ashish Mishra, Z. Zhou, Patrick LaFontaine
article en

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.

Proceedings of the ACM on Programming LanguagesVol. 9(OOPSLA2)
Purdue University West Lafayette (US), Indian Institute of Technology Hyderabad (IN)
Openalex Percentile: Top 71%
Software Testing and Debugging Techniques
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We’ve Got You Covered: Type-Guided Repair of Incomplete Input Generators — Suresh Jagannathan, Benjamin Delaware, et al. · Proceedings of the ACM on Programming Languages (2025) | TGRS Research Map | TGRS