1 citations · 2 across the 11 of their papers we have counts for
12 papers
Explorable Proofs: Making Written Proofs Explorable by Grounding Them in Formal Representations
Hita Kambhamettu, Will Crichton, Sean Welleck +2
LLM-generated explanations can make technical content more accessible, but there is a ceiling on what they can support interactively. Because LLM outputs are static text, they cann…
Decision-Oriented Programming with Aporia
Saketh Ram Kasibatla, Raven Rothkopf, Hila Peleg +4
AI agents help developers express computational intent abstractly, but at a cost: developers cede decision-making authority to agents, often without realizing that important design…
DIRT: Database-Integrated Random Testing
Alperen Keles, Ethan Chou, Harrison Goldstein +1
Database management systems (DBMSs) are notoriously complex, making them difficult to test effectively, especially during early development when many features are incomplete. Tradi…
Etna: An evaluation platform for property-based testing
Alperen Keles, Jessica Shi, Nikhil Kamath +5
Property-based testing is a mainstay of functional programming, boasting a rich literature, an enthusiastic user community, and an abundance of tools~ -- so many, indeed, that new…
Programmable Property-Based Testing
Alperen Keles, Justine Frank, Ceren Mert +2
Property-based testing (PBT) is a popular technique for establishing confidence in software, where users write properties -- i.e., executable specifications -- that can be checked…
The Search for Constrained Random Generators
Harrison Goldstein, Hila Peleg, Cassia Torczon +3
Among the biggest challenges in property-based testing (PBT) is the constrained random generation problem: given a predicate on program values, randomly sample from the set of all…
Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Ryan Tjoa, Poorva Garg, Harrison Goldstein +3
Property-based testing validates software against an executable specification by evaluating it on randomly generated inputs. The standard way that PBT users generate test inputs is…
Fail Faster: Staging and Fast Randomness for High-Performance PBT
Cynthia Richey, Joseph W. Cutler, Harrison Goldstein +1
Property-based testing (PBT) relies on generators for random test cases, often constructed using embedded domain specific languages, which provide expressive combinators for buildi…
Mica: Automated Differential Testing for OCaml Modules
Ernest Ng, Harrison Goldstein, Benjamin C. Pierce
Suppose we are given two OCaml modules implementing the same signature. How do we check that they are observationally equivalent -- that is, that they behave the same on all inputs…
Stream Types
Joseph W. Cutler, Christopher Watson, Emeka Nkurumeh +4
We propose a rich foundational theory of typed data streams and stream transformers, motivated by two high-level goals: (1) The type of a stream should be able to express complex s…
Parsing Randomness: Unifying and Differentiating Parsers and Random Generators
Harrison Goldstein, Benjamin C. Pierce
"A generator is a parser of randomness." This perspective on generators for random data structures is well established as folklore in the programming languages community, but it ha…
Abstractions for AI-Based User Interfaces and Systems
Alex Renda, Harrison Goldstein, Sarah Bird +2
Novel user interfaces based on artificial intelligence, such as natural-language agents, present new categories of engineering challenges. These systems need to cope with uncertain…