76 citations · 344 across the 30 of their papers we have counts for
16 papers · 1 filter
Fixing the Fixpoint: A Formal Theory of Convergence Detection for Incremental Recursive Computation
Chengxi Yang, Tej Chajed, Thomas Reps
Modern incremental computation theories like DBSP have enabled efficient incrementalization of general recursive computations. To do so, they require a runtime Fixpoint Detection (…
Modular System Synthesis
Kanghee Park, Keith J. C. Johnson, Loris D'Antoni +1
This paper describes a way to improve the scalability of program synthesis by exploiting modularity: larger programs are synthesized from smaller programs. The key issue is to make…
Newtonian Program Analysis of Probabilistic Programs
Di Wang, Thomas Reps
Due to their quantitative nature, probabilistic programs pose non-trivial challenges for designing compositional and efficient program analyses. Many analyses for probabilistic pro…
Coarse-Tuning Models of Code with Reinforcement Learning Feedback
Abhinav Jain, Chima Adiole, Swarat Chaudhuri +2
Large Language Models (LLMs) pre-trained on code have recently emerged as the dominant approach to program synthesis. However, these models are trained using next-token prediction,…
Synthesizing Specifications
Kanghee Park, Loris D'Antoni, Thomas Reps
Every program should be accompanied by a specification that describes important aspects of the code's behavior, but writing good specifications is often harder than writing the cod…
Unrealizability Logic
Jinwoo Kim, Loris D'Antoni, Thomas Reps
We consider the problem of establishing that a program-synthesis problem is unrealizable (i.e., has no solution in a given search space of programs). Prior work on unrealizability…
Synthesizing Abstract Transformers
Pankaj Kumar Kalita, Sujit Kumar Muduli, Loris D'Antoni +2
This paper addresses the problem of creating abstract transformers automatically. The method we present automates the construction of static analyzers in a fashion similar to the w…
Sound Probabilistic Inference via Guide Types
Di Wang, Jan Hoffmann, Thomas Reps
Probabilistic programming languages aim to describe and automate Bayesian modeling and inference. Modern languages support programmable inference, which allows users to customize i…
Expected-Cost Analysis for Probabilistic Programs and Semantics-Level Adaption of Optional Stopping Theorems
Di Wang, Jan Hoffmann, Thomas Reps
In this article, we present a semantics-level adaption of the Optional Stopping Theorem, sketch an expected-cost analysis as its application, and survey different variants of the O…
Synthesis with Asymptotic Resource Bounds
Qinheping Hu, John Cyphert, Loris D'Antoni +1
We present a method for synthesizing recursive functions that satisfy both a functional specification and an asymptotic resource bound. Prior methods for synthesis with a resource…
Semantics-Guided Synthesis
Jinwoo Kim, Qinheping Hu, Loris D'Antoni +1
This paper develops a new framework for program synthesis, called semantics-guided synthesis (SemGuS), that allows a user to provide both the syntax and the semantics for the const…
A Generating-Extension-Generator for Machine Code
Michael Vaughn, Thomas Reps
The problem of "debloating" programs for security and performance purposes has begun to see increased attention. Of particular interest in many environments is debloating commodity…
Exact and Approximate Methods for Proving Unrealizability of Syntax-Guided Synthesis Problems
Qinheping Hu, John Cyphert, Loris D'Antoni +1
We consider the problem of automatically establishing that a given syntax-guided-synthesis (SyGuS) problem is unrealizable (i.e., has no solution). We formulate the problem of prov…
Templates and Recurrences: Better Together
Jason Breck, John Cyphert, Zachary Kincaid +1
This paper is the confluence of two streams of ideas in the literature on generating numerical invariants, namely: (1) template-based methods, and (2) recurrence-based methods. A t…
Central Moment Analysis for Cost Accumulators in Probabilistic Programs
Di Wang, Jan Hoffmann, Thomas Reps
For probabilistic programs, it is usually not possible to automatically derive exact information about their properties, such as the distribution of states at a given program point…
Proving Unrealizability for Syntax-Guided Synthesis
Qinheping Hu, Jason Breck, John Cyphert +2
Proving Unrealizability for Syntax-Guided Synthesis We consider the problem of automatically establishing that a given syntax-guided-synthesis (SyGuS) problem is unrealizable (i.e.…