76 citations · 344 across the 30 of their papers we have counts for
9 papers · 1 filter
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…
TOFU: Target-Oriented FUzzer
Zi Wang, Ben Liblit, Thomas Reps
Program fuzzing---providing randomly constructed inputs to a computer program---has proved to be a powerful way to uncover bugs, find security vulnerabilities, and generate test in…
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…
A Dataset of Dockerfiles
Jordan Henkel, Christian Bird, Shuvendu K. Lahiri +1
Dockerfiles are one of the most prevalent kinds of DevOps artifacts used in industry. Despite their prevalence, there is a lack of sophisticated semantics-aware static analysis of…
Learning from, Understanding, and Supporting DevOps Artifacts for Docker
Jordan Henkel, Christian Bird, Shuvendu K. Lahiri +1
With the growing use of DevOps tools and frameworks, there is an increased need for tools and techniques that support more than code. The current state-of-the-art in static develop…
Semantic Robustness of Models of Source Code
Goutham Ramakrishnan, Jordan Henkel, Zi Wang +3
Deep neural networks are vulnerable to adversarial examples - small input perturbations that result in incorrect predictions. We study this problem for models of source code, where…
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…