62 citations · 238 across the 21 of their papers we have counts for
21 papers
OBsmith: LLM-Powered JavaScript Obfuscator Testing
Shan Jiang, Chenguang Zhu, Sarfraz Khurshid
JavaScript obfuscators are widely deployed to protect intellectual property and resist reverse engineering, yet their correctness has been largely overlooked compared to performanc…
APRIL: API Synthesis with Automatic Prompt Optimization and Reinforcement Learning
Hua Zhong, Shan Jiang, Sarfraz Khurshid
APIs are central to modern software development, yet composing new APIs from large libraries is difficult due to the exponential search space; traditional component-based synthesis…
On the Effectiveness of Large Language Models in Writing Alloy Formulas
Yang Hong, Shan Jiang, Yulei Fu +1
Declarative specifications have a vital role to play in developing safe and dependable software systems. Writing specifications correctly, however, remains particularly challenging…
An approach for API synthesis using large language models
Hua Zhong, Shan Jiang, Sarfraz Khurshid
APIs play a pivotal role in modern software development by enabling seamless communication and integration between various systems, applications, and services. Component-based API…
Generating executable oracles to check conformance of client code to requirements of JDK Javadocs using LLMs
Shan Jiang, Chenguang Zhu, Sarfraz Khurshid
Software testing remains the most widely used methodology for validating quality of code. However, effectiveness of testing critically depends on the quality of test suites used. T…
Detecting Multi-Parameter Constraint Inconsistencies in Python Data Science Libraries
Xiufeng Xu, Fuman Xie, Chenguang Zhu +3
Modern AI- and Data-intensive software systems rely heavily on data science and machine learning libraries that provide essential algorithmic implementations and computational fram…
TAC: Hybrid IAM Privilege Escalation Detection
Yang Hu, Wenxi Wang, Sarfraz Khurshid +1
IAM misconfigurations are a major cause of privilege escalation (PE) attacks in the cloud, leading to data breaches and major financial losses. Existing PE detectors have two main…
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions
Ripon K. Saha, Akira Ura, Sonal Mahajan +6
Automatic machine learning, or AutoML, holds the promise of truly democratizing the use of machine learning (ML), by substantially automating the work of data scientists. However,…
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
Wenxi Wang, Yang Hu, Mohit Tiwari +3
Propositional satisfiability (SAT) is an NP-complete problem that impacts many research fields, such as planning, verification, and security. Mainstream modern SAT solvers are base…
Programming and Training Rate-Independent Chemical Reaction Networks
Marko Vasic, Cameron Chalk, Austin Luchsinger +2
Embedding computation in biochemical environments incompatible with traditional electronics is expected to have wide-ranging impact in synthetic biology, medicine, nanofabrication…
Distributed Symbolic Execution using Test-Depth Partitioning
Shikhar Singh, Sarfraz Khurshid
Symbolic execution is a classic technique for systematic bug finding, which has seen many applications in recent years but remains hard to scale. Recent work introduced ranged symb…
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
Marko Vasic, Cameron Chalk, Sarfraz Khurshid +1
Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and ot…
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)
Muhammad Usman, Wenxi Wang, Kaiyuan Wang +3
This paper introduces the MCML approach for empirically studying the learnability of relational properties that can be expressed in the well-known software design language Alloy. A…
CRNs Exposed: Systematic Exploration of Chemical Reaction Networks
Marko Vasic, David Soloveichik, Sarfraz Khurshid
Formal methods have enabled breakthroughs in many fields, such as in hardware verification, machine learning and biological systems. The key object of interest in systems biology,…
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
Marko Vasic, Andrija Petrovic, Kaiyuan Wang +3
Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their ad…
CRN++: Molecular Programming Language
Marko Vasic, David Soloveichik, Sarfraz Khurshid
Synthetic biology is a rapidly emerging research area, with expected wide-ranging impact in biology, nanofabrication, and medicine. A key technical challenge lies in embedding comp…
Executable Trigger-Action Comments
Pengyu Nie, Rishabh Rai, Junyi Jessy Li +3
Natural language elements, e.g., todo comments, are frequently used to communicate among the developers and to describe tasks that need to be performed (actions) when specific cond…
Symbolic Execution for Deep Neural Networks
Divya Gopinath, Kaiyuan Wang, Mengshi Zhang +2
Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with substantial safety and security concerns. This paper introduces DeepCheck, a new ap…
Fault Localization for Declarative Models in Alloy
Kaiyuan Wang, Allison Sullivan, Darko Marinov +1
Fault localization is a popular research topic and many techniques have been proposed to locate faults in imperative code, e.g. C and Java. In this paper, we focus on the problem o…
DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing
Mengshi Zhang, Yuqun Zhang, Lingming Zhang +2
While Deep Neural Networks (DNNs) have established the fundamentals of DNN-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To reso…
Effectiveness of Anonymization in Double-Blind Review
Claire Le Goues, Yuriy Brun, Sven Apel +3
Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions. We explore anonymization effectiveness at ASE 2016, OOPSLA 2016, and…