3,458 papers · page 55 of 173
Pretrained language models have been shown to be effective in many software-related generation tasks; however, they are not well-suited for editing tasks as they are not designed to reason about edits. To address this, we propose a novel pretraining objective which explicitly mod…
Deep learning (DL) techniques have attracted much attention in recent years, and have been applied to many application scenarios, including those that are safety-critical. Improving the universal robustness of DL models is vital and many approaches have been proposed in the last …
The robustness of deep neural networks is crucial to modern AI-enabled systems and should be formally verified. Sigmoid-like neural networks have been adopted in a wide range of applications. Due to their non-linearity, Sigmoid-like activation functions are usually over-approxima…
Deep learning has become a promising programming paradigm in software development, owing to its surprising performance in solving many challenging tasks. Deep neural networks (DNNs) are increasingly being deployed in practice, but are limited on resource-constrained devices owing…
Rust is a young systems programming language, but it has gained tremendous popularity thanks to its assurance of memory safety. However, the performance of Rust has been less systematically understood, although many people are claiming that Rust is comparable to C/C++ regarding e…
Modern programs are usually heap-based, where the programs manipulate heap-based data structures to perform computations. In software engineering tasks such as test generation and bounded verification, we need to determine the existence of a reachable heap state that satisfies a …
Researchers have proposed various approaches to generate test programs. The state-of-the-art approaches can be roughly divided into random-based and mutation-based approaches: random-based approaches generate random programs and mutation-based approaches mutate programs to genera…
Although the exception handling mechanism is critical for resolving runtime errors, bugs inside this process can have far-reaching impacts. Therefore, researchers have proposed various approaches to assist catching and handling such thrown exceptions and to detect corresponding b…
Recently, the emerging trend in automatic program repair is to apply deep neural networks to generate fixed code from buggy ones, called NPR (Neural Program Repair). However, the existing NPR systems are trained and evaluated under very different settings (e.g., different trainin…
Unit testing is a critical part of software development process, ensuring the correctness of basic programming units in a program (e.g., a method). Search-based software testing (SBST) is an automated approach to generating test cases. SBST generates test cases with genetic algor…
The size of deep learning models in artificial intelligence (AI) software is increasing rapidly, which hinders the large-scale deployment on resource-restricted devices (e.g., smartphones). To mitigate this issue, AI software compression plays a crucial role, which aims to compre…
No abstract available.
WebAssembly is the newest web standard. It defines a compact bytecode format that allows it to be loaded and executed fast. While WebAssembly is generally believed to be faster than JavaScript, there have been inconsistent results when it comes to showing which code is faster. Un…
Unlike traditional software, smart contracts have the unique organization in which a sequence of transactions shares persistent states. Unfortunately, such a characteristic makes it difficult for existing fuzzers to find out critical transaction sequences. To tackle this challeng…
Decision systems such as Multiple-Criteria Decision Analysis systems formulate a decision process in terms of a mathematical function that takes into consideration different aspects of a problem. Testing such systems is crucial, as they are usually employed in safety-critical sys…
Greybox fuzzing is an effective method for software testing. Greybox fuzzers, such as AFL, use instrumentation that collects path coverage information in order to guide the fuzzing process. The instrumentation is usually inserted by a modified compiler toolchain, meaning that the…
In this paper, we propose intelligent change operators and integrate them into an evolutionary multi-objective search algorithm to recommend valid refactorings that address conflicting quality objectives such as understandability and effectiveness. The proposed intelligent crosso…
At Google, fuzzing C/C++ libraries has discovered tens of thousands of security and robustness bugs. However, these bugs are often reported much after they were introduced. Developers are provided only with fault-inducing test inputs and replication instructions that highlight a …
A symbolic execution engine regularly queries a Satisfiability Modulo Theory (SMT) solver to determine reachability of code during execution. Unfortunately, the SMT solver is often the bottleneck of symbolic execution. Inspired by abstract interpretation, we propose an abstract s…
Testing concurrent systems remains an uncomfortable problem for developers. The common industrial practice is to stress-test a system against large workloads, with the hope of triggering enough corner-case interleavings that reveal bugs. However, stress testing is often inefficie…