26,098 papers · page 136 of 1,305
As modern software systems evolve towards greater complexity, ensuring their reliable operation has become a critical challenge. Log data analysis is vital in maintaining system stability, with anomaly detection being a key aspect. However, existing log anomaly detection methods …
Rust is renowned for its robust memory safety capabilities, yet its distinctive memory management model poses substantial challenges in both writing and understanding programs. Within Rust source code, comments are employed to clearly delineate conditions that might cause panic b…
Automated web GUI testing has been widely adopted since manual testing is time-consuming and tedious. Waiting strategy plays a vital role in automated web GUI testing since it significantly impacts the testing performance. Though important, little focus has been set on the waitin…
Existing fault localization techniques typically analyze static information and run-time profiles of faulty software programs, and subsequently calculate suspiciousness values for each program entity. Such strategies typically have overbroad information to be analyzed and lead to…
Software vulnerabilities pose serious threats to the security of modern software systems. Deep Learning-based Automated Vulnerability Repair (AVR) has gained attention as a potential solution to accelerate the remediation of vulnerabilities. However, recent studies indicate that …
Convolutional neural network (CNN) currently has been widely used to undertake the task of image classification. Unfortunately, a trained CNN model is a nonlinear system with high complexity, and the implicit decision knowledge carried by the CNN model is often difficult to be co…
Large Language Models (LLMs) have shown their superior capability in natural language processing, promoting extensive LLM-powered applications to be the new portals for people to access various content on the Internet. However, LLM-powered applications do not have sufficient secu…
The proliferation of pre-trained models (PTMs) and datasets has led to the emergence of centralized model hubs like Hugging Face, which facilitate collaborative development and reuse. However, recent security reports have uncovered vulnerabilities and instances of malicious attac…
Mutation testing is widely used to measure the test adequacy of a project. Despite its popularity, mutation testing is time-consuming and extremely expensive. To mitigate this problem, researchers propose Predictive Mutation Testing (PMT). Existing PMT approaches build classifica…
Vulnerability detection is garnering increasing attention in software engineering, since code vulnerabilities possibly pose significant security. Recently, reusing various code pre-trained models (e.g., CodeBERT, CodeT5, and CodeGen) has become common for code embedding without p…
Estimating software functional size is a crucial initial step before development, impacting costs and timelines. This involves applying standard Function Point Analysis (FPA) to the Software Requirements Specification (SRS). However, manual analysis by Function Point (FP) analyst…
As machine learning (ML) systems are increasingly used in various fields, including tasks with high social impact, concerns about their fairness are growing. To address these concerns, individual fairness testing (IFT) has been introduced to identify individual discriminatory ins…
Software architectures are usually meticulously designed to address multiple quality concerns and support long-term maintenance. However, there may be a lack of motivation for developers to document design rationales (i.e., the design alternatives and the underlying arguments for…
Automatic Program Repair (APR) endeavors to autonomously rectify issues within specific projects, which generally encompasses three categories of tasks: bug resolution, new feature development, and feature enhancement. Despite extensive research proposing various methodologies, t…
Android malware attacks have posed a severe threat to mobile users, necessitating a significant demand for the automated detection system. Among the various tools employed in malware detection, graph representations (e.g., function call graphs) have played a pivotal role in chara…
The exponential growth of open-source package ecosystems, particularly NPM and PyPI, has led to an alarming increase in software supply chain poisoning attacks. Existing static analysis methods struggle with high false positive rates and are easily thwarted by obfuscation and dyn…
Deploying deep learning (DL) models on mobile applications (Apps) has become ever-more popular. However, existing studies show attackers can easily reverse-engineer mobile DL models in Apps to steal intellectual property or generate effective attacks. A recent approach, Model Obf…
The wide adoption of open source third-party libraries can propagate vulnerabilities that originally exist in third-party libraries through dependency chains to downstream projects. To mitigate this security risk, vulnerability exploitation analysis has been proposed to further r…
In recent years, Deep Learning (DL) applications in JavaScript environment have become increasingly popular. As the infrastructure for DL applications, JavaScript DL frameworks play a crucial role in the development and deployment. It is essential to ensure the quality of JavaScr…
We study the binomial, trinomial, and Black-Scholes-Merton models of option pricing. We present fast parallel discrete-time finite-difference algorithms for American call option pricing under the binomial and trinomial models and American put option pricing under the Black-Schole…