2,847 papers · page 11 of 143
Smart contracts are a critical component of blockchain systems. Due to the large amount of digital assets carried by smart contracts, their security is of critical importance. Although numerous tools have been developed for detecting smart contract vulnerability, their effectiven…
Infrastructure-as-Code (IaC) generation holds significant promise for automating the provisioning of cloud infrastructure. Recent advances in Large Language Models (LLMs) present a promising opportunity to democratize IaC development by generating deployable infrastructure templa…
Deep learning (DL)-based systems can exhibit unexpected behavior when exposed to out-of-distribution (OOD) scenarios, posing serious risks in safety-critical domains such as malware detection and autonomous driving. This underscores the importance of thoroughly testing such syste…
Code comprehension is a fundamental challenge in software engineering that impacts developer productivity and software quality. While Large Language Models (LLMs) demonstrate strong capabilities in code generation and summarization, they process code differently from human develo…
Protocol reverse engineering infers the specification of proprietary or poorly documented protocols and serves as the foundation for security analysis such as fuzz testing. While many existing techniques achieve this by mining statistical features from network traces, they face i…
Many modern software projects evolve rapidly to incorporate new features and security patches. It is important for users to update their dependencies to safer versions, but many still use older, vulnerable package versions because upgrading can be difficult and may break their ex…
Reliable pose estimation is critical for intelligent systems, including autonomous vehicles, unmanned aerial vehicles, and virtual reality applications. While visual-inertial odometry (VIO) has made significant advancements in estimating pose, its performance can still be affecte…
Array programming (AP) frameworks (e.g., NumPy and Octave) are widely adopted in scientific computing. Critical defects can jeopardize the entire ecosystem. The stability of API designs enables differential testing on various implementations (e.g., two versions). However, two pri…
Automated unit testing is essential for ensuring software quality. Achieving high code coverage through automated unit test generation remains challenging, especially for hard-to-cover branches guarded by complex or deeply nested conditions. Traditional search-based approaches of…
Screen readers are essential for visually impaired users to access Android apps, but inadequate developer support often leads to semantic ambiguity or label missing. While prior work has focused primarily on label missing issues, semantic ambiguity remains underexplored. In this …
Large language models (LLMs) have achieved remarkable progress in code understanding and analysis tasks. However, state-of-the-art LLMs demonstrate limited performance in vulnerability detection tasks, and even state-of-the-art models struggle to distinguish vulnerable code from …
Large language models (LLMs) have achieved remarkable progress in code generation, yet the structural properties of programming languages introduce distinctive challenges. In particular, program correctness is disproportionately influenced by a subset of structurally critical tok…
Accurate method naming is crucial for code readability and maintainability. However, manually creating concise and meaningful names remains a significant challenge. To this end, in this paper, we propose an approach based on Large Language Model (LLMs) to suggest method names acc…
Nowadays, developers increasingly rely on solutions powered by Large Language Models (LLM) to assist them with their coding tasks. This makes it crucial to align these tools with human values to prevent malicious misuse. In this paper, we propose a comprehensive framework for ass…
Large Language Models (LLMs) were shown to struggle with long-term planning, which may be caused by the limited way in which they explore the space of possible solutions. We propose an architecture where a Reinforcement Learning (RL) Agent guides an LLM’s space exploration: (1) t…
Adaptive Random Testing (ART) has faced criticism, particularly for its computational inefficiency, as highlighted by Arcuri and Briand. Their analysis clarified how ART requires a quadratic number of distance computations as the number of test executions increases, which limits …
Debugging consists in understanding the behavior of a program to identify and correct its defects. Breakpoints are the most commonly used debugging tool and aim to facilitate the debugging process by allowing developers to interrupt a program’s execution at a source code location…
Software development Effort Estimation (SEE) comprises predicting the most realistic amount of effort (e.g., in work hours) required to develop or maintain software based on incomplete, uncertain, and noisy input. Expert judgment is the dominant SEE strategy used in the industry.…
Model-based testing (MBT) has been an important methodology in software engineering, attracting extensive research attention for over four decades. However, despite its academic acclaim, studies examining the impact of MBT in industrial environments—particularly regarding its ext…
Detecting semantically recurring vulnerabilities with similar root causes remains a challenge due to the complex interactions between multiple variables. This paper introduces V ul PA, a novel approach for precisely identifying such vulnerabilities through complex inter-procedura…