26,098 papers · page 27 of 1,305
Writing code requires significant time and effort in software development. To automate this process, researchers have made substantial progress using Large Language Models (LLMs) for code generation. Many benchmarks like HumanEval and EvoCodeBench have been created to evaluate LL…
In recent years, Extended Reality (XR) has emerged as a transformative technology, offering users immersive and interactive experiences across diversified virtual or virtual-real environments. Users can interact with XR applications (apps) through interactable GUI elements (IGEs)…
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation at the function or file level. However, they achieve limited performance on repository-level code generation due to the complicated repository context where a substantial amount of files and…
The rapid advancement of large language models (LLMs) in code generation has greatly improved software development efficiency, but it has also raised concerns about misuse, making the distinction between human-written and LLM-generated code an urgent task. However, existing detec…
Automated Program Repair (APR) powered by Large Language Models (LLMs) has shown strong potential for improving software reliability. However, existing LLM-based APR approaches underutilize the rich debugging knowledge latent in large-scale bug-fix corpora. Prior work has primari…
Multi-user Extended Reality (XR) systems enable transformative shared experiences but introduce unique software defects that compromise user experience. Understanding software defects in multi-user XR systems is crucial for enhancing system reliability, yet remains underexplored.…
Manual formal analysis of cryptographic schemes is labor-intensive and requires substantial expertise. While model-checking tools (e.g., Scyther and Tamarin) and computational-security tools (e.g., CryptoVerif and EasyCrypt) improve the automation of security proofs, they still r…
Java deserialization vulnerabilities (JDVs) enable attackers to execute arbitrary code by crafting malicious serialized objects that trigger sequences of method calls (gadget chains) leading to dangerous operations. Existing detection approaches face a fundamental trade-off: stat…
Simulation-based testing of autonomous driving systems (ADS) must uncover realistic and diverse failures in dense, heterogeneous traffic. However, existing search-based seeding methods (e.g., genetic algorithms) struggle in high-dimensional spaces, often collapsing to limited mod…
The dynamics and complexity of cloud-native systems present significant challenges for Root Cause Analysis (RCA). While causality-based RCA methods have shown significant progress in recent years, their practical adoption is fundamentally limited by three intertwined challenges: …
Using data mining to extract frequent code patterns for bug detection has proven effective. However, prior studies have overlooked the prevalence of infrequent ( rare ) patterns, even though violations of such patterns can also lead to bugs. In this paper, we present LTMiner, whi…
Dynamic languages (such as Python and JavaScript) offer flexibility and simplified type handling for programming, but this can also lead to an increase in type-related errors and additional overhead for compile-time type inference. As a result, type inference for dynamic language…
Deep Reinforcement Learning (DRL) agents have been widely adopted across diverse domains to address challenging decision-making problems, such as autonomous driving and robotic control. Given that many of these applications are safety- and security-critical, rigorous testing of D…
Large Language Models (LLMs) can translate natural language (NL) into SQL, enabling non-experts to query databases via conversational interfaces. However, the generated SQL often contains intent-violating hallucinations —queries that are syntactically valid and executable, yet se…
Modern mobile applications have high-resolution user interfaces (UI) and heavy computations, resulting in significant energy consumption and latency, especially on older devices. Precisely measuring the performance of mobile operations (e.g., the number of CPU instructions) and d…
Trusted Execution Environments (TEEs) have become a cornerstone of confidential computing, attracting significant attention from academia and industry. To support secure and scalable application deployment on confidential clouds, TEE containers (Tcons) have been introduced as mid…
Rust is a modern systems programming language that ensures memory safety through unique mechanisms, including ownership, borrowing, and lifetime annotations. These features prevent critical vulnerabilities but also impose strict constraints that many developers find difficult to …
Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate the opportunities of LLMs for just-in-time regression test generation for programs, like parsers, interpreters, or compilers, that take highly structured, h…
The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental yet challenging in automated software engineering due to the semantic gap between issue description …
In low-resource framework development (e.g., HarmonyOS), large language models (LLMs) often lack sufficient pre-training exposure, resulting in poor code generation performance. Although they generally preserve programming logic across languages, they frequently fail on framework…