3,458 papers · page 17 of 173
Frequent software updates lead to log evolution, posing generalization challenges for current log anomaly detection. Traditional log anomaly detection research focuses on using small deep learning models (SMs), but these models inherently lack generalization due to their closed-w…
Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance on the design-to-code task, i.e., generating UI code from UI mock-ups. However, existing benchmarks only contain static web pages for evaluation and ignore the dynamic interaction, limiting the prac…
Coincidental correctness (CC) refers to test cases that execute faulty code but still produce excepted outputs. This phenomenon introduces noise into the data of software testing-related tasks. As demonstrated in the literature, CC has negative impact on test suite reduction, tes…
To automatically tune configurations for the best possible system performance (e.g., runtime or throughput), much work has been focused on designing intelligent heuristics in a tuner. However, existing tuner designs have mostly ignored the presence of complex performance requirem…
Continuous Integration and Deployment (CI/CD) pipelines are critical to modern software engineering, yet diagnosing and resolving their failures remains complex and labor-intensive. We present LogSage, the first end-to-end LLM-powered framework for root cause analysis (RCA) and a…
Software testing plays a crucial role in software engineering, ensuring the reliability and correctness of evolving systems. Well-maintained test suites are essential for ensuring software quality. However, in modern development cycles that emphasize rapid feature iteration, the …
Binary Software Composition Analysis (BSCA) is a technique for identifying the versions of third-party libraries (TPLs) used in compiled binaries, thereby tracing the dependencies and vulnerabilities of software components without access to their source code. However, existing BS…
Essential for debugging compilers and interpreters, existing reduction tools face a fundamental trade-off. Language-Specific reducers, such as C-Reduce and ddSMT, offer highly effective reductions but require substantial engineering effort for each target language. Conversely, la…
Protocol reverse engineering (PRE) aims to infer the protocol formats of unknown protocols. Existing techniques, whether Network-Trace based or Execution-Trace based methods, face two main limitations: a reliance on the quality and scale of traffic datasets, which often leads to …
Large Language Models (LLMs) have rapidly gained popularity, transforming research and industry. To support their adoption, LLM agent workflow orchestration frameworks (hereinafter referred to as LLM agent frameworks) like LangChain have become essential for building advanced app…
Ensuring the safety and reliability of Cyber-Physical Systems (CPS) is critical, particularly in safety-critical domains such as automotive and aerospace. Fault Injection (FI) is a well-established technique for testing system resilience, but current FI tools often face challenge…
As invisible image watermarking gains importance for verifying AI-generated content, consistency and reproducibility remain major challenges due to the diverse methods, datasets, attacks, and metrics.We aim to provide a flexible, extensible, and user-friendly framework that enabl…
As software development increasingly relies on external collaboration, organizations face new risks of intellectual property leakage beyond traditional concerns about deployed software. Even when the source code is protected, adversaries may infer sensitive internal program speci…
Guardrails are critical for the safe deployment of Large Language Models (LLMs)-powered software. Unlike traditional rule-based systems with limited, predefined input-output spaces that inherently constrain unsafe behavior, LLMs enable open-ended, intelligent interactions—opening…
The distributed deep learning training process within large-scale clusters serves as the foundation of contemporary artificial intelligence. However, its inherent characteristics make it particularly sensitive to stragglers, specifically the presence of slow workers, which can si…
Rust is a programming language that combines memory safety and low-level control, providing C-like performance while guaranteeing the absence of undefined behaviors by default. Rust’s growing popularity has prompted research on correct and idiomatic transpiling of existing code-b…
Type errors in Python often lead to runtime failures, posing significant challenges to software reliability and developer productivity. Existing static analysis tools aim to detect such errors without execution but frequently suffer from high false positive rates. Recently, unit …
Among the programming languages for Programmable Logic Controllers (PLCs), Structured Text (ST) is widely adopted for industrial automation due to its expressiveness and flexibility. However, major vendors implement ST with proprietary extensions and hardware-specific libraries -…
Current call graph generation methods for ArkTS, a new programming language for OpenHarmony, exhibit precision limitations when supporting advanced static analysis tasks such as data flow analysis and vulnerability pattern detection, while the workflow of traditional JavaScript(J…
Recent advances in large language models (LLMs) have enabled promising performance in unit test generation through in-context learning (ICL). However, the quality of in-context examples significantly influences the effectiveness of generated tests—poorly structured or semanticall…