软件工程
Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories. Bug reproduction tests (BRTs) are an important building block…
Quantum computing is increasingly explored for software engineering (SE) optimization, but translating natural-language (NL) task-level requirements into executable quantum applications still demands substantial quantum and programming…
Delta debugging provides an automatic way to minimize a program input while preserving a certain property. However, its effectiveness fundamentally relies on the availability of test oracles to determine whether a reduced input still…
AI coding agents increasingly rely on skills: structured context bundles, typically a SKILL.md file with a YAML header and Markdown body, loaded on demand for domain knowledge, workflows, and scripts. Public registries such as skills.sh now…
The landscape of automated formal verification is populated by techniques that make prominently different trade-offs: some focus on expressiveness and precision, supporting the verification of complex properties; others favor scalability…
Frontier foundation models have changed the math on vulnerability discovery, but the bigger challenge is how the remediation side keeps up. Despite recent progresses in Automated Vulnerability Repair (AVR), current solutions struggle to…
Microservice availability is commonly assessed by fault injection and chaos experiments, but such experiments are costly, operationally risky, and difficult to repeat for every architectural change. Distributed tracing and deployment…
Large Language Models have emerged as programming assistants. However, the efficacy of code generation is constrained by the quality of input requirements, which are frequently ambiguous, incomplete, or underspecified. While LLMs excel at…
LLVM is a widely used compiler infrastructure whose scale and complexity make issue resolution labor-intensive and challenging. Although large language models (LLMs) have recently achieved remarkable success in issue resolution, their…
A growing class of tools recovers a program from observations of its behavior using an untrusted generator, a neural model or a search, that proposes candidates with no correctness guarantee. We study how to make such recovery trustworthy,…
Terminology such as "whitelist/blacklist," "master/slave," "man-hours," or "dummy value" has long been part of the technical vocabulary used in software artifacts, including source code, version histories, and documentation. In recent…
Block-based languages such as Scratch let beginners assemble interactive programs from sprites and scripts. These programs are concurrent in practice: green-flag scripts, broadcasts, and clones run as cooperatively scheduled threads over…
Debugging the Linux kernel remains a formidable challenge due to its vast codebase, complex architecture, and low-level programming intricacies. Effective fault localization (FL) is thus essential for efficient kernel debugging and…
As Artificial Intelligence(AI)-based applications take off, a clear understanding of AI patterns can uplift the quality of AI applications. Many AI patterns have been proposed in the literature; however, their prevalence in real-life code…
Deep learning (DL) frameworks are critical AI infrastructures that often hide bugs with serious security implications. While dynamic approaches such as fuzzing are effective in uncovering these bugs, they require real test execution and…
Empirical quantum-software papers often report that one compiler, optimizer, backend, or ansatz outperforms another. Such comparisons are not properties of a tool alone: they can change with benchmark scope, circuit construction,…
Mutation testing is a powerful technique for ensuring software quality. However, the presence of equivalent mutants introduces unnecessary costs and biases, limiting its practical effectiveness. Although numerous equivalent mutant detection…
Social coding platforms such as GitHub host millions of repositories, yet many suffer from high mortality rates. Despite this, several survival factors remain poorly understood. Human capital is widely recognized as essential. Social…
Large language models (LLMs) are increasingly applied to requirements engineering (RE) tasks, yet the prompts guiding them are typically designed manually through trial and error, yielding inconsistent and suboptimal results. Automated…
Enterprise adoption of LLM agents requires model selection methods that balance quality, reliability, safety, latency, and cost. Evaluation-Driven Development and Operations (EDDOps) positions evaluation as a continuous governing function…