2,847 papers · page 8 of 143
Delta debugging is a fundamental technique for automatically minimizing failure-inducing inputs. ProbDD improves ddmin via probability-guided search, and Weighted Delta Debugging (WDD) further incorporates token-based weighting to mitigate size disparities. However, token counts …
Visual language model (VLM) agents show great promise in automating graphical user interface (GUI) testing against requirements in natural language. However, the probabilistic nature of language models can have inherent hallucinations. Therefore, given a detected inconsistency be…
False-positive bug reports represent a significant yet underexplored challenge in the development and maintenance of the Linux kernel. They occur when correct system behavior is mistakenly flagged as a defect, consuming developer effort without leading to actual code improvements…
Automatically generated code is gaining traction recently, owing to the prevalence of Large Language Models (LLMs). Further, the AlphaProof initiative has demonstrated the possibility of using AI for general mathematical reasoning. Reasoning about computer programs (software) can…
Binary code analysis plays an essential role in cybersecurity, facilitating reverse engineering to reveal the inner workings of programs in the absence of source code. Traditional approaches, such as static and dynamic analysis, extract valuable insights from stripped binaries, b…
Code summarization plays a vital role in program comprehension and software maintenance by generating natural language descriptions to summarize the semantics of code. While Large Language Models (LLMs) have shown remarkable performance in this area, recent empirical studies reve…
Collaborative platforms such as Google Workspace, Microsoft Teams, and Zoom increasingly rely on third-party applications (referred to as plugins) to extend their core functionalities, with AI-assisted plugins emerging as a key driver of productivity. Despite their popularity and…
Automatically generating bug reproduction tests (BRT) from issue descriptions is crucial for facilitating software maintenance. Large Language Model (LLM)-based approaches have shown great potential for this task. Their effectiveness heavily relies on retrieving high-quality cont…
LLM-based mutation testing is a promising testing technology, but existing approaches typically rely on a fixed set of mutants as few-shot examples or none at all. This can result in generic low-quality mutants, missed context-specific mutation patterns, substantial numbers of re…
Static program analysis plays an essential role in program optimization, bug detection, and debugging. However, reliance on compilation and limited customization hinder its adoption in the real world. This paper presents a compositional neuro-symbolic approach named NESA that fac…
Log parsing serves as the fundamental step in log analysis, splitting logs into constant templates and dynamic variables. While recent semantic-based parsers leveraging LLM have shown superior generalizability over prior syntax-based methods, their effectiveness is critically dep…
By early 2025, AI code assistants had evolved into sophisticated collaborators capable of generating, explaining, reviewing, and modifying substantial portions of a software system. In February 2025, as we were delivering an upper-level undergraduate course on Software Engineerin…
Programmable Logic Controllers (PLCs) lack built-in security mechanisms, and their critical role in industrial control systems makes them prime targets for cyberattacks. Next-generation PLCs increasingly adopt embedded virtualization to partition functional domains and to integra…
Recent large language models (LLMs) have shown strong performance on software engineering tasks, yet most existing benchmarks evaluate code reasoning at the function level, where all relevant information is localized. This setting fails to reflect real-world development, which re…
Code editing constitutes a fundamental practice in software development, wherein developers modify existing codebases according to natural language requirements. Accurate code editing necessitates a comprehensive understanding of both the existing codebase and the modification re…
Building software that is correct by construction is a long-standing goal in software engineering, as it ensures reliability during design and development rather than after deployment. Formal methods realize this vision by enabling the expression of system behavior and requiremen…
Jailbreak attacks have been regarded as a crucial threat to LLM-powered software systems. Recent studies indicate the existence of a steering vector within models' internal activations, which can adjust a model's propensity to reject user requests, and thus is regarded as an effe…
Secure multi-party computation (MPC) enables privacy-preserving computations using secret data, with applications ranging from health care and finance to machine learning and blockchains. MPC compilers translate high-level function descriptions to the low-level representations re…
Large Language Models (LLMs) have achieved tremendous success in various tasks, yet concerns about their safety and security have emerged. In particular, they pose risks of generating harmful content and are vulnerable to jailbreaking attacks, creating unaddressed security issues…
Modern software usually exposes a large number of configuration options to the user, giving rise to enormous configuration spaces in practice. Appropriate choices for these options dramatically influence the performance of the software (throughput, memory consumption, execution t…