3,458 papers · page 6 of 173
Large Code Models (LCMs) have demonstrated potential in advancing various code intelligence tasks. However, their effectiveness can be greatly influenced by the quality of the prompts. Current prompt design strategies in code intelligence studies are mostly manually generated, wh…
The growing use of large language models (LLMs) has increased the importance of natural language (NL) in software engineering. However, ambiguity of NL can harm software quality, as unclear problem descriptions may lead to incorrect program generation. Detecting and resolving suc…
Membership inference attacks (MIAs) on code completion models offer an effective way to assess privacy risks by inferring whether a given code snippet was part of the training data. Existing black- and gray-box MIAs rely on expensive surrogate models or manually crafted heuristic…
Effective alert diagnosis is essential for ensuring the reliability of large-scale online service systems. However, on-call engineers are often burdened with manually inspecting massive volumes of logs to identify root causes. While various automated tools have been proposed, the…
Zero-knowledge proof (ZKP) circuits implemented in programming languages like Circom are fundamental to blockchain and privacy-preserving applications. These code often suffer from constraint-related issues where constraints fail to accurately specify intended computations. While…
Issue solving aims to generate patches to fix re-ported issues in real-world code repositories according to issue descriptions. Issue localization forms the basis for accurate issue solving. Recently, large language model (LLM) based issue localization methods have demonstrated s…
The PHP interpreter, powering over 70% of web-sites on the internet, plays a crucial role in web development. Existing approaches to finding bugs in PHP primarily focus on detecting explicit security issues through crashes or sanitizer-based oracles, but fail to identify logic bu…
Repository-level code completion remains a challenging task for existing code large language models (code LLMs) due to their limited understanding of repository-specific context and domain knowledge. While retrieval-augmented generation (RAG) approaches have shown promise by retr…
Static analyzers are vital to ensure software quality, but often produce false alarms. In this paper, we focus on the challenging task, directly refining defective static detection rules in the analyzer with Large Language Models to mitigate false positives/negatives fundamentall…
In recent years, large language models (LLMs) have emerged as powerful tools to assist developers in various coding tasks, including the challenging domain of vulnerability repair. While these models have demonstrated significant potential in generating patches for software vulne…
Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks. Software engineers increasingly rely on LLMs to assess whether system code implementation satisfy task requirements, there…
Software companies are establishing new data centers to enhance software performance with lower response times as well as meet security requirements for storing local user data. ByteDance also has a strong need to deploy its services to new data centers worldwide quickly and with…
Quantum programs are designed to run on quantum computers, leveraging quantum circuits to solve problems that are intractable for classical machines. As quantum computing advances, ensuring the reliability of quantum programs has become increasingly important. This paper introduc…
In the context of mobile apps, bug report management tasks have been shown to be among the most time-consuming and intellectually intensive software maintenance activities. As such, researchers have developed tools to automate the reproduction, validation, and localization of rep…
Many enterprise systems including large-scale deployment platforms like Ansible provide a declarative user interface through programming languages like JavaScript Object Notation (JSON). These systems maintain integrity through validation rules, typically enforced via JSON schema…
Dependency bloat is a persistent challenge in Python projects, which increases maintenance costs and security risks. While numerous tools exist for detecting unused dependencies in Python, removing these dependencies across the source code and configuration files of a project req…
Ensuring complete software requirements specifications (SRS) is critical to preventing costly downstream errors. We present a tool that ensembles three complementary LLMs—DeepSeek Chat, GPT-4o Mini, and Claude Sonnet 4—to detect and suggest remedies for missing requirements. The …
Does the choice of programming language affect energy consumption? Previous highly visible studies have established associations between certain programming languages and energy consumption. A causal misinterpretation of this work has led academics and industry leaders to use or …
Open source software (OSS) underpins modern software infrastructure, yet many projects struggle with long-term sustainability. We introduce OSSPREY, an AI-powered platform that can predict the sustainability of any GitHub-hosted project. OSSPREY collects longitudinal socio-techni…
Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development…