3,458 papers · page 27 of 173
Recent studies indicate that traditional techniques for understanding code changes are not as effective as techniques that directly prompt language models (LMs). However, current LM-based techniques heavily rely on expensive, large LMs (LLMs) such as GPT-4 and Llama-13b, which ar…
Automated Program Repair (APR) aims to fix bugs by generating patches. And existing work has demonstrated that "pre-training and fine-tuning" paradigm enables Large Language Models (LLMs) improve fixing capabilities on APR. However, existing work mainly focuses on Full-Model Fine…
Adversarial code examples are important to investigate the robustness of deep code models. Existing work on adversarial code example generation has shown promising results yet still falls short in practical applications due to either the high number of model invocations or the li…
The success of language models in code assistance has spurred the proposal of repository-level code completion as a means to enhance prediction accuracy, utilizing the context from the entire codebase. However, this comprehensive context comes at a cost: while it enhances model p…
Web applications are crucial infrastructures in the modern society, which have high demand of reliability and security. However, their frontend can be manipulable by the clients (e.g., the frontend code can be modified to bypass some validation steps), which incurs the runtime an…
The automotive industry is undergoing rapid growth driven by advancements such as electrification, necessitating efficient software development methodologies. While model-based system engineering is prevalent in automotive software development, its application is typically confin…
Software deployment misconfiguration often happens and has been one of the major causes of deployment failures that give rise to service interruptions. However, there is currently no existing approach to automatically repairing deployment failures. We propose DeployFix, which aut…
Data wrangling is a critical yet often labor-intensive process, essential for transforming raw data into formats suitable for downstream tasks such as machine learning or data analysis. Traditional data wrangling methods can be time-consuming, resource-intensive, and prone to err…
HarmonyOS NEXT is a distributed operating system developed to support HarmonyOS native apps. To support the new and independent Harmony ecosystem, developers are required to migrate their applications from Android to HarmonyOS. However, HarmonyOS utilizes ArkTS, a superset of Typ…
The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on reposito…
Compilers are one of the most fundamental software systems. A large number of software systems rely on compilers for execution. Compiler bugs can significantly hinder software developers from diagnosing issues within their software. Therefore, it is essential to ensure the correc…
Duck typing is a flexible programming style in dynamic languages, enabling the achievement of complex behaviors using less code. The use of duck typing is currently widespread; however, the question is whether its use in code is truly safe. In fact, improper use of duck typing ma…
GUI test repair is important to detect and repair obsolete test events to enhance the reliability of the software. From an interaction design perspective, designers usually design different routes to facilitate users to reach the same functional entry point. Based on this, we pro…
Software correctness is crucial, with unit testing playing an indispensable role in the software development lifecycle. However, creating unit tests is time-consuming and costly, underlining the need for automation. Leveraging Large Language Models (LLMs) for unit test generation…
Interactive theorem provers such as Coq are powerful tools to formally guarantee the correctness of software. However, using these tools requires significant manual effort and expertise. While Large Language Models (LLMs) have shown promise in automatically generating informal pr…
Recent studies proposed to leverage large language models (LLMs) with In-Context Learning (ICL) to handle code intelligence tasks without fine-tuning. ICL employs task instructions and a set of examples as demonstrations to guide the model in generating accurate answers without u…
When using large language models (LLMs) for code translation of complex software, numerous compilation and runtime errors can occur due to insufficient context awareness. To address this issue, this paper presents a code translation method based on call graphs and bridged debugge…
Recent Large Language Models (LLMs) have demonstrated significant capabilities in generating code snippets directly from problem statements. This increasingly automated process mirrors traditional human-led software development, where code is often written in response to a requir…
This paper briefly outlines a high-level architecture of a context-aware ethics-based negotiation approach in which autonomous systems utilize user ethical profiles, together with contextual factors and user status, to control their autonomy while collaboratively negotiating to r…
In this paper, we present MADE-WIC, a large dataset of functions and their comments with multiple annotations for technical debt and code weaknesses leveraging different state-of-the-art approaches. It contains about 860K code functions and more than 2.7M related comments from 12…