Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Nguyen, Tien N."'
Predicting program behavior without execution is a crucial and challenging task in software engineering. Traditional models often struggle to capture the dynamic dependencies and interactions within code. This paper introduces a novel machine learnin
Externí odkaz:
http://arxiv.org/abs/2408.02816
Regression testing of software is a crucial but time-consuming task, especially in the context of user interface (UI) testing where multiple microservices must be validated simultaneously. Test case prioritization (TCP) is a cost-efficient solution t
Externí odkaz:
http://arxiv.org/abs/2408.00705
Software migration is garnering increasing attention with the evolution of software and society. Early studies mainly relied on handcrafted translation rules to translate between two languages, the translation process is error-prone and time-consumin
Externí odkaz:
http://arxiv.org/abs/2407.07472
Code Large Language Models (CodeLLMs) have demonstrated impressive proficiency in code completion tasks. However, they often fall short of fully understanding the extensive context of a project repository, such as the intricacies of relevant files an
Externí odkaz:
http://arxiv.org/abs/2403.06095
Programming languages are essential tools for developers, and their evolution plays a crucial role in supporting the activities of developers. One instance of programming language evolution is the introduction of syntactic sugars, which are additiona
Externí odkaz:
http://arxiv.org/abs/2402.01079
Pre-trained language models for code (PLMCs) have gained attention in recent research. These models are pre-trained on large-scale datasets using multi-modal objectives. However, fine-tuning them requires extensive supervision and is limited by the s
Externí odkaz:
http://arxiv.org/abs/2304.01228
We propose a novel method for code summarization utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet. HCRs effectively capture essential code features at lexical, syntactic, and semantic levels by abstracting c
Externí odkaz:
http://arxiv.org/abs/2205.15479
The existing deep learning (DL)-based automated program repair (APR) models are limited in fixing general software defects. % We present {\tool}, a DL-based approach that supports fixing for the general bugs that require dependent changes at once to
Externí odkaz:
http://arxiv.org/abs/2205.01859
Despite the successes of machine learning (ML) and deep learning (DL) based vulnerability detectors (VD), they are limited to providing only the decision on whether a given code is vulnerable or not, without details on what part of the code is releva
Externí odkaz:
http://arxiv.org/abs/2106.10478
In this paper, we propose DeepRL4FL, a deep learning fault localization (FL) approach that locates the buggy code at the statement and method levels by treating FL as an image pattern recognition problem. DeepRL4FL does so via novel code coverage rep
Externí odkaz:
http://arxiv.org/abs/2103.00270