Bowen Xu

Works (9)

Updated: January 4th, 2025 05:02

2024 journal article

<i>PTM4Tag+</i>: Tag recommendation of stack overflow posts with pre-trained models

EMPIRICAL SOFTWARE ENGINEERING, 30(1).

By: J. He *, B. Xu n, Z. Yang *, D. Han *, C. Yang *, J. Liu *, Z. Zhao *, D. Lo *

author keywords: Tag recommendation; Stack overflow; Pre-trained models; Transformer
Source: Web Of Science
Added: December 2, 2024

2024 article

A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback

PROCEEDINGS OF THE 1ST ACM INTERNATIONAL CONFERENCE ON AI-POWERED SOFTWARE, AIWARE 2024, pp. 103–111.

By: U. Kulsum n, H. Zhu *, B. Xu n & M. d'Amorim n 

author keywords: Automated Vulnerability Repair; Large Language Models
Sources: Web Of Science, ORCID, NC State University Libraries
Added: July 15, 2024

2024 journal article

An empirical study on the effectiveness of large language models for SATD identification and classification

EMPIRICAL SOFTWARE ENGINEERING, 29(6).

By: M. Sheikhaei, Y. Tian, S. Wang & B. Xu*

author keywords: Self-admitted technical debt (SATD); SATD identification; SATD classification; Large language models; Fine tuning; In-context learning
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: Web Of Science
Added: October 14, 2024

2024 journal article

Leveraging Large Language Model for Automatic Patch Correctness Assessment

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 50(11), 2865–2883.

By: X. Zhou *, B. Xu n, K. Kim *, D. Han *, H. Nguyen*, T. Le-Cong *, J. He *, B. Le *, D. Lo *

author keywords: Computer bugs; Codes; Task analysis; Large language models; Feature extraction; Training; Manuals; Automatic patch correctness assessment; large language models of code; in-context learning
Source: Web Of Science
Added: December 23, 2024

2024 journal article

Stealthy Backdoor Attack for Code Models

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 50(4), 721–741.

By: Z. Yang *, B. Xu n, J. Zhang, H. Kang *, J. Shi *, J. He *, D. Lo *

author keywords: Adversarial attack; data poisoning; backdoor attack; pre-trained models of code
Source: Web Of Science
Added: May 20, 2024

2023 article

Are We Ready to Embrace Generative AI for Software Q&A?

2023 38TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING, ASE, pp. 1713–1717.

By: B. Xu n, T. Nguyen *, T. Le-Cong *, T. Hoang *, J. Liu *, K. Kim *, C. Gong *, C. Niu * ...

TL;DR: It is suggested that human-written and ChatGPT-generated answers are semantically similar, however, human- written answers outperform ChatG PT-generated ones consistently across multiple aspects, specifically by 10% on the overall score. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: January 29, 2024

2023 article

CCBERT: Self-Supervised Code Change Representation Learning

2023 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE AND EVOLUTION, ICSME, pp. 182–193.

By: X. Zhou *, B. Xu n, D. Han *, Z. Yang *, J. He * & D. Lo *

TL;DR: CCBERT (Code Change BERT), a new Transformer-based pre-trained model that learns a generic representation of code changes based on a large-scale dataset containing massive unlabeled code changes, is proposed. (via Semantic Scholar)
Source: Web Of Science
Added: February 26, 2024

2023 journal article

Representation Learning for Stack Overflow Posts: How Far Are We?

ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 33(3).

By: J. He *, X. Zhou *, B. Xu n, T. Zhang *, K. Kim *, Z. Yang *, F. Thung *, I. Irsan *, D. Lo *

author keywords: Stack Overflow; transformers; pre-trained models
Source: Web Of Science
Added: May 13, 2024

2023 article

The Devil is in the Tails: How Long-Tailed Code Distributions Impact Large Language Models

2023 38TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING, ASE, pp. 40–52.

By: X. Zhou *, K. Kim *, B. Xu n, J. Liu *, D. Han * & D. Lo *

TL;DR: An exploratory study on the distribution of SE data found that such data usually follows a skewed distribution where a small number of classes have an extensive collection of samples, while a large number of Classes have very few samples, which has a substantial impact on the effectiveness of LLMs for code. (via Semantic Scholar)
Source: Web Of Science
Added: January 29, 2024

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