Bowen Xu

Bowen Xu

Research Scientist
SOAR Lab
School of Computing and Information Systems
Singapore Management University
maxxbw54@gmail.com
[Google Scholar] [Github]

Research Focus

My research interests span software engineering, machine learning, and program analysis. My primary research interests are:

Large Language Model of Code

Empirical Studies of Pre-trained Language Models of Code [SANER-22] [ICPC-22]
Code Representation Enhancement [SANER-22] [FSE-22]
Model Compression [ASE-22]

Software Text Analytics for Software Automation

Interconnected Software Data Prediction [ESEM-18] [ASE-16]
Representation Learning of Software Data [TSE-21]
Sentiment Analysis for Software Engineering [ICSME-20]
Biases in Bug Report Analytics [TOSEM-22]

Understand and Assist Software Practice

Code Reuse and Re-implementation [EMSE-19]
Automatic Code Review [PAKDD-19]
Automatic Answer Summarization Tool for Technical Query [ICSE-23] [ASE-22] [ESEC/FSE-19] [ASE-17]
Automatic Relevant Question Retrival Tool for Technical Query [ESEC/FSE-17] [EMSE-17] [MSR-16]

News

Demonstrations Track

Generation-based Code Review Automation: How Far Are We?

TECHSUMBOT: A Stack Overflow Answer Summarization Tool for Technical Query

Curiosity-Driven and Victim-Aware Adversarial Policies

Duplicate Bug Report Detection: How Far Are We?

Compressing Pre-trained Models of Code into 3 MB

Answer Summarization for Technical Queries: Benchmark and New Approach

How to Better Utilize Code Graphs in Semantic Code Search?

[Call For Paper]

the 6th edition of the MaLTeSQuE

PTM4Tag: Sharpening Tag Recommendation of Stack Overflow with Pre-trained Models

Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?

Can Identifier Splitting Improve Open-Vocabulary Language Model of Code?

Post2Vec: Learning Distributed Representations of Stack Overflow Posts

Bio

Bowen Xu is a Research Scientist (Post-Doc) in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU). He got his PhD in 2022 from SCIS at SMU, supervised by Prof. David Lo (IEEE Fellow). His research area is software engineering. Particularly, his research interests lie in machine learning and software engineering. The aims of his works are to improve software quality and developers’ productivity. He has published his works in both top-tier software engineering conferences (ICSE, FSE, ASE) and journals (TSE, TOSEM, EMSE). One of his works is nominated for ACM SIGSOFT Distinguished Paper Award (top 10% among all accepted submissions) for ASE 2022 (Core A*). Besides, another piece of his works published in ESEM 2018 (Core A) has won the Highly Commended Full Paper Award (the 2nd best among all accepted submissions). Two of his works published in ICPC 2022 (Core A) and MSR 2016 (Core A) with high scores have been invited extended to the journal EMSE.