VLDB 2026 Research / reviewers in the wild / expert
Huizi Hao
dblp:228/7139
· DBLP profile ↗
3ranked-venue papers
1as first author
2since 2021 · last 2025
0009-0006-8603-3840ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 first-author · 2 since 2021Databases, data management, data science and information retrieval · 2 · 1 since 2021Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Understanding Abandonment and Slowdown Dynamics in the Maven EcosystemabstractThe sustainability of libraries is critical for modern software development, yet many libraries face abandonment, posing significant risks to dependent projects. This study explores the prevalence and patterns of library abandonment in the Maven ecosystem. We investigate abandonment trends over the past decade, revealing that approximately one in four libraries fail to survive beyond their creation year. We also analyze the release activities of libraries, focusing on their lifespan and release speed, and analyze the evolution of these metrics within the lifespan of libraries. We find that while slow release speed and relatively long periods of inactivity are often precursors to abandonment, some abandoned libraries exhibit bursts of high frequent release activity late in their life cycle. Our findings contribute to a new understanding of library abandonment dynamics and offer insights for practitioners to identify and mitigate risks in software ecosystems. Kazi Amit Hasan, Jerin Yasmin, Huizi Hao, Yuan Tian 0008, Safwat Hassan, Steven H. H. Ding |
MSR | 3 |
| 2024 | An empirical study on developers' shared conversations with ChatGPT in GitHub pull requests and issues
Huizi Hao, Kazi Amit Hasan, Hong Qin 0014, Marcos Macedo, Yuan Tian 0008, Steven H. H. Ding, Ahmed E. Hassan |
Empir. Softw. Eng. | 1 |
| 2018 | Social Network Mining for Recommendation of Friends Based on Music InterestsabstractWith the rapid development of technology and software, social media have become a necessity in our daily lives as it is a way for people to keep in touch with friends and share about current events. Some of the most popular social media and social networking sites that people use include Facebook, Instagram, Snapchat, and Twitter. Finding compatible persons to be friends on social media can be a challenge as many of the people recommended to the user by social media are people who are already friends with them or have been followed. However, when users are looking for friends, the real concern is whether they have common interests or hobbies with each other and whether they often interact with one another. In this paper, we propose friend recommendation algorithms revolving around music interests and interactions in social media. Chenxi Fan, Huizi Hao, Carson K. Leung, Leslie Yu Sun, Jennifer Tran |
ASONAM | 2 |