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Haodi Qi

dblp:259/6962 · DBLP profile ↗
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4ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-author · 1 since 2021Artificial intelligence and machine learning · 2 · 1 first-authorSoftware engineering, systems software and programming languages · 2 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software testing · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
software testing tools		 0.112020
BugsInPy: a database of existing bugs in Python programs to enable controlled testing and debugging studies · ESEC/SIGSOFT FSE 2020
YearPublicationVenuePosition
2023 NICHE: A Curated Dataset of Engineered Machine Learning Projects in Python
abstract
Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This dataset can help researchers understand the practices that are adopted in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.
Ratnadira Widyasari, Zhou Yang 0003, Ferdian Thung, Sheng Qin Sim, Fiona Wee, Camellia Lok, Jack Phan, Haodi Qi, Constance Tan, Qijin Tay, David Lo 0001
 MSR8
2020 Social Media Analytics: A Case Study of Singapore General Election 2020
abstract
The 2020 Singaporean General Election (GE2020) was a general election held in Singapore on July 10, 2020. In this study, we present an analysis on social conversations about GE2020 during the election period. We analyzed social conversations from popular platforms such as Twitter, HardwareZone, and TR Emeritus.
Sebastian Zhi Tao Khoo, Leong Hock Ho, Ee Hong Lee, Danston Kheng Boon Goh, Zehao Zhang, Swee Hong Ng, Haodi Qi, Kyong Jin Shim
 IEEE BigData7
2020 BugsInPy: a database of existing bugs in Python programs to enable controlled testing and debugging studies
abstract
The 2019 edition of Stack Overflow developer survey highlights that, for the first time, Python outperformed Java in terms of popularity. The gap between Python and Java further widened in the 2020 edition of the survey. Unfortunately, despite the rapid increase in Python's popularity, there are not many testing and debugging tools that are designed for Python. This is in stark contrast with the abundance of testing and debugging tools for Java. Thus, there is a need to push research on tools that can help Python developers.
Ratnadira Widyasari, Sheng Qin Sim, Camellia Lok, Haodi Qi, Jack Phan, Qijin Tay, Constance Tan, Fiona Wee, Jodie Ethelda Tan, Yuheng Yieh, Brian Goh, Ferdian Thung, Hong Jin Kang, Thong Hoang, David Lo 0001, Eng Lieh Ouh
 ESEC/SIGSOFT FSE4
2019 Tracking Political Events in Social Media: A Case Study of Hong Kong Protests
abstract
In this study, we analyze social conversations about Hong Kong Protests, a series of events that were widely seen and debated in social media in 2019. Our system collects data from Twitter and Reddit using their APIs. It performs sentiment analysis, and the analysis results show changes in public sentiment around major events. Social network analysis reveals influencers - users that are in the center of social conversations. Our interactive Tableau dashboard allows the user to easily monitor live social conversations about Hong Kong protests.
Haodi Qi, Wende Bu, Chengzi Zhang, Kyong Jin Shim
 IEEE BigData1