Wei Fu

Works (6)

Updated: August 21st, 2023 08:59

2022 article

Predicting health indicators for open source projects (using hyperparameter optimization)

Xia, T., Fu, W., Shu, R., Agrawal, R., & Menzies, T. (2022, June 22). Empirical Software Engineering, Vol. 27.

By: T. Xia n, W. Fu n, R. Shu n, R. Agrawal  n & T. Menzies n 

author keywords: Hyperparameter optimization; Project health; Machine learning
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Software System Performance and Reliability
TL;DR: This is the largest study yet conducted, using recent data for predicting multiple health indicators of open-source projects, and finds that traditional estimation algorithms make many mistakes. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: July 5, 2022

2018 article

Applications of psychological science for actionable analytics

Chen, D., Fu, W., Krishna, R., & Menzies, T. (2018, October 26). ESEC/FSE'18: PROCEEDINGS OF THE 2018 26TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 456–467.

By: D. Chen n, W. Fu n, R. Krishna  n & T. Menzies n 

author keywords: Decision trees; heuristics; software analytics; psychological science; empirical studies; defect prediction
topics (OpenAlex): Software Engineering Research; Advanced Software Engineering Methodologies; Evolutionary Algorithms and Applications
TL;DR: Assessment of Fast-and-Frugal Trees for software analytics finds that FFTs are remarkably effective in that their models are very succinct (5 lines or less describing a binary decision tree) while also outperforming result from very recent, top-level, conference papers. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 25, 2019

2018 article

Data-driven search-based software engineering

Nair, V., Agrawal, A., Chen, J., Fu, W., Mathew, G., Menzies, T., … Yu, Z. (2018, May 28). 2018 IEEE/ACM 15TH INTERNATIONAL CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR), pp. 341–352.

By: V. Nair n, A. Agrawal n, J. Chen n, W. Fu n, G. Mathew n, T. Menzies n , L. Minku*, M. Wagner*, Z. Yu n 

topics (OpenAlex): Software Engineering Research; Software Testing and Debugging Techniques; Software Reliability and Analysis Research
TL;DR: It is argued that combining these two fields is useful for situations which require learning from a large data source or when optimizers need to know the lay of the land to find better solutions, faster. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 4, 2019

2017 article

Heterogeneous Defect Prediction

Nam, J., Fu, W., Kim, S., Menzies, T., & Tan, L. (2017, June 27). IEEE Transactions on Software Engineering, Vol. 44, pp. 874–896.

By: J. Nam *, W. Fu n, S. Kim *, T. Menzies n  & L. Tan *

author keywords: Defect prediction; quality assurance; heterogeneous metrics; transfer learning
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Software Engineering Techniques and Practices
Sources: Web Of Science, ORCID, NC State University Libraries
Added: October 16, 2018

2016 article

Too much automation? the bellwether effect and its implications for transfer learning

Krishna, R., Menzies, T., & Fu, W. (2016, August 25). 2016 31ST IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE), pp. 122–131.

By: R. Krishna n, T. Menzies n  & W. Fu n

author keywords: Defect Prediction; Data Mining; Transfer learning
topics (OpenAlex): Software Engineering Research; Machine Learning and Algorithms; Machine Learning and Data Classification
TL;DR: This bellwether method is a useful (and very simple) transfer learning method; “bellwethers” are a baseline method against which future transfer learners should be compared; sometimes, when building increasingly complex automatic methods, researchers should pause and compare their supposedly more sophisticated method against simpler alternatives. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2016 journal article

Tuning for software analytics: Is it really necessary?

Information and Software Technology, 76, 135–146.

By: W. Fu n, T. Menzies n  & X. Shen n 

author keywords: Defect prediction; CART; Random forest; Differential evolution; Search-based software engineering
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Software Testing and Debugging Techniques
TL;DR: This paper finds that it is no longer enough to just run a data miner and present the result without conducting a tuning optimization study, and that standard methods in software analytics need to change. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, Crossref
Added: August 6, 2018

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