Xueqi Yang

Works (9)

Updated: January 4th, 2025 05:02

2024 article

Fuzzy support vector regressions for short-term load forecasting

Luo, J., Zheng, Y., Hong, T., Luo, A., & Yang, X. (2024, June 14). Fuzzy Optimization and Decision Making.

By: J. Luo *, Y. Zheng *, T. Hong *, A. Luo* & X. Yang n

author keywords: Electric load forecasting; Fuzzy membership; Support vector regression; Fuzzy SVR; Quantile regression
topics (OpenAlex): Energy Load and Power Forecasting; Grey System Theory Applications; Hydrological Forecasting Using AI
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
13. Climate Action (OpenAlex)
Source: Web Of Science
Added: August 19, 2024

2024 article

SparseCoder: Advancing source code analysis with sparse attention and learned token pruning

Yang, X., Jakubowski, M., Kang, L., Yu, H., & Menzies, T. (2024, December 10). Empirical Software Engineering, Vol. 30.

By: X. Yang n, M. Jakubowski *, L. Kang *, H. Yu* & T. Menzies n 

author keywords: Vulnerability detection; Long sequence code analysis; Transformer; Efficient fining-tuning; Attention mechanism
topics (OpenAlex): Software Engineering Research; Software Testing and Debugging Techniques; Advanced Malware Detection Techniques
Sources: Web Of Science, NC State University Libraries
Added: December 23, 2024

2023 article

How to Find Actionable Static Analysis Warnings: A Case Study With FindBugs

Yedida, R., Kang, H. J., Tu, H., Yang, X., Lo, D., & Menzies, T. (2023, January 9). IEEE Transactions on Software Engineering, Vol. 49, pp. 2856–2872.

By: R. Yedida n , H. Kang *, H. Tu *, X. Yang n, D. Lo * & T. Menzies n 

Contributors: R. Yedida n , H. Kang *, H. Tu *, X. Yang n, D. Lo * & T. Menzies n 

author keywords: Codes; Computer bugs; Static analysis; Training; Source coding; Measurement; Industries; Software analytics; static analysis; false alarms; locality; hyperparameter optimization
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Advanced Malware Detection Techniques
TL;DR: It is shown here that effective predictors of static code warnings can be created by methods that locally adjust the decision boundary (between actionable warnings and others), and these methods yield a new high water-mark for recognizing actionablestatic code warnings. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: May 30, 2023

2021 article

Documenting evidence of a replication of ‘analyze this! 145 questions for data scientists in software engineering’

Yang, X., & Menzies, T. (2021, August 18). Documenting evidence of a replication of ‘analyze this! 145 questions for data scientists in software engineering.’ PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1602–1602.

By: X. Yang n & T. Menzies n 

author keywords: reuse; replication; data science; software analysis
topics (OpenAlex): Software Engineering Research; Data Mining Algorithms and Applications; Scientific Computing and Data Management
TL;DR: The use of the 145 software engineering questions for data scientists presented in the Microsoft study is reported here in a recent FSE~'20 paper by Huijgens et al. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 7, 2022

2021 article

Documenting evidence of a replication of ‘populating a release history database from version control and bug tracking systems’

Yang, X., & Menzies, T. (2021, August 18). Documenting evidence of a replication of ‘populating a release history database from version control and bug tracking systems.’ PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1601–1601.

By: X. Yang n & T. Menzies n 

author keywords: reuse; replication; bug fixing; text tagging
topics (OpenAlex): Software Engineering Research; Scientific Computing and Data Management; Software System Performance and Reliability
TL;DR: The use of a keyword-based and regular expression-based approach to identify bug-fixing commits by linking commit messages and issue tracker data in a recent FSE '20 paper is reported. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 7, 2022

2021 article

Documenting evidence of a reproduction of ‘is there a “golden” feature set for static warning identification? — an experimental evaluation’

Yang, X., & Menzies, T. (2021, August 18). Documenting evidence of a reproduction of ‘is there a “golden” feature set for static warning identification? — an experimental evaluation.’ PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1603–1603.

By: X. Yang n & T. Menzies n 

author keywords: reuse; reproduction; static analysis; deep learning
topics (OpenAlex): Data Visualization and Analytics; Anomaly Detection Techniques and Applications; Digital and Cyber Forensics
TL;DR: The use of the static analysis dataset generated by FindBugs in a recent EMSE '21 paper by Yang et al. is reported here. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 7, 2022

2021 article

Learning to recognize actionable static code warnings (is intrinsically easy)

Yang, X., Chen, J., Yedida, R., Yu, Z., & Menzies, T. (2021, April 12). Empirical Software Engineering, Vol. 26.

By: X. Yang n, J. Chen n, R. Yedida n , Z. Yu  n & T. Menzies n 

Contributors: X. Yang n, J. Chen n, R. Yedida n , Z. Yu  n & T. Menzies n 

author keywords: Static code analysis; Actionable warnings; Deep learning; Linear SVM; Intrinsic dimensionality
topics (OpenAlex): Software Engineering Research; Software Reliability and Analysis Research; Advanced Malware Detection Techniques
TL;DR: It is found that data mining algorithms can find actionable warnings with remarkable ease and is concluded that learning to recognize actionable static code warnings is easy, using a wide range of learning algorithms, since the underlying data is intrinsically simple. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: May 17, 2021

2021 journal article

Simpler Hyperparameter Optimization for Software Analytics: Why, How, When

IEEE Transactions on Software Engineering, 48(8), 1–1.

By: A. Agrawal*, X. Yang n, R. Agrawal  n, R. Yedida n , X. Shen n  & T. Menzies n 

Contributors: A. Agrawal*, X. Yang n, R. Agrawal  n, R. Yedida n , X. Shen n  & T. Menzies n 

author keywords: Software analytics; hyperparameter optimization; defect prediction; bad smell detection; issue close time; bug reports
topics (OpenAlex): Software Engineering Research; Software Testing and Debugging Techniques; Software Reliability and Analysis Research
TL;DR: The simple DODGE works best for data sets with low “intrinsic dimensionality” and very poorly for higher-dimensional data; nearly all the SE data seen here was intrinsically low-dimensional, indicating that DODGE is applicable for many SE analytics tasks. (via Semantic Scholar)
Sources: Web Of Science, Crossref, NC State University Libraries, ORCID
Added: June 12, 2021

2020 article

Understanding static code warnings: An incremental AI approach

Yang, X., Yu, Z., Wang, J., & Menzies, T. (2020, November 2). Expert Systems with Applications, Vol. 167.

By: X. Yang n, Z. Yu n , J. Wang * & T. Menzies n 

author keywords: Actionable warning identification; Active learning; Static analysis; Selection process
topics (OpenAlex): Software Engineering Research; Advanced Malware Detection Techniques; Software Reliability and Analysis Research
TL;DR: An incremental AI tool that watches humans reading false alarm reports can quickly learn to distinguish spurious false alarms from more serious matters that deserve further attention and can identify over 90% of actionable warnings in a priority order given by the algorithm. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: November 24, 2020

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