Chao Ni

Orcid: 0000-0002-2906-0598

Affiliations:
  • Zhejiang University, School of Software Technology, Ningbo, China
  • Nanjing University, China (PhD 2020)


According to our database1, Chao Ni authored at least 27 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Federated Learning for Software Engineering: A Case Study of Code Clone Detection and Defect Prediction.
IEEE Trans. Software Eng., February, 2024

2023
Boosting multi-objective just-in-time software defect prediction by fusing expert metrics and semantic metrics.
J. Syst. Softw., December, 2023

Code-line-level Bugginess Identification: How Far have We Come, and How Far have We Yet to Go?
ACM Trans. Softw. Eng. Methodol., July, 2023

Automatic Identification of Crash-inducing Smart Contracts.
Proceedings of the IEEE International Conference on Software Analysis, 2023

C³: Code Clone-Based Identification of Duplicated Components.
Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023

Distinguishing Look-Alike Innocent and Vulnerable Code by Subtle Semantic Representation Learning and Explanation.
Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023

Boosting Just-in-Time Defect Prediction with Specific Features of C/C++ Programming Languages in Code Changes.
Proceedings of the 20th IEEE/ACM International Conference on Mining Software Repositories, 2023

Unifying Defect Prediction, Categorization, and Repair by Multi-Task Deep Learning.
Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, 2023

Function-Level Vulnerability Detection Through Fusing Multi-Modal Knowledge.
Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, 2023

FVA: Assessing Function-Level Vulnerability by Integrating Flow-Sensitive Structure and Code Statement Semantic.
Proceedings of the 31st IEEE/ACM International Conference on Program Comprehension, 2023

An Empirical Study of the Apache Voting Process on Open Source Community Governance.
Proceedings of the 14th Asia-Pacific Symposium on Internetware, 2023

2022
Revisiting Supervised and Unsupervised Methods for Effort-Aware Cross-Project Defect Prediction.
IEEE Trans. Software Eng., 2022

Just-In-Time Defect Prediction on JavaScript Projects: A Replication Study.
ACM Trans. Softw. Eng. Methodol., 2022

Defect Identification, Categorization, and Repair: Better Together.
CoRR, 2022

The best of both worlds: integrating semantic features with expert features for defect prediction and localization.
Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2022

Detecting and Defending CSRF at API-Level.
Proceedings of the IEEE International Symposium on Software Reliability Engineering Workshops, 2022

2021
Revisiting heterogeneous defect prediction methods: How far are we?
Inf. Softw. Technol., 2021

2020
Do different cross-project defect prediction methods identify the same defective modules?
J. Softw. Evol. Process., 2020

Revisiting Dependence Cluster Metrics based Defect Prediction.
Proceedings of the 32nd International Conference on Software Engineering and Knowledge Engineering, 2020

2019
Multitask defect prediction.
J. Softw. Evol. Process., 2019

An empirical study on pareto based multi-objective feature selection for software defect prediction.
J. Syst. Softw., 2019

Software defect number prediction: Unsupervised vs supervised methods.
Inf. Softw. Technol., 2019

Revisiting Heterogeneous Defect Prediction: How Far Are We?
CoRR, 2019

Multi-project Regression based Approach for Software Defect Number Prediction.
Proceedings of the 31st International Conference on Software Engineering and Knowledge Engineering, 2019

2017
A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction.
J. Comput. Sci. Technol., 2017

FeSCH: A Feature Selection Method using Clusters of Hybrid-data for Cross-Project Defect Prediction.
Proceedings of the 41st IEEE Annual Computer Software and Applications Conference, 2017

2009
Bottom-up fabrication of special parylene films based on selective growth on Au-coated surface.
Proceedings of the 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, 2009


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