2022 - Research.com Rising Star of Science Award
Xin Xia mainly investigates Data mining, Artificial intelligence, Software, Software bug and Machine learning. The study incorporates disciplines such as Bug tracking system, Source lines of code and Software regression, Software quality in addition to Data mining. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Software maintenance.
Xin Xia combines subjects such as Code, Debugging, World Wide Web and Source code with his study of Software. His Software bug research integrates issues from Information retrieval, Component and Word embedding. His study on Precision and recall and Active learning is often connected to Cost effectiveness as part of broader study in Machine learning.
Xin Xia mostly deals with Software, Artificial intelligence, Code, Empirical research and Source code. His Software study combines topics in areas such as Debugging and Software engineering. His Artificial intelligence research includes elements of Machine learning, Software maintenance, Data mining and Natural language processing.
His Machine learning study incorporates themes from Classifier, Software system and Task. His Code study integrates concerns from other disciplines, such as Quality, Program comprehension, Theoretical computer science, Word and Information retrieval. Xin Xia has included themes like Java and Executable in his Source code study.
Xin Xia mainly focuses on Artificial intelligence, Code, Software, Deep learning and Empirical research. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Natural language processing. His Code research includes themes of Program comprehension, Java, Information retrieval, Source code and Software engineering.
Xin Xia studies Software, focusing on Software development in particular. The concepts of his Deep learning study are interwoven with issues in Software maintenance, Theoretical computer science, Feature learning and Leverage. In his study, Maintenance engineering is strongly linked to Software bug, which falls under the umbrella field of Task analysis.
Artificial intelligence, Deep learning, Software bug, Software and Empirical research are his primary areas of study. His Software development research extends to the thematically linked field of Artificial intelligence. Xin Xia has researched Deep learning in several fields, including Stability, Software maintenance, Theoretical computer science and Leverage.
While the research belongs to areas of Software maintenance, Xin Xia spends his time largely on the problem of Java, intersecting his research to questions surrounding Source code, Code, Code smell, Usability and Quality. His research in Software bug intersects with topics in Maintenance engineering, Task analysis, Commit and Knowledge management. The study incorporates disciplines such as Data modeling, Machine learning, Debugging and Information retrieval in addition to Software.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Deep code comment generation
Xing Hu;Ge Li;Xin Xia;David Lo.
international conference on program comprehension (2018)
Deep Learning for Just-in-Time Defect Prediction
Xinli Yang;David Lo;Xin Xia;Yun Zhang.
2015 IEEE International Conference on Software Quality, Reliability and Security (2015)
HYDRA: Massively Compositional Model for Cross-Project Defect Prediction
Xin Xia;David Lo;Sinno Jialin Pan;Nachiappan Nagappan.
IEEE Transactions on Software Engineering (2016)
Practitioners' expectations on automated fault localization
Pavneet Singh Kochhar;Xin Xia;David Lo;Shanping Li.
international symposium on software testing and analysis (2016)
Tag recommendation in software information sites
Xin Xia;David Lo;Xinyu Wang;Bo Zhou.
mining software repositories (2013)
TLEL: A two-layer ensemble learning approach for just-in-time defect prediction
Xinli Yang;David Lo;Xin Xia;Jianliang Sun.
Information & Software Technology (2017)
What do developers search for on the web
Xin Xia;Xin Xia;Lingfeng Bao;David Lo;Pavneet Singh Kochhar.
Empirical Software Engineering (2017)
Accurate developer recommendation for bug resolution
Xin Xia;David Lo;Xinyu Wang;Bo Zhou.
working conference on reverse engineering (2013)
Predicting semantically linkable knowledge in developer online forums via convolutional neural network
Bowen Xu;Deheng Ye;Zhenchang Xing;Xin Xia.
automated software engineering (2016)
What Security Questions Do Developers Ask? A Large-Scale Study of Stack Overflow Posts
Xin Li Yang;David Lo;Xin Xia;Zhi Yuan Wan.
Journal of Computer Science and Technology (2016)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: