D-Index & Metrics Best Publications
Research.com 2022 Rising Star of Science Award Badge

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Rising Stars D-index 48 Citations 7,480 245 World Ranking 320 National Ranking 11
Computer Science D-index 46 Citations 7,387 237 World Ranking 4439 National Ranking 118

Research.com Recognitions

Awards & Achievements

2022 - Research.com Rising Star of Science Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Operating system
  • Software

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.

His most cited work include:

  • Deep code comment generation (186 citations)
  • Deep Learning for Just-in-Time Defect Prediction (138 citations)
  • HYDRA: Massively Compositional Model for Cross-Project Defect Prediction (128 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Software (33.86%)
  • Artificial intelligence (29.13%)
  • Code (20.47%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (29.13%)
  • Code (20.47%)
  • Software (33.86%)

In recent papers he was focusing on the following fields of 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.

Between 2019 and 2021, his most popular works were:

  • Perceptions, Expectations, and Challenges in Defect Prediction (42 citations)
  • Deep code comment generation with hybrid lexical and syntactical information (29 citations)
  • How does Machine Learning Change Software Development Practices (26 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Operating system
  • Software

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.

Best Publications

Deep code comment generation

Xing Hu;Ge Li;Xin Xia;David Lo.
international conference on program comprehension (2018)

333 Citations

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)

289 Citations

HYDRA: Massively Compositional Model for Cross-Project Defect Prediction

Xin Xia;David Lo;Sinno Jialin Pan;Nachiappan Nagappan.
IEEE Transactions on Software Engineering (2016)

220 Citations

Practitioners' expectations on automated fault localization

Pavneet Singh Kochhar;Xin Xia;David Lo;Shanping Li.
international symposium on software testing and analysis (2016)

205 Citations

Tag recommendation in software information sites

Xin Xia;David Lo;Xinyu Wang;Bo Zhou.
mining software repositories (2013)

181 Citations

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)

157 Citations

What do developers search for on the web

Xin Xia;Xin Xia;Lingfeng Bao;David Lo;Pavneet Singh Kochhar.
Empirical Software Engineering (2017)

156 Citations

Accurate developer recommendation for bug resolution

Xin Xia;David Lo;Xinyu Wang;Bo Zhou.
working conference on reverse engineering (2013)

154 Citations

Predicting semantically linkable knowledge in developer online forums via convolutional neural network

Bowen Xu;Deheng Ye;Zhenchang Xing;Xin Xia.
automated software engineering (2016)

150 Citations

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)

143 Citations

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Contact us

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