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Fingerprint Dive into the research topics where Jianjun Zhao is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Software engineering Engineering & Materials Science
Testing Engineering & Materials Science
Program debugging Engineering & Materials Science
Specifications Engineering & Materials Science
Software architecture Engineering & Materials Science
Flow graphs Engineering & Materials Science
Aspect oriented programming Engineering & Materials Science
Learning systems Engineering & Materials Science

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Research Output 1996 2019

1 Citation (Scopus)

API recommendation for event-driven Android application development

Yuan, W., Nguyen, H. H., Jiang, L., Chen, Y., Zhao, J. & Yu, H., Mar 1 2019, In : Information and Software Technology. 107, p. 30-47 18 p.

Research output: Contribution to journalArticle

Application programming interfaces (API)
Recommender systems
Software engineering
3 Citations (Scopus)

DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems

Ma, L., Juefei-Xu, F., Xue, M., Li, B., Li, L., Liu, Y. & Zhao, J., Mar 15 2019, SANER 2019 - Proceedings of the 2019 IEEE 26th International Conference on Software Analysis, Evolution, and Reengineering. Shihab, E., Lo, D. & Wang, X. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 614-618 5 p. 8668044. (SANER 2019 - Proceedings of the 2019 IEEE 26th International Conference on Software Analysis, Evolution, and Reengineering).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Learning systems
Testing
Deep learning
1 Citation (Scopus)

DeepGraph: A PyCharm Tool for Visualizing and Understanding Deep Learning Models

Hu, Q., Ma, L. & Zhao, J., May 21 2019, Proceedings - 25th Asia-Pacific Software Engineering Conference, APSEC 2018. IEEE Computer Society, p. 628-632 5 p. 8719435. (Proceedings - Asia-Pacific Software Engineering Conference, APSEC; vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Visualization
Learning systems
Network architecture
Software engineering
Neural networks
1 Citation (Scopus)

Deephunter: A coverage-guided fuzz testing framework for deep neural networks

Xie, X., Ma, L., Juefei-Xu, F., Xue, M., Chen, H., Liu, Y., Zhao, J., Li, B., Yin, J. & See, S., Jul 10 2019, ISSTA 2019 - Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. Moller, A. & Zhang, D. (eds.). Association for Computing Machinery, Inc, p. 158-168 11 p. (ISSTA 2019 - Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Seed
Testing
Defects
Deep neural networks
Accidents
1 Citation (Scopus)

DeepStellar: Model-based quantitative analysis of stateful deep learning systems

Du, X., Xie, X., Li, Y., Ma, L., Liu, Y. & Zhao, J., Aug 12 2019, ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Apel, S., Dumas, M., Russo, A. & Pfahl, D. (eds.). Association for Computing Machinery, Inc, p. 477-487 11 p. (ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Recurrent neural networks
Learning systems
Chemical analysis
Image classification
Speech recognition