If you made any changes in Pure these will be visible here soon.

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.

  • 2 Similar Profiles
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
Computer programming languages Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 1996 2019

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
2 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

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

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

Safety engineering
Software testing
Edge detection
Seed
Semantics
14 Citations (Scopus)

DeepGauge: Multi-granularity testing criteria for deep learning systems

Ma, L., Juefei-Xu, F., Zhang, F., Sun, J., Xue, M., Li, B., Chen, C., Su, T., Li, L., Liu, Y., Zhao, J. & Wang, Y., Sep 3 2018, ASE 2018 - Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering. Kastner, C., Huchard, M. & Fraser, G. (eds.). Association for Computing Machinery, Inc, p. 120-131 12 p. (ASE 2018 - Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering).

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

Learning systems
Testing
Deep learning
Testbeds
Neurons