Position:	~~PhD Student~~
Room:	~~MF 7.108~~
Tel (internal):	~~2087~~
Links:	Google scholar page ~~TU/e employee page~~

Projects

3TU.BSR - 3TU – Big Software on the Run Description Millions of lines of code – written in different languages by different people at different times, and operating on a variety of platforms – drive the systems performing key processes in our society. The resulting software needs to evolve and can no longer be controlled a priori Read More ...

Publications

Software data analytics: architectural model discovery and design pattern detection - Liu, C. (2019). Software data analytics: architectural model discovery and design pattern detection Eindhoven: Technische Universiteit Eindhoven
Visual analysis of parallel interval events - Qi, J., Liu, C., Cappers, B. C. M., & van de Wetering, H. M. M. (2018). Visual analysis of parallel interval events. In J. Johansson, F. Sadio, & T. Schreck (Eds.), 20th EG/VGTC Conference on Visualization (pp. 1-6). Geneve: The Eurographics Association. DOI: 10.2312/eurovisshort.20181074 Abstract System logs typically contain lines with time stamps that each Read More ...
Learning process models in IoT Edge - Cheng, L., Liu, C., Liu, Q., Duan, Y., & Murphy, J. (2019). Learning process models in IoT Edge. In C. K. Chang, P. Chen, M. Goul, K. Oyama, S. Reiff-Marganiec, Y. Sun, S. Wang, … Z. Wang (Eds.), Proceedings – 2019 IEEE World Congress on Services, SERVICES 2019 (pp. 147-150). [8817302] Piscataway: Institute of Electrical Read More ...
Detecting behavioral design patterns from software execution data - Liu, C., van Dongen, B. F., Assy, N., & van der Aalst, W. M. P. (2019). Detecting behavioral design patterns from software execution data. In E. Damiani, G. Spanoudakis, & L. A. Maciaszek (Eds.), Evaluation of Novel Approaches to Software Engineering – 13th International Conference, ENASE 2018, Revised Selected Papers (pp. 137-164). (Communications in Computer Read More ...
A general framework to identify software components from execution data - Liu, C., van Dongen, B. F., Assy, N., & van der Aalst, W. M. P. (2019). A general framework to identify software components from execution data. In G. Spanoudakis, E. Damiani, L. Maciaszek, & L. Maciaszek (Eds.), ENASE 2019 – Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (pp. Read More ...
Software architectural model discovery from execution data - Liu, C., van Dongen, B. F., Assy, N., & van der Aalst, W. M. P. (2018). Software architectural model discovery from execution data. In ENASE 2018 – Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering (pp. 3-10) Abstract During the execution of software systems, many crashes and exceptions may Read More ...
Component interface identification and behavioral model discovery from software execution data - Liu, Cong, van Dongen, Boudewijn, Assy, Nour & van der Aalst, Wil M.P. (2018). Component interface identification and behavioral model discovery from software execution data. Proceedings – 2018 ACM/IEEE 26th International Conference on Program Comprehension, ICPC 2018 (pp. 97-107). New York: Association for Computing Machinery, Inc Abstract Restructuring an object-oriented software system into a component-based Read More ...
A framework to support behavioral design pattern detection from software execution data - Liu, Cong, van Dongen, Boudewijn, Assy, Nour & van der Aalst, Wil M.P. (2018). A framework to support behavioral design pattern detection from software execution data. ENASE 2018 – Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering (pp. 65-76). SCITEPRESS-Science and Technology Publications, Lda.. Abstract The detection of design Read More ...
A general framework to detect behavioral design patterns - Liu, Cong, Van Dongen, Boudewijn, Assy, Nour & Van Der Aalst, Wil M.P. (2018). A general framework to detect behavioral design patterns. Proceedings – International Conference on Software Engineering (pp. 234-235). IEEE Computer Society. Abstract This paper presents a general framework to detect behavioral design patterns by combining source code and execution data. The framework Read More ...