Jianmei Guo (郭健美), Ph.D.

Postdoctoral Fellow

Office: DC 3587
Email: gjm [at] gsd.uwaterloo.ca

Ph.D., Computer Science & Engineering, Shanghai Jiao Tong University
M.S., Computer Science & Engineering, Shanghai Jiao Tong University
B.S., Management Information Systems, Tianjin University

My research interests are in Software Engineering and Artificial Intelligence, focusing on methods, techniques, and tools for developing adaptable and reliable software systems. Currently, I investigate algorithms of Statistical Machine Learning, Multi-Objective Combinatorial Optimization, and Parallelization to accurately and efficiently predict and optimize the quality attributes (e.g., performance, cost, and energy consumption) of configurable software and products. Moreover, I study Model-Driven Engineering for sustainable evolution of Linux Kernel and eCos.

More details are available at my DBLP, Google Scholar, and LinkedIn.

News

Projects

Current Projects

Tools

Publications

2015
Sarkar, A., J. Guo, N. Siegmund, S. Apel, and K. Czarnecki, "Cost-Efficient Sampling for Performance Prediction of Configurable Systems", 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), Lincoln, Nebraska, USA, IEEE, 11/2015. [pdf]
Zhang, Y., J. Guo, E. Blais, and K. Czarnecki, "Performance Prediction of Configurable Software Systems by Fourier Learning", 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), Lincoln, Nebraska, USA, 11/2015. [pdf][pdf]
Passos, L., L. Teixeira, D. Nicolas, S. Apel, A. Wąsowski, K. Czarnecki, P. Borba, and J. Guo, "Coevolution of Variability Models and Related Software Artifacts: A Fresh Look at Evolution Patterns in the Linux Kernel", Empirical Software Engineering, Springer, 05/2015.
Murashkin, A., L S. Azevedo, J. Guo, E. Zulkoski, J. Liang, K. Czarnecki, and D. Parker, "Automated Decomposition and Allocation of Automotive Safety Integrity Levels Using Exact Solvers", SAE 2015 World Congress & Exhibition, Detroit, Michigan, USA, SAE, 04/2015.
2014
Guo, J., E. Zulkoski, R. Olaechea, D. Rayside, K. Czarnecki, S. Apel, and J. M. Atlee, "Scaling Exact Multi-Objective Combinatorial Optimization by Parallelization", 29th IEEE/ACM International Conference on Automated Software Engineering (ASE), Västerås, Sweden, ACM, to appear, 2014. [pdf][pdf]
Olaechea, R., D. Rayside, J. Guo, and K. Czarnecki, "Comparison of exact and approximate multi-objective optimization for software product lines", Software Product Line Conference, vol. 1, Florence, Italy, ACM, pp. 92-101, 10/2014. [pdf]
Berger, T., and J. Guo, "Towards System Analysis with Variability Model Metrics", Eigth International Workshop on Variability Modelling of Software-intensive Systems (VAMOS'14), 2014. [pdf][pdf]
2013
Passos, L., J. Guo, L. Teixeira, K. Czarnecki, A. Wąsowski, and P. Borba, "Coevolution of Variability Models and Related Artifacts: A Case Study from the Linux Kernel", 17th International Software Product Line Conference, Tokyo, ACM, 2013. [pdf][pdf]
Guo, J., K. Czarnecki, S. Apel, N. Siegmund, and A. Wąsowski, "Variability-Aware Performance Prediction: A Statistical Learning Approach", 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), Silicon Valley, California, USA, IEEE, 11/2013. [pdf][pdf]
Guo, J., K. Czarnecki, S. Apel, N. Siegmund, and A. Wąsowski, Why CART Works for Variability-Aware Performance Prediction? An Empirical Study on Performance Distributions, , Waterloo, Generative Software Development Laboratory, University of Waterloo, 04/2013. [pdf]
Passos, L., K. Czarnecki, S. Apel, A. Wąsowski, C. Kästner, J. Guo, and C. Hunsen, "Feature-Oriented Software Evolution", The Seventh International Workshop on Variability Modelling of Software-intensive Systems, Italy, ACM , 01/2013. [pdf][pdf]
Yang, D., J. Cao, J. Fu, J. Wang, and J. Guo, "A Pattern Fusion Model for Multi-Step-Ahead CPU Load Prediction", Journal of Systems and Software, vol. 86, issue 5, 2013.
2012
Guo, J., K. Czarnecki, S. Apel, N. Siegmund, and A. Wąsowski, Variability-Aware Performance Modeling: A Statistical Learning Approach, , Waterloo, Generative Software Development Laboratory, University of Waterloo, 08/2012. [pdf]
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Our regular stand-up scrum meetings are a great way to have regular interaction with other team members, as well as our supervisor.

Rafael Lotufo

Our lab is not only about research: we do a lot of development using Java, Python, Javascript, Haskell and other languages in combination with advanced libraries and frameworks. This development experience was very helpful during my job interviews, and employers were impressed by the projects we develop here in the lab.

Alexandr Murashkin