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

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Publications

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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]
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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]
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, Variability-Aware Performance Modeling: A Statistical Learning Approach, , Waterloo, Generative Software Development Laboratory, University of Waterloo, 08/2012. [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]
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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.
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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]
P
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.
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]
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]
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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]
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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.
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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]
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Our extensive industrial collaboration enables us to do research with immediate application to software development practices in realistic settings.

Thiago Tonelli Bartolomei

People in the lab are very enthusiastic about what they do. The environment is very stimulating and soon you realize that most of the limits are those in your head.

Kacper Bąk