publications

journal articles

  1. Fredericks, E. M., DeVries, B., & Cheng, B. H. C. (2014). AutoRELAX: automatically RELAXing a goal model to address uncertainty. Empirical Software Engineering, 19(5), 1466–1501.
  2. Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), 1–16.
  3. Bowers, K. M., Fredericks, E. M., Hariri, R. H., & Cheng, B. H. C. (2020). Providentia: Using search-based heuristics to optimize satisficement and competing concerns between functional and non-functional objectives in self-adaptive systems. Journal of Systems and Software, 162, 110497.
  4. Langdon, W. B., Weimer, W., Petke, J., Fredericks, E., Lee, S., Winter, E., Basios, M., Cohen, M. B., Blot, A., Wagner, M., & others. (2020). Genetic improvement@ icse 2020. ArXiv Preprint ArXiv:2007.15987.
  5. Gerasimou, S., Vogel, T., & Diaconescu, A. (2019). Software Engineering for Intelligent and Autonomous Systems (SEfIAS). ArXiv Preprint ArXiv:1904.01518.
  6. Langdon, W. B., Nowack, V., Petke, J., Wagner, M., Lee, H., Fredericks, E. M., An, G., & Blot, A. (2023). Genetic Improvement @ ICSE 2023. ACM SIGSOFT Software Engineering Notes, 48(4), 51–59.
  7. Fredericks, E. M., Moore, J. M., & Diller, A. C. (2024). GenerativeGI: creating generative art with genetic improvement. Automated Software Engineering, 31(1), 23. https://rdcu.be/dAKXK

conference / workshop papers

  1. Ramirez, A. J., Fredericks, E. M., Jensen, A. C., & Cheng, B. H. C. (2012). Automatically relaxing a goal model to cope with uncertainty. International Symposium on Search Based Software Engineering, 198–212.
  2. Fredericks, E. M., Ramirez, A. J., & Cheng, B. H. C. (2013). Towards run-time testing of dynamic adaptive systems. 2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 169–174.
  3. Fredericks, E. M., Ramirez, A. J., & Cheng, B. H. C. (2013). Validating code-level behavior of dynamic adaptive systems in the face of uncertainty. International Symposium on Search Based Software Engineering, 81–95.
  4. Fredericks, E. M., & Cheng, B. H. C. (2013). Exploring automated software composition with genetic programming. Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, 1733–1734.
  5. Fredericks, E. M., DeVries, B., & Cheng, B. H. C. (2014). Towards run-time adaptation of test cases for self-adaptive systems in the face of uncertainty. Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 17–26.
  6. Fredericks, E. M., & Cheng, B. H. C. (2015). Automated generation of adaptive test plans for self-adaptive systems. 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 157–167.
  7. Fredericks, E. M., & Cheng, B. H. C. (2015). An empirical analysis of providing assurance for self-adaptive systems at different levels of abstraction in the face of uncertainty. 2015 IEEE/ACM 8th International Workshop on Search-Based Software Testing, 8–14.
  8. Fredericks, E. M. (2016). Automatically hardening a self-adaptive system against uncertainty. 2016 IEEE/ACM 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 16–27.
  9. Fredericks, E. M., & Hariri, R. H. (2016). Extending search-based software testing techniques to big data applications. 2016 IEEE/ACM 9th International Workshop on Search-Based Software Testing (SBST), 41–42.
  10. Albouq, S. S., & Fredericks, E. M. (2017). Lightweight detection and isolation of black hole attacks in connected vehicles. 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), 97–104.
  11. Albouq, S. S., & Fredericks, E. M. (2017). Securing communication between service providers and road side units in a connected vehicle infrastructure. 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), 1–5.
  12. Albouq, S. S., & Fredericks, E. M. (2017). Detection and avoidance of wormhole attacks in connected vehicles. Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, 107–116.
  13. Hariri, R. H., & Fredericks, E. M. (2018). Towards traceability link recovery for self-adaptive systems. Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence.
  14. Fredericks, E. M. (2018). An empirical analysis of the mutation operator for run-time adaptive testing in self-adaptive systems. 2018 IEEE/ACM 11th International Workshop on Search-Based Software Testing (SBST), 59–66.
  15. Bowers, K. M., Fredericks, E. M., & Cheng, B. H. C. (2018). Automated optimization of weighted non-functional objectives in self-adaptive systems. International Symposium on Search Based Software Engineering, 182–197.
  16. Fredericks, E. M. K. (2019). CARETAKVR: A Virtual Reality Environment to Train Alzheimer’s Caregivers. Proceedings of the 2019 American Society for Engineering Education North Central Section Conference.
  17. Fredericks, E. M., Bowers, K. M., & Hariri, R. H. (2019). On incorporating search-based heuristics into real-world systems. 2019 IEEE/ACM 12th International Workshop on Search-Based Software Testing (SBST), 11–12.
  18. Fredericks, E. M., Gerostathopoulos, I., Krupitzer, C., & Vogel, T. (2019). Planning as optimization: Dynamically discovering optimal configurations for runtime situations. 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 1–10.
  19. Fredericks, E. M., & Moore, J. M. (2020). Search@ Home: A Commercial Off-the-Shelf Environment for Investigating Optimization Problems. International Symposium on Search Based Software Engineering, 171–177.
  20. Fredericks, E. M., & DeVries, B. (2021). (Genetically) Improving Novelty in Procedural Story Generation. 2021 IEEE/ACM International Workshop on Genetic Improvement (GI), 39–40.
  21. Samin, H., Paucar, L. H. G., Bencomo, N., Hurtado, C. M. C., & Fredericks, E. M. (2021). RDMSim: an exemplar for evaluation and comparison of decision-making techniques for self-adaptation. 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 238–244.
  22. DeVries, B., Fredericks, E. M., & Cheng, B. H. C. (2021). Analysis and Monitoring of Cyber-Physical Systems via Environmental Domain Knowledge & Modeling. 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 11–17.
  23. Fredericks, E. M., DeVries, B., & Moore, J. M. (2022). Towards Self-Adaptive Game Logic. ArXiv Preprint ArXiv:2205.05498.
  24. Diller, A. C., & Fredericks, E. M. (2022). Towards Run-Time Search for Real-World Multi-Agent Systems. ArXiv Preprint ArXiv:2205.05502.
  25. Fredericks, E. M., Diller, A. C., & Moore, J. M. (2023). [Best Research Paper Award] Generative Art via Grammatical Evolution. To Be Published in the Proceedings of the 12th International Workshop on Genetic Improvement.
  26. Fredericks, E. M., Bowers, K. M., Price, K. A., & Hariri, R. H. (2018). Cal: A smart home environment for monitoring cognitive decline. 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), 1500–1506.
  27. Fredericks, E. M., & Burden, S. (2024). Towards Fuzz Testing a Procedurally-Generated Video Game. To Appear in the Proceedings of the 2024 ASEE North Central Section Conference (ASEE NCS).
  28. Goodling, A., Fredericks, E. M., Alsum-Wassenaar, S., & Chen, H.-P. (2024). Walk and Draw: Digital Cartography as Artistic Practice for K-12 Students. To Appear in the Proceedings of the 2024 ASEE North Central Section Conference (ASEE NCS).

book chapters

  1. Fredericks, E. M., & Bowers, K. M. (2021). Software Artifact Traceability in Big Data Systems. In Knowledge Management in the Development of Data-Intensive Systems (pp. 43–59). Auerbach Publications.

theses

  1. Fredericks, E. M. (2015). Mitigating uncertainty at design time and run time to address assurance for dynamically adaptive systems [PhD thesis]. Michigan State University.
  1. Fredericks, E. (2010). Machine Learning and Language Syntax: The Genetic Language Parser [Master's thesis]. Oakland University.