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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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publications
Irradiation-induced NanoCluster Evolution
Published in The Summer Undergraduate Research Fellowship (SURF) Symposium, 2017
Predict size evolution of solute nanoclusters in metallic alloys under irradiation
Recommended citation: Didier Ishimwe, Matthew J. Swenson, and Janelle P. Wharry, "Irradiation-induced NanoCluster Evolution" (August 3, 2017). The Summer Undergraduate Research Fellowship (SURF) Symposium. Paper 56.
Using Dynamically Inferred Invariants to Analyze Program Runtime Complexity
Published in International Workshop on Software Security from Design to Deployment, 2020
In this paper, we explore the use of recurrence relation in computing runtime complexity
Recommended citation: ThanhVu Nguyen, Didier Ishimwe, Alexey Malyshev, Timos Antonopoulos, and Quoc-Sang Phan. 2020. Using dynamically inferred invariants to analyze program runtime complexity. Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Security from Design to Deployment. Association for Computing Machinery, New York, NY, USA, 11–14.
Dynaplex: Analyzing Program Complexity using Dynamically Inferred Recurrence Relations
Published in Proceedings of the ACM on Programming Languages, 2021
In this paper we infer asymptotic worst case complexity using recurrence relations
Recommended citation: Didier Ishimwe, KimHao Nguyen, and ThanhVu Nguyen. 2021. Dynaplex: analyzing program complexity using dynamically inferred recurrence relations. Proc. ACM Program. Lang. 5, OOPSLA, Article 138 (October 2021), 23 pages.
Dynaplex: Inferring Asymptotic Runtime Complexity of Recursive Programs
Published in Proceedings of the 44th ACM/IEEE International Conference on Software Engineering: Companion Proceedings, 2022
In this paper we present Dynaplex, a tool for the automatic inference of recurrence relations from execution traces, which are subsequently solved to determine the asymptotic complexity bounds of recursive programs.
Recommended citation: Ishimwe, D., Nguyen, T., & Nguyen, K. (2022, May). Dynaplex: inferring asymptotic runtime complexity of recursive programs. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings (pp. 61-64).
Inferring Complexity Bounds from Recurrence Relations
Published in Proceedings of the 2023 18th Joint Meeting on Foundations of Software Engineering, 2023
In this paper we infer asymptotic worst case complexity using recurrence relations
Recommended citation: Didier Ishimwe. 2023. Inferring Complexity Bounds from Recurrence Relations. In Proceedings of the 2023 18th Joint Meeting on Foundations of Software Engineering.
Pathogen: LLM-Guided Fuzzing for Pathological Input Generation
Published in Proceedings of the International Symposium on Search-Based Software Engineering (SSBSE), 2025
In this paper we generate input leading to worst-case runtime execution using an LLM-guided fuzzing approach.
Recommended citation: Ishimwe, D., & Nguyen, K. (2025, November). LLM-Guided Fuzzing for Pathological Input Generation. In Proceedings of the International Symposium on Search-Based Software Engineering (SSBSE). Cham: Springer Nature Switzerland.
talks
Using Dynamically Inferred Invariants to Analyze Program Runtime Complexity
Published:
Presented a novel methodology and preliminary experimental results on inferring program complexity bounds. The approach utilizes dynamically computed recurrence relations to provide insights into software performance characteristics during early-stage analysis.
Dynaplex: Analyzing Program Complexity using Dynamically Inferred Recurrence Relations
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Detailed the implementation and empirical evaluation of Dynaplex, a novel dynamic analysis framework. The talk highlighted how Dynaplex automates the computation of runtime complexity for recursive programs through systematic execution monitoring.
Invariant/Specification Discovery using Dynamic and Symbolic Analyses
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Provided a comprehensive overview of the benefits of complexity analysis in the software development lifecycle. Introduced a dynamic approach for deriving asymptotic complexity bounds by automatically inferring recurrence relations directly from program execution traces.
Inferring Complexity Bounds for Nondeterministic Recursive Programs
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Discussed the inherent limitations of traditional recurrence-based analyses when applied to nondeterministic recursive programs. I presented my research on a specialized framework for inferring recurrence relations in nondeterministic recursive programs to enable accurate complexity inference.
Pathological Input Generation
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Delivered an invited talk as part of the GMU Software Engineering Seminar series regarding automated fuzzing techniques. The presentation focused on generating pathological inputs designed to expose worst-case runtime resource vulnerabilities in complex software.
Inferring Complexity Bounds from Recurrence Relations
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This talk was given at the New Jersey Programming Languages and Systems Seminar (NJPLS), which brings researchers in the New Jersey area together for a day of informal talks and discussions.
Inferring Complexity Bounds from Recurrence Relations
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Presented a poster session on automated complexity analysis for the ACM Student Research Competition (SRC). The presentation covered the core mechanics of dynamic complexity inference and was selected as a competition finalist.
LLM-Guided Fuzzing for Pathological Input Generation
Published:
Presented our research on leveraging Large Language Models to guide fuzzing processes in identifying inputs that trigger worst-case resource consumption. This work explores the intersection of generative AI and software testing to improve system robustness. Recognized with the Best Paper Award.