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- short-paperNovember 2023
Inferring Complexity Bounds from Recurrence Relations
ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software EngineeringNovember 2023, Pages 2198–2200https://doi.org/10.1145/3611643.3617853Determining program complexity bounds is a fundamental problem with a variety of applications in software development. In this paper we present a novel approach for computing the asymptotic complexity bounds of non-deterministic recursive programs by ...
- research-articleOctober 2022
Dynaplex: inferring asymptotic runtime complexity of recursive programs
ICSE '22: Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion ProceedingsMay 2022, Pages 61–64https://doi.org/10.1145/3510454.3516853Automated runtime complexity analysis can help developers detect egregious performance issues. Existing runtime complexity analysis are often done for imperative programs using static analyses. In this demo paper, we demonstrate the implementation and ...
- research-articleOctober 2021
Dynaplex: analyzing program complexity using dynamically inferred recurrence relations
Proceedings of the ACM on Programming Languages (PACMPL), Volume 5, Issue OOPSLAArticle No.: 138, Pages 1–23https://doi.org/10.1145/3485515Being able to detect program runtime complexity is useful in many tasks (e.g., checking expected performance and identifying potential security vulnerabilities). In this work, we introduce a new dynamic approach for inferring the asymptotic complexity ...
- research-articleNovember 2020
Using dynamically inferred invariants to analyze program runtime complexity
SEAD 2020: Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Security from Design to DeploymentNovember 2020, Pages 11–14https://doi.org/10.1145/3416507.3423189Being able to detect program runtime complexity can help identify security vulnerabilities such as DoS attacks and side-channel information leakage. In prior work, we use dynamic invariant generation to infer nonlinear numerical relations to represent ...