Statistical Bug Isolation for Consensus Systems

Bachelor thesis (2023)

Authors

Levin N. Winter Electrical Engineering, Mathematics and Computer Science

Contributors

Burcu Kulahcioglu Ozkan Software Engineering - (mentor)

J.A. Pouwelse Data-Intensive Systems - (graduation committee member)

E.B. Gülcan Software Engineering - (coach)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

More Info

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http://resolver.tudelft.nl/uuid:41945f0a-39a8-4562-80bc-b33087380548

Published Date

30-06-2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The testing of consensus systems has received growing attention and recent testing tools generate many faulty executions. However, there is a lack of methods that automatically analyze these outputs to identify the root causes of the bugs they found.
This paper presents Isolation, a statistical bug isolation algorithm that uses message-based predicates to discriminate between different faults. We applied our method to executions of the XRP Ledger blockchain and evaluated its performance. Our comparison shows that Isolation correctly separates bugs by their root cause and consistently outperforms the state-of-the-art with higher F-scores.

Files

Thesis.pdf

(.pdf | 0.399 Mb)