Using finite-state models for log differencing-Reference-Cited by-同舟云学术

Using finite-state models for log differencing

Published:2018-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
language:
Short-container-title:

Author:

Amar Hen¹,Bao Lingfeng²,Busany Nimrod¹,Lo David³,Maoz Shahar¹

Affiliation:

1. Tel Aviv University, Israel

2. Zhejiang University, Singapore

3. Singapore Management University, Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3236024.3236069

Reference40 articles.

1. Tool and supporting materials website. http://smlab.cs.tau.ac.il/xlog/#FSE18. Tool and supporting materials website. http://smlab.cs.tau.ac.il/xlog/#FSE18.

2. Diffchecker. http://www.diffchecker.com. Diffchecker. http://www.diffchecker.com.

3. Mining API patterns as partial orders from source code

4. Mining sandboxes: Are we there yet?

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A literature review and existing challenges on software logging practices;Empirical Software Engineering;2024-06-18

2. Log‐based anomaly detection for distributed systems: State of the art, industry experience, and open issues;Journal of Software: Evolution and Process;2024-02-07

3. Deep Learning or Classical Machine Learning? An Empirical Study on Log-Based Anomaly Detection;Proceedings of the IEEE/ACM 46th International Conference on Software Engineering;2024-02-06

4. EvLog: Identifying Anomalous Logs over Software Evolution;2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE);2023-10-09

5. AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11