TitleUsing declarative specification to improve the understanding, extensibility, and comparison of model-inference algorithms
Publication TypeJournal Article
Year of Publication2015
AuthorsBeschastnikh I, Brun Y, Abrahamson J, Ernst MD, Krishnamurthy A
JournalIEEE Transactions on Software Engineering
Date or Month PublishedApril
AbstractIt is a staple development practice to log system behavior. Numerous powerful model-inference algorithms have been proposed to aid developers in log analysis and system understanding. Unfortunately, existing algorithms are typically declared \emphprocedurally, making them difficult to understand, extend, and compare. This paper presents InvariMint, an approach to specify model-inference algorithms \emphdeclaratively. \par We applied the InvariMint declarative approach to two model-inference algorithms. The evaluation results illustrate that InvariMint (1) leads to new fundamental insights and better understanding of existing algorithms, (2) simplifies creation of new algorithms, including hybrids that combine or extend existing algorithms, and (3) makes it easy to compare and contrast previously published algorithms. \par InvariMint's declarative approach can outperform procedural implementations. For example, on a log of 50,000 events, InvariMint's declarative implementation of the kTails algorithm completes in 12 seconds, while a procedural implementation completes in 18 minutes. We also found that InvariMint's declarative version of the Synoptic algorithm can be over 170 times faster than the procedural implementation.
Downloadshttps://homes.cs.washington.edu/~mernst/pubs/fsm-inference-declarative-i... ICSE 2013 slides (PDF) https://github.com/ModelInference/synoptic InvariMint implementation https://homes.cs.washington.edu/~mernst/pubs/fsm-inference-declarative-t... PDF
Citation KeyBeschastnikhBAEK2015