Statistical log differencing

Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recent works have considered the problem of log differencing: given two or more system's execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which accounts for frequencies of behaviors found in the logs. We present two algorithms, s2KDiff for differencing two logs, and snKDiff, for differencing of many logs at once, both presenting their results over a single inferred model. A unique aspect of our algorithms is their use of statistical hypothesis testing: we let the engineer control the sensitivity of the analysis by setting the target distance between probabilities and the statistical significance value, and report only (and all) the statistically significant differences. Our evaluation shows the effectiveness of our work in terms of soundness, completeness, and performance. It also demonstrates its effectiveness compared to previous work via a user-study and its potential applications via a case study using real-world logs.

Original languageEnglish
Title of host publicationProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages851-862
Number of pages12
ISBN (Electronic)9781728125084
DOIs
StatePublished - Nov 2019
Event34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 - San Diego, United States
Duration: 10 Nov 201915 Nov 2019

Publication series

NameProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019

Conference

Conference34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
Country/TerritoryUnited States
CitySan Diego
Period10/11/1915/11/19

Keywords

  • Log analysis
  • Model inference

Fingerprint

Dive into the research topics of 'Statistical log differencing'. Together they form a unique fingerprint.

Cite this