Component behavior discovery from software execution data

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

11 Citations (Scopus)

Abstract

Tremendous amounts of data can be recorded during software execution. This provides valuable information on software runtime analysis. Many crashes and exceptions may occur, and it is a real challenge to understand how software is behaving. Software is usually composed of various components. A component is a nearly independent part of software that full-fills a clear function. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs. This paper presents an approach to utilize process mining as a tool to discover the real behavior of software and analyze it. The unstructured software execution data may be too complex, involving multiple interleaved components, etc. Applying existing process mining techniques results in spaghetti-like models with no clear structure and no valuable information that can be easily understood by end. In this paper, we start with the observation that software is composed of components and we use this information to decompose the problem into smaller independent ones by discovering a behavioral model per component. Through experimental analysis, we illustrate that the proposed approach facilitates the discovery of more understandable software models. All proposed approaches have been implemented in the open-source process mining toolkit ProM.
Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
PublisherInstitute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Print)978-1-590-4240-1
DOIs
Publication statusPublished - 6 Dec 2016
Event2016 IEEE Symposium on Computational Intelligence (SSCI 2016), December 6-9, 2016, Athens, Greece - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Conference

Conference2016 IEEE Symposium on Computational Intelligence (SSCI 2016), December 6-9, 2016, Athens, Greece
Abbreviated titleSSCI 2016
CountryGreece
CityAthens
Period6/12/169/12/16

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Liu, C., van Dongen, B. F., Assy, N., & van der Aalst, W. M. P. (2016). Component behavior discovery from software execution data. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SSCI.2016.7849947
Liu, C. ; van Dongen, B.F. ; Assy, N. ; van der Aalst, W.M.P. / Component behavior discovery from software execution data. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Institute of Electrical and Electronics Engineers, 2016. pp. 1-8
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Liu, C, van Dongen, BF, Assy, N & van der Aalst, WMP 2016, Component behavior discovery from software execution data. in 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Institute of Electrical and Electronics Engineers, pp. 1-8, 2016 IEEE Symposium on Computational Intelligence (SSCI 2016), December 6-9, 2016, Athens, Greece, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7849947

Component behavior discovery from software execution data. / Liu, C.; van Dongen, B.F.; Assy, N.; van der Aalst, W.M.P.

2016 IEEE Symposium Series on Computational Intelligence (SSCI). Institute of Electrical and Electronics Engineers, 2016. p. 1-8.

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

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Liu C, van Dongen BF, Assy N, van der Aalst WMP. Component behavior discovery from software execution data. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Institute of Electrical and Electronics Engineers. 2016. p. 1-8 https://doi.org/10.1109/SSCI.2016.7849947