research-article

Interaction Traces Mining for Efficient System Responses Generation

Authors:

Steve Versteeg,

Jean-Guy Schneider,

John GrundyAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 40, Issue 1

Pages 1 - 8

https://doi.org/10.1145/2693208.2693221

Published: 06 February 2015 Publication History

Abstract

Software service emulation is an emerging technique for creating realistic executable models of server-side behaviour. It is particularly useful in quality assurance and DevOps, replicating production-like conditions for large-scale enterprise software systems. Existing approaches can automatically build client-server and server-server interaction models of complex software systems directly from analysis of service interaction trace data. However, when these interaction traces become large, searching an entire trace library to generate a run-time responses can become very slow. In this paper we describe a new technique that utilises data mining, specifically clustering algorithms, to pre-process large amounts of recorded interaction trace data. With the obtained clusters we facilitate efficient yet well-formed runtime response generation in our Enterprise System emulation environment. We evaluate our approach using two common application-layer protocols: LDAP and SOAP. Our experimental results show that by utilising clustering techniques in the pre-processing step, the response generation time can be reduced by 99% on average compared with existing approaches.

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Cited By

Mittal HLaxman JKumar D(2023)ML-aVAT: A Novel 2-Stage Machine-Learning Approach for Automatic Clustering Tendency AssessmentBig Data Research10.1016/j.bdr.2023.10041334(100413)Online publication date: Nov-2023
https://doi.org/10.1016/j.bdr.2023.100413
Ospina Herrera JBotia D(2023)Cloud-Native Architecture for Distributed Systems that Facilitates Integration with AIOps PlatformsAdvances in Computing10.1007/978-3-031-47372-2_26(318-329)Online publication date: 14-Nov-2023
https://doi.org/10.1007/978-3-031-47372-2_26
Hossain MHan JSchneider JJiang JKabir MVersteeg S(2022)Extracting Formats of Service Messages with Varying PayloadsACM Transactions on Internet Technology10.1145/350315922:3(1-31)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1145/3503159
Show More Cited By

Index Terms

Interaction Traces Mining for Efficient System Responses Generation
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Project and people management
      2. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
    2. Software post-development issues
  2. Software notations and tools
    1. Software configuration management and version control systems

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Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 40, Issue 1

January 2015

237 pages

ISSN:0163-5948

DOI:10.1145/2693208

Editor:
Michael Wing

Issue’s Table of Contents

Copyright © 2015 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 February 2015

Published in SIGSOFT Volume 40, Issue 1

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Citations

Cited By

Mittal HLaxman JKumar D(2023)ML-aVAT: A Novel 2-Stage Machine-Learning Approach for Automatic Clustering Tendency AssessmentBig Data Research10.1016/j.bdr.2023.10041334(100413)Online publication date: Nov-2023
https://doi.org/10.1016/j.bdr.2023.100413
Ospina Herrera JBotia D(2023)Cloud-Native Architecture for Distributed Systems that Facilitates Integration with AIOps PlatformsAdvances in Computing10.1007/978-3-031-47372-2_26(318-329)Online publication date: 14-Nov-2023
https://doi.org/10.1007/978-3-031-47372-2_26
Hossain MHan JSchneider JJiang JKabir MVersteeg S(2022)Extracting Formats of Service Messages with Varying PayloadsACM Transactions on Internet Technology10.1145/350315922:3(1-31)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1145/3503159
Hossain MJiang JHan JKabir MSchneider JLiu C(2022)Inferring data model from service interactions for response generation in service virtualizationInformation and Software Technology10.1016/j.infsof.2021.106803145:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.infsof.2021.106803
Farahmandpour ZSeyedmahmoudian MStojcevski A(2021)A Review on the Service Virtualisation and Its Structural PillarsApplied Sciences10.3390/app1105238111:5(2381)Online publication date: 8-Mar-2021
https://doi.org/10.3390/app11052381
Jiang JSchneider JVersteeg SHan JHossain MLiu C(2021)R-gramJournal of Network and Computer Applications10.1016/j.jnca.2021.103247196:COnline publication date: 15-Dec-2021
https://dl.acm.org/doi/10.1016/j.jnca.2021.103247
Kumar DBezdek J(2020)Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of AlgorithmsIEEE Systems, Man, and Cybernetics Magazine10.1109/MSMC.2019.29611636:2(10-48)Online publication date: Apr-2020
https://doi.org/10.1109/MSMC.2019.2961163
Shen SZhang JHuang DXiao J(2020)Evolving from Traditional Systems to AIOps: Design, Implementation and Measurements2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA)10.1109/AEECA49918.2020.9213650(276-280)Online publication date: Aug-2020
https://doi.org/10.1109/AEECA49918.2020.9213650
Kabir MHan JHossain MVersteeg S(2020)SpecMiner: Heuristic-based mining of service behavioral models from interaction tracesFuture Generation Computer Systems10.1016/j.future.2020.10.033Online publication date: Nov-2020
https://doi.org/10.1016/j.future.2020.10.033
Jiang JVersteeg SHan JHossain MSchneider J(2019)A positional keyword-based approach to inferring fine-grained message formatsFuture Generation Computer Systems10.1016/j.future.2019.08.011Online publication date: Aug-2019
https://doi.org/10.1016/j.future.2019.08.011
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