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Calling Network: A New Method for Modeling Software Runtime Behaviors

Published: 06 February 2015 Publication History

Abstract

Modern software systems' structures and behaviors are becoming very complicated. Existing models either lack systematic considerations on the whole system's behavior patterns or are inefficient in runtime monitoring. In this paper, the Calling Network (CN) model is proposed to provide new perspectives to analyze the dynamic execution process of a software system. CN is consisted of one or a series of Calling Graph (CG), which is a dynamic version of Call Graph and encodes method call frequencies. Some new perspectives such as Growing Network and Network (Graph) Sequence are also embodied in CN model. Based on a data set of 10 real-world Java programs, we show that CN presents several interesting features, such as Power-law degree distribution, Densification Power Law, and the stability of an entropy value -- Local Entropy. Experiments have been conducted to show the applications of CN in software significant module identification and runtime failure diagnosis.

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  • (2023)Fault Localization Using TrustRank AlgorithmApplied Sciences10.3390/app13221234413:22(12344)Online publication date: 15-Nov-2023
  • (2023)A Method-Level Defect Prediction Approach Based on Structural Features of Method-Calling NetworkIEEE Access10.1109/ACCESS.2023.323926611(7933-7946)Online publication date: 2023
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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
      Issue’s Table of Contents

      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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      Author Tags

      1. Method Call
      2. Software Behavior
      3. Software Model

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      View all
      • (2023)Fault Localization Using TrustRank AlgorithmApplied Sciences10.3390/app13221234413:22(12344)Online publication date: 15-Nov-2023
      • (2023)A Method-Level Defect Prediction Approach Based on Structural Features of Method-Calling NetworkIEEE Access10.1109/ACCESS.2023.323926611(7933-7946)Online publication date: 2023
      • (2022)Fault centrality: boosting spectrum-based fault localization via local influence calculationApplied Intelligence10.1007/s10489-021-02822-452:7(7113-7135)Online publication date: 1-May-2022
      • (2021)Multiscale Empirical Analysis of Software Network Evolution2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C)10.1109/QRS-C55045.2021.00165(1109-1118)Online publication date: Dec-2021
      • (2020)Enhancing Spectrum-Based Fault Localization Using Fault Influence PropagationIEEE Access10.1109/ACCESS.2020.29651398(18497-18513)Online publication date: 2020
      • (2020)Kieker: A monitoring framework for software engineering researchSoftware Impacts10.1016/j.simpa.2020.100019(100019)Online publication date: Jun-2020
      • (2020)Relation-based test case prioritization for regression testingJournal of Systems and Software10.1016/j.jss.2020.110539163(110539)Online publication date: May-2020
      • (2019)Mining of Probabilistic Controlling Behavior Model From Dynamic Software Execution TraceIEEE Access10.1109/ACCESS.2019.29229987(79602-79616)Online publication date: 2019
      • (2018)Test Case Prioritization Based on Method Call Sequences2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2018.00039(251-256)Online publication date: Jul-2018
      • (2018)Dynamic structure measurement for distributed softwareSoftware Quality Journal10.1007/s11219-017-9369-326:3(1119-1145)Online publication date: 24-Dec-2018
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