research-article

Call Graph Evolution Analytics over a Version Series of an Evolving Software System

Author:

Animesh ChaturvediAuthors Info & Claims

ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

Article No.: 120, Pages 1 - 5

https://doi.org/10.1145/3551349.3559573

Published: 05 January 2023 Publication History

Abstract

Software evolution analytics can be supported by generating and comparing call graph evolution information over versions of a software system. Call Graph evolution analytics can assist a software engineer when maintaining or evolving a software system. This paper proposes Call Graph Evolution Analytics to extract information from a set of Evolving Call Graphs ECG = {CG1, CG2,...CGN} representing a Version Series VS = {V1, V2,...VN} of an evolving software system. This is done using Call Graph Evolution Rules (CGERs) and Call Graph Evolution Subgraphs (CGESs). Similar to association rule mining, the CGERs are used to capture co-occurrences of dependencies in the system. Like subgraph patterns in a call graph, the CGESs are used to capture evolution of dependency patterns in evolving call graphs. Call graph analytics on the evolution in these patterns can identify potentially affected dependencies (or procedure calls) that need attention. The experiments are done on the evolving call graphs of 10 large evolving systems to support dependency evolution management. This is demonstrated with detailed results for evolving call graphs of Maven-Core’s version series.

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Cited By

Chaturvedi SSaritha SChaturvedi A(2023)Spark based Parallel Frequent Pattern Rules for Social Media Data Analytics2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)10.1109/CCGridW59191.2023.00039(168-175)Online publication date: May-2023
https://doi.org/10.1109/CCGridW59191.2023.00039

Index Terms

Call Graph Evolution Analytics over a Version Series of an Evolving Software System
1. Information systems
  1. Information systems applications
    1. Data mining
2. Software and its engineering
  1. Software notations and tools
    1. Software libraries and repositories

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ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

October 2022

2006 pages

ISBN:9781450394758

DOI:10.1145/3551349

Copyright © 2022 ACM.

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Published: 05 January 2023

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ASE '22

ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering

October 10 - 14, 2022

MI, Rochester, USA

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Cited By

Chaturvedi SSaritha SChaturvedi A(2023)Spark based Parallel Frequent Pattern Rules for Social Media Data Analytics2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)10.1109/CCGridW59191.2023.00039(168-175)Online publication date: May-2023
https://doi.org/10.1109/CCGridW59191.2023.00039

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