Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)

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The Control Flow Complexity Metrics for Software Process Using Ant Colony Optimization

Authors
Rajeeb Sankar Bal1, *, Jibendu Kumar Mantri2
1Ph.D Scholar, Department of Computer Application, MSCB University, Baripada, Odisha, India
2Professor, Department of Computer Application, MSCB University, Baripada, Odisha, India
*Corresponding author. Email: rajiv.s.bal@gmail.com
Corresponding Author
Rajeeb Sankar Bal
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-616-6_54How to use a DOI?
Keywords
Software Development Life Cycle; Software Process; Software Evolution; Petri nets; Basic blocks; Ant Colony Optimization
Abstract

In the Software Development Life Cycle (SDLC), software evolution plays a crucial role in maintaining product quality, minimizing risks, and reducing the need for extensive rework. The software industry continuously seeks to enhance the quality and reliability of software products. Recent development efforts have increasingly focused on establishing well-structured and systematic processes for building and evolving software systems. In this study, Petri nets are employed as the fundamental modeling framework for representing the software process. We calculate the control flow and cyclomatic complexity to evaluate the structural complexity of the Petri net model. Furthermore, we propose an Ant Colony Optimization (ACO)-based approach for selecting optimal software process paths, where each path is weighted according to its underlying logical structure. The proposed algorithm is applied to a software process model to analyze and identify the optimal path, thereby improving process efficiency and supporting effective software evolution.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 March 2026
ISBN
978-94-6239-616-6
ISSN
1951-6851
DOI
10.2991/978-94-6239-616-6_54How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Rajeeb Sankar Bal
AU  - Jibendu Kumar Mantri
PY  - 2026
DA  - 2026/03/31
TI  - The Control Flow Complexity Metrics for Software Process Using Ant Colony Optimization
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 719
EP  - 731
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_54
DO  - 10.2991/978-94-6239-616-6_54
ID  - Bal2026
ER  -