Mathematical foundations based statistical modeling of software source code for software system evolution

Raghavendra Rao Althar; Abdulrahman Alahmadi; Debabrata Samanta; Mohammad Zubair Khan; Ahmed H. Alahmadi; Raghavendra Rao Althar; Abdulrahman Alahmadi; Debabrata Samanta; Mohammad Zubair Khan; Ahmed H. Alahmadi

doi:10.3934/mbe.2022170

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 4: 3701-3719. doi: 10.3934/mbe.2022170

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Research article Special Issues

Mathematical foundations based statistical modeling of software source code for software system evolution

1.
Department of Data Science, CHRIST University, Bangalore, Karnataka, India
2.
Specialist-QMS, First American India Private Ltd., Bangalore, Karnataka, India
3.
Department of Computer Science and Information, Taibah University, Madinah, Saudi Arabia
4.
Department of Computer Science, CHRIST University, Bangalore, India

Academic Editor: Danilo Pelusi

Received: 20 October 2021 Revised: 19 January 2022 Accepted: 26 January 2022 Published: 09 February 2022

Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the pattern hidden in various software development processes and artifacts. This module is part of the smart requirements management system that is intended to be built. This system will have multiple modules to make the software requirements management phase more secure from vulnerabilities. Some of the critical challenges bothering the software development community are discussed. The background of Machine Learning approaches and their application in software development practices are explored. Some of the work done around modeling the source code and approaches used for vulnerabilities understanding in software systems are reviewed. Program representation is explored to understand some of the principles that would help in understanding the subject well. Further deeper dive into source code modeling possibilities are explored. Machine learning best practices are explored inline with the software source code modeling.
- software development,
- requirements management,
- Bidirection encoders transformers,
- knowledge graphs,
- transfer learning
Citation: Raghavendra Rao Althar, Abdulrahman Alahmadi, Debabrata Samanta, Mohammad Zubair Khan, Ahmed H. Alahmadi. Mathematical foundations based statistical modeling of software source code for software system evolution[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3701-3719. doi: 10.3934/mbe.2022170

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Abstract

Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the pattern hidden in various software development processes and artifacts. This module is part of the smart requirements management system that is intended to be built. This system will have multiple modules to make the software requirements management phase more secure from vulnerabilities. Some of the critical challenges bothering the software development community are discussed. The background of Machine Learning approaches and their application in software development practices are explored. Some of the work done around modeling the source code and approaches used for vulnerabilities understanding in software systems are reviewed. Program representation is explored to understand some of the principles that would help in understanding the subject well. Further deeper dive into source code modeling possibilities are explored. Machine learning best practices are explored inline with the software source code modeling.

References

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