Author:
Althar Raghavendra Rao, ,Alahmadi Abdulrahman,Samanta Debabrata,Khan Mohammad Zubair,Alahmadi Ahmed H., , ,
Abstract
<abstract><p>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.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference42 articles.
1. A. Ahmad, C. Feng, M. Khan, A. Khan, A. Ullah, S. Nazir, et al., A systematic literature review on using machine learning algorithms for software requirements identification on stack overflow, Secur. Commun. Networks, 2020.
2. R. Russell, L. Kim, L. Hamilton, T. Lazovich, J. Harer, O. Ozdemir, et al., Automated vulnerability detection in source code using deep representation learning, in 2018 17th IEEE international conference on machine learning and applications (ICMLA), IEEE, (2018), 757–762.
3. H. El-Hadary, S. El-Kassas, Capturing security requirements for software systems, J. Adv. Res., 5 (2014), 463–472. https://doi.org/10.1016/j.jare.2014.03.001
4. K. Chen G. S. Corrado, T. Mikolov, I. Sutskever, J. Dean, Distributed representations of words and phrases and their compositionality, Adv. Neural Inf. Process. Syst., (2013), 3111–3119.
5. Y. Kim, Convolutional neural networks for sentence classification, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (2014), 1746–1751. https://doi.org/10.3115/v1/D14-1181
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献