AI and Software Engineering: Rapid Process Improvement through Advanced Techniques

AI and Software Engineering: Rapid Process Improvement through Advanced Techniques

Authors

  • Meghasai Bodimani Department of Computer Science, University of Missouri, Kansas City, USA

Downloads

Keywords:

Artificial Intelligence, Machine Learning, Privacy Mechanisms, Network

Abstract

In recent years, a variety of research have effectively applied machine learning approaches across a broad range of industries. This led to the creation of a large range of deep learning models, each adapted to a specific purpose in software development. There are various ways in which the software development business may benefit from employing deep learning models. Nowadays, nothing is more vital than consistently testing and maintaining software. Software engineers are responsible for a broad variety of duties during the lifespan of a software system, from original design to final delivery to clients via cloud-based platforms. It is evident from this list that all jobs involve meticulous planning and the availability of a range of materials. A developer may study a range of resources, including internal corporate resources, external websites with important programming material, and code repositories, before creating and testing a solution to the current issue. Finding out what went into building the  recommended is what this inquiry is all about. Based on user feedback, this system examines the  recommended's effectiveness and proposes methods to enhance the programme.

Downloads

Download data is not yet available.

References

X. Li, H. Jiang, Z. Ren, G. Li, and J. Zhang, “Deep learning in software engineering,” 2018, https://arxiv.org/ftp/arxiv/papers/1805/1805.04825.pdf.

G. Lorenzoni, P. Alencar, N. Nascimento, and D. Cowan, “Machine learning model development from a software engineering perspective: a systematic literature review,” 2021, https://arxiv.org/abs/2102.07574.

Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.

Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.

S. Shafiq, A. Mashkoor, C. Mayr-Dorn, and A. Egyed, “A literature review of using machine learning in software development life cycle stages,” IEEE Access, vol. 9, pp. 140896–140920, 2021.

N. Koneru, S. Rai, S. S. kumar, and S. Koppu, “Deep learning-based automated recommendation systems: a systematic review and trends,” Turkish Journal of Computer Mathematics Education, vol. 12, no. 6, pp. 3326–3345, 2021.

S. Amershi, A. Begel, C. Bird et al., “Software engineering for machine learning: a case study,” in Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 291–300, Montreal, QC, Canada, May 2019.

Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40-53.

Y. Yang, X. Xia, D. Lo, and J. Grundy, “A survey on deep learning for software engineering,” ACM Computing Surveys, vol. 54, no. 10, 2022

Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.

M. Z. M. Hazil, M. N. Mahdi, M. S. Mohd Azmi, L. K. Cheng, A. Yusof, and A. R. Ahmad, “Software project management using machine learning technique - a review,” in Proceedings of the 2020 8th International Conference on Information Technology and Multimedia (ICIMU), pp. 363–370, Selangor, Malaysia, August 2020.

S. Wang, T. Liu, and L. Tan, “Automatically learning semantic features for defect prediction,” Proceedings of the 38th International Conference on Software Engineering, vol. 14-22, pp. 297–308, 2016.

Pargaonkar, Shravan. "Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering." Journal of Science & Technology 1.1 (2020): 67-81.

M. Ulan, Aggregation as Unsupervised Learning in Software Engineering and Beyond, Linnaeus University Press, Cambridge, MA, USA, 2021.

N. C. Dang, M. N. Moreno-García, and F. De la Prieta, “Sentiment analysis based on deep learning: a comparative study,” Electronics, vol. 9, pp. 483–3, 2020.

J. A. Fadhil, K. T. Wei, and K. S. Na, “Artificial intelligence for software engineering: an initial review on software bug detection and prediction,” Journal of Computer Science, vol. 16, no. 12, pp. 1709–1717, 2020.

Pargaonkar, S. (2020). Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering. Journal of Science & Technology, 1(1), 67-81.

J. Wen, S. Li, Z. Lin, Y. Hu, and C. Huang, “Systematic literature review of machine learning based software development effort estimation models,” Information and Software Technology, vol. 54, no. 1, pp. 41–59, 2012.

Raparthi, M., Dodda, S. B., & Maruthi, S. (2020). Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks. European Economic Letters (EEL), 10(1).

Z. Wan, X. Xia, D. Lo, and G. C. Murphy, “How does machine learning change software development practices?” IEEE Transactions on Software Engineering, vol. 47, no. 9, pp. 1–1871, 2020.

Raparthi, Mohan, Sarath Babu Dodda, and SriHari Maruthi. "Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks." European Economic Letters (EEL) 10.1 (2020).

F. Del Carpio and L. B. Angarita, “Trends in software engineering processes using deep learning: a systematic literature review,” in Proceedings of the 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 445–454, Kranj, Slovenia, August 2020.

F. Meziane and S. Vadera, Artificial Intelligence in Software Engineering, Carnegie Mellon University, Pittsburgh, PA, USA, 2010.

M. Barenkamp, J. Rebstadt, and O. Thomas, “Applications of AI in classical software engineering,” AI Perspect, vol. 2, no. 1, pp. 1–15, 2020.

M. Harman, “The role of artificial intelligence in software engineering,” in Proceedings of the 2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE), pp. 1–6, Zurich, Switzerland, June 2012.

J. Tate, Software Process Quality Models: A Comparative Evaluation, Citeseerx, Pennslyvennia, PA, USA, 2003.

S. Kothawar and R. G. Vajrapu, “Software requirements prioritization practices in software start-ups: a qualitative research based on start-ups in India,” vol. 57, 2018.

W. Guo, D. Mu, J. Xu, P. Su, G. Wang, and X. Xing, “Lemma,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 364–379, Toronto, Canada, October 2018.

M. Shafiq, Z. Tian, A. K. Bashir, X. Du, and M. Guizani, “CorrAUC: a malicious bot-IoT traffic detection method in IoT network using machine learning techniques,” IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3242–3254, 2021.

Shafiq, Z. Tian, A. K. Bashir, X. Du, and M. Guizani, “IoT malicious traffic identification using wrapper-based feature selection mechanisms,” Computers & Security, vol. 94, Article ID 101863, 2020.

Shafiq, Z. Tian, A. K. Bashir, A. Jolfaei, and X. Yu, “Data mining and machine learning methods for sustainable smart cities traffic classification: a survey,” Sustainable Cities and Society, vol. 60

Downloads

Published

13-03-2021

How to Cite

Bodimani, M. “AI and Software Engineering: Rapid Process Improvement through Advanced Techniques”. Journal of Science & Technology, vol. 2, no. 1, Mar. 2021, pp. 95-119, https://thesciencebrigade.com/jst/article/view/69.
PlumX Metrics

Plaudit

License Terms

Ownership and Licensing:

Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.

License Permissions:

Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.

Additional Distribution Arrangements:

Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.

Online Posting:

Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.

Responsibility and Liability:

Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.

Loading...