TIM-8340 v1

Casola, V., De Benedictis, A., Mazzocca, C., & Orbinato, V. (2024). Secure software development and testing: A model-based methodology. Computers & Security, 137. https://doi.org/10.1016/j.cose.2023.103639
The author discusses the challenges of introducing security checks in SDLC.

Chun, T. J., En, L. J., Xuen, M. T. Y., Xuan, Y. M., & Muzafar, S. (2023). Secured software development and importance of secure software development life cycle. https://doi.org/10.36227/techrxiv.24548416.v1
The paper presents the SLDC phases and discusses how to integrate security testing.

OWASP Top Ten | OWASP Foundation. (n.d.). Retrieved August 2, 2024, from https://owasp.org/www-project-top-ten/
OWASP mapped their 2017 categories to the new report and introduced three new vulnerabilities.

Hassan, F. M. A., Das, S. R., & Hussain, M. (2023). Importance of secure software development for the software development at different SDLC phases. https://doi.org/10.31124/advance.23947392.v1
The authors discuss security at different phases of the SDLC process.

Rajapakse, R. N., Zahedi, M., Babar, M. A., & Shen, H. (2022). Challenges and solutions when adopting DevSecOps: A systematic review. Information and Software Technology, 141, 106700. https://doi.org/10.1016/j.infsof.2021.106700
This paper discusses the challenges and solutions in the adoption of DevSecOps.

Viswanathan, G., & J., P. (2021). A hybrid threat model for system-centric and attack-centric for effective security design in SDLC. Web Intelligence (2405-6456), 19(1/2), 1–11. https://doi.org/10.3233/WEB-210452
A hybrid model is proposed based on system-centric and attacker-centric to identify the threats in the software application during the software design phase.

Tosi, D. (2024). Studying the quality of source code generated by different AI generative engines: An empirical evaluation. Future Internet, 16(6), 188. https://doi.org/10.3390/fi16060188
In this paper, an empirical evaluation of AI-generated source code is performed: three complex coding problems (selected from the exams for the Java Programming course at the University of Insubria) are prompted to three different Large Language Model (LLM) Engines, and the generated code is evaluated in its correctness and quality by means of human-implemented test suites and quality metrics.

Saklamaeva, V., & Pavlič, L. (2023). The potential of ai-driven assistants in scaled agile software development. Applied Sciences, 14(1). https://doi.org/10.3390/app14010319
This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework.

Sauvola, J., Tarkoma, S., Klemettinen, M., Riekki, J., & Doermann, D. (2024). Future of software development with generative AI. Automated Software Engineering: An International Journal, 31(1). https://doi.org/10.1007/s10515-024-00426-z
The authors propose four primary scenarios, model trajectories for transitions between them, and reflect against relevant software development operations.

Odeh, A., Odeh, N., & Mohammed, A. S. (2024). A comparative review of ai techniques for automated code generation in software development: Advancements, challenges, and future directions. TEM Journal, 13(1), 726–739. https://doi.org/10.18421/TEM131-76
The authors discuss Artificial Intelligence (AI), as one of the most important fields of computer science, plays a significant role in the software development life cycle process, especially in the implementation phase, where developers require considerable effort to convert software requirements and design into code.