Abstract :

Artificial Intelligence (AI) is transforming software development by automating key processes such as code generation, testing, maintenance, and security. AI-powered tools like OpenAI Codex, GitHub Copilot, and DeepMind AlphaCode are revolutionizing programming by enhancing efficiency, reducing errors, and accelerating development cycles. Similarly, AI-driven testing frameworks improve bug detection, security analysis, and software performance optimization. This review explores recent advancements in AI-driven software development, analyzing its benefits, challenges, and ethical considerations. Additionally, it examines AI’s role in cybersecurity, low-code/no-code development, and workforce transformation. The paper concludes by discussing future research directions and the balance between automation and human oversight in AI-assisted software engineering.

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Keywords :

AI in Software Development, Automated Testing, Code Generation, Cybersecurity, Software Maintenance.

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References :

1. Mathews, M. Nagappan, “Test-Driven Development for Code Generation”, ArXiv, vol. abs/2402.13521, 2024.
2. Baqar, R. Khanda, “The Future of Software Testing: AI-Powered Test Case Generation and Validation”, ArXiv, vol. abs/2409.05808, 2024.
3. Odeh, N. Odeh, A. S. Mohammed, “A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions”, TEM Journal, vol. 13, no. 1, 2024.
4. Gajbhiye, A. Aggarwal, S. Jain, “Automated Security Testing in DevOps Environments Using AI and ML”, International Journal for Research Publication and Seminar, vol. 15, no. 2, 2024.
5. Yen, F. Bastani, F. Mohamed, H. Ma, J. Linn, “Application of AI Planning Techniques to Automated Code Synthesis and Testing”, in Proc. 14th IEEE International Conference on Tools with Artificial Intelligence, 2002, pp. 131-137.
6. A. Florez Muñoz, J. C. Jaramillo De La Torre, S. P. López, S. Herrera, C. A. Candela Uribe, “Comparative Study of AI Code Generation Tools: Quality Assessment and Performance Analysis”, LatIA, 2024.
7. Tembhekar, M. Devan, J. Jeyaraman, “Role of GenAI in Automated Code Generation within DevOps Practices”, Journal of Knowledge Learning and Science Technology, vol. 2, no. 2, p. 512, 2023.
8. Kendon, L. Wu, J. Aycock, “AI-Generated Code Not Considered Harmful”, Proceedings of the 25th Western Canadian Conference on Computing Education, 2023.
9. Tufano, A. Agarwal, J. Jang, R. Zilouchian Moghaddam, N. Sundaresan, “AutoDev: Automated AI-Driven Development”, ArXiv, vol. abs/2403.08299, 2024.
10. P. Velaga, “AI-Assisted Code Generation and Optimization: Leveraging Machine Learning to Enhance Software Development Processes”, International Journal of Innovations in Engineering Research and Technology, vol. 7, no. 9, pp. 177-186, 2020.
11. Ren, X. Ye, D. Zhao, Z. Xing, X. Yang, “From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining”, Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, pp. 976-987, 2023.
12. Liu, C. Tantithamthavorn, Y. Liu, L. Li, “On the Reliability and Explainability of Automated Code Generation Approaches”, ArXiv, vol. abs/2302.09587, 2023.
13. Takerngsaksiri, C. Warusavitarne, C. Yaacoub, M. H. K. Hou, C. Tantithamthavorn, “Students’ Perspectives on AI Code Completion: Benefits and Challenges”, ArXiv, vol. abs/2311.00177, 2023.
14. Xia, Y. Chen, T. Shi, J. Wang, J. Yang, “AICoderEval: Improving AI Domain Code Generation of Large Language Models”, ArXiv, vol. abs/2406.04712, 2024.
15. Kulkarni, “Artificial Intelligence in Software Testing”, International Journal of Innovative Science and Research Technology (IJISRT), 2024.
16. Pham, V.-L. Nguyen, T. Nguyen, “A Review of AI-Augmented End-to-End Test Automation Tools”, Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 2022.
17. Bajaj, M. Samal, “Accelerating Software Quality: Unleashing the Power of Generative AI for Automated Test-Case Generation and Bug Identification”, International Journal for Research in Applied Science and Engineering Technology, 2023.
18. Khankhoje, “AI in Test Automation: Overcoming Challenges, Embracing Imperatives”, International Journal on Soft Computing, Artificial Intelligence and Applications, 2024.
19. Khaliq, S. U. Farooq, D. Khan, “Artificial Intelligence in Software Testing: Impact, Problems, Challenges, and Prospect”, ArXiv, vol. abs/2201.05371, 2022.
20. Li, A. Ulrich, X. Bai, A. Bertolino, “Advances in Test Automation for Software with Special Focus on Artificial Intelligence and Machine Learning”, Software Quality Journal, vol. 28, pp. 245-248, 2019.
21. A. Hayat, S. Islam, M. F. Hossain, “The Evolving Role of Artificial Intelligence in Software Testing: Prospects and Challenges”, International Journal for Multidisciplinary Research, 2024.
22. Ricca, A. Marchetto, A. Stocco, “A Multi-Year Grey Literature Review on AI-Assisted Test Automation”, ArXiv, vol. abs/2408.06224, 2024.
23. Krishna, D. Thakur, H. S. Meka, “Enhancing software engineering practices with generative AI: A framework for automated code synthesis and refactoring”, World Journal of Advanced Engineering Technology and Sciences, 2024.
24. L. Lima, R. S. Santos, G. P. Garcia, S. S. da Silva, C. Franca, L. F. Capretz, “Software Testing and Code Refactoring: A Survey with Practitioners”, 2023 IEEE International Conference on Software Maintenance and Evolution, pp. 500-507, 2023.
25. Miskell, R. Diaz, P. Ganeriwala, K. Slhoub, F. Nembhard, “Automated Framework to Extract Software Requirements from Source Code”, Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval, 2023.
26. Oliveira, W. K. Assunção, A. F. Garcia, A. C. Bibiano, M. Ribeiro, R. Gheyi, B. Neto, “The untold story of code refactoring customizations in practice”, 2023 IEEE/ACM 45th International Conference on Software Engineering, pp. 108-120, 2023.
27. Baumgartner, P. Iyenghar, T. Schoemaker, E. Pulvermüller, “AI-Driven Refactoring: A Pipeline for Identifying and Correcting Data Clumps in Git Repositories”, Electronics, vol. 13, 2024.
28. Sajja, D. Thakur, A. Mehra, “Integrating Generative AI into the Software Development Lifecycle: Impacts on Code Quality and Maintenance”, International Journal of Science and Research Archive, 2024.
29. Agarwal, S. Chimalakonda, S. Krishnan, V. Kanvar, S. Shah, “Tutorial Report on Legacy Software Modernization: A Journey From Non-AI to Generative AI Approaches”, Proceedings of the 17th Innovations in Software Engineering Conference, 2024.
30. Oumarou, K. H. Tizi, “Improving Source Code Quality by Minimizing Refactoring Effort”, ILKOM Jurnal Ilmiah, 2024.
31. Rao, J. Tsay, K. Kate, V. Hellendoorn, M. Hirzel, “AI for Low-Code for AI”, ArXiv, vol. abs/2305.20015, 2023.
32. Nimje, “The Rise of Low-Code/No-Code Development Platforms”, International Journal of Advanced Research in Science, Communication and Technology, 2024.
33. Cabot, R. Clarisó, T. Menzies, “Low Code for Smart Software Development”, IEEE Software, vol. 40, pp. 89-93, 2023.
34. I. DeSilva, R. Ranathunga, R. Shangavie, “Quality Assurance in Low-Code/No-Code Development”, 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, pp. 1-7, 2023.
35. Yan, “The Impacts of Low/No-Code Development on Digital Transformation and Software Development”, ArXiv, vol. abs/2112.14073, 2021.
36. Picek, “Low-code/No-code Platforms and Modern ERP Systems”, 2023 International Conference on Information Management, pp. 44-49, 2023.
37. Sufi, “Algorithms in Low-Code-No-Code for Research Applications: A Practical Review”, Algorithms, vol. 16, pp. 108, 2023.
38. Palthya, “AI-based Systems Enhance Cybersecurity Defenses, Identify and Mitigate Cyber Threats in Real-Time”, International Journal of Science and Research, 2021.
39. M. Nour, S. A. Said, “Harnessing the Power of AI for Effective Cybersecurity Defense”, 2024 6th International Conference on Computing and Informatics, pp. 98-102, 2024.
40. Adhikari, S. Thapaliya, “An Overview of AI Applications in Cybersecurity for IT Management,” NPRC Journal of Multidisciplinary Research, 2024.
41. Guzman Camacho, “The Role of AI in Cybersecurity: Addressing Threats in the Digital Age,” Journal of Artificial Intelligence General Science, 2024.
42. Mahfuri, S. Ghwanmeh, R. Almajed, W. Alhasan, M. Salahat, J. H. Lee, T. M. Ghazal, “Transforming Cybersecurity in the Digital Era: The Power of AI,” 2024 2nd International Conference on Cyber Resilience, pp. 1-8, 2024.
43. B. Akhtar, A. T. Rawol, “Enhancing Cybersecurity through AI-Powered Security Mechanisms,” IT Journal Research and Development, 2024.
44. Hummelholm, “AI-based Quantum-Safe Cybersecurity Automation and Orchestration for Edge Intelligence in Future Networks”, European Conference on Cyber Warfare and Security, 2023.
45. Stevens, “Knowledge in the Grey Zone: AI and Cybersecurity,” Digital War, vol. 1, pp. 164-170, 2020.
46. -C. Necula, “Artificial Intelligence Impact On The Labour Force – Searching For The Analytical Skills Of The Future Software Engineers”, ArXiv, vol. abs/2302.13229, 2023.
47. Raj, “The Impact of AI on Job Roles, Workforce and Employment”, International Journal of Scientific Research in Engineering and Management, 2024.
48. Santhosh, R. Unnikrishnan, S. Shibu, K. M. Meenakshi, G. Joseph, “AI Impact on Job Automation,” International Journal of Engineering Technology and Management Sciences, 2023.
49. D. Jadhav, A. B. Banubakode, “The Implications of Artificial Intelligence on the Employment Sector,” International Journal For Multidisciplinary Research, 2024.
50. Tailor, S. Jain, A. Kamble, “A Review Paper on the Impact of Artificial Intelligence on the Job Market,” International Journal of Advanced Research in Science, Communication and Technology, 2023.
51. Subudhi, “Future of IT Jobs in the Era of Artificial Intelligence,” International Journal For Multidisciplinary Research, 2024.
52. Du, “AI and Your Job: What’s Changing and What’s Next,” Frontiers in Science and Engineering, 2024.
53. Terragni, P. Roop, K. Blincoe, “The Future of Software Engineering in an AI-Driven World,” ArXiv, vol. abs/2406.07737, 2024.
54. K. Bali, A. Mehdi, and Hariharan, “AI-Driven DevOps Transformation: A Paradigm Shift in Software Development,” 2024 3rd International Conference on Sentiment Analysis and Deep Learning, pp. 117-123, 2024.
55. K. Ale, “The Role of Artificial Intelligence in Enhancing Test Automation: Current Trends and Future Directions,” International Journal of Science and Research, 2024.
56. M. Ali, “AI-Driven Software Engineering,” Advances in Engineering Innovation, 2023.
57. Fatima, S. Haider, M. A. Ali, M. Abbas, I. Akhtar, M. Rasool, H. Maqbool, N. Khan, “AI Unleashed: Pioneering Trends and Future Directions in Artificial Intelligence,” Saudi Journal of Engineering and Technology, vol. 9, no. 8, 2024.
58. O. Ekpobimi, R. C. Kandekere, A. A. Fasanmade, “The Future of Software Development: Integrating AI and Machine Learning into Front-End Technologies,” Global Journal of Advanced Research and Reviews, 2024.
59. Satyam, P. Geetha, K. S. Shashikala, N. Kumar, “AI-Enabled Edge Computing Models: Trends, Developments, and Future Implications,” Proceedings of the 2023 2nd International Conference on Edge Computing and Applications, pp. 63-67, 2023.
60. Murthy MR, “Future Scope of Artificial Intelligence in Software Engineering,” International Journal of Science and Research, 2023.