پژوهشنامه پردازش و مدیریت اطلاعات

پژوهشنامه پردازش و مدیریت اطلاعات

AI-Driven Automation for Transforming the Future of Software Development

نوع مقاله : مقاله پژوهشی

نویسندگان
Laith S. Ismail 1
Abeer Salim Jamil 2
Azimov Amantur Dastanbekovich 3
Ibraheem Hatem Mohammed Al-Dosari 4
Khdier Salman 5
1 Al-Turath University, Baghdad 10013, Iraq
2 Al-Mansour University College, Baghdad 10067, Iraq
3 Osh State University, Osh City 723500, Kyrgyzstan
4 Al-Rafidain University College Baghdad 10064, Iraq
5 Madenat Alelem University College, Baghdad 10006, Iraq
10.22034/jipm.2025.728106
چکیده
ABSTRACT
Background: Artificial Intelligence (AI) has recently emerged as a transformative innovation within the software industry, disrupting conventional approaches to application development by automating tasks, refining code, and enhancing resource efficiency. Prior research indicates the effectiveness of AI-powered tools across various domains. However, contemporary studies lack a detailed analysis of the diverse sectors utilizing AI tools for software development.
Objective: This article aims to identify the potential benefits and impacts of AI in software development, specifically regarding time-to-market, productivity, code quality, bug-fixing rates, resource flexibility, and developer satisfaction. The goal is to present fact-based information about AI’s impact on multiple industries and scopes of work.
Methods: A mixed-methods research design was employed to analyze quantitative data from 40 projects across healthcare, financial services, retail, technology, and e-commerce industries. Data were collected using various project management tools, automated testing environments, and online questionnaires addressed to developers. The study incorporated a comparative evaluation of AI-based projects and traditional projects, with statistical analysis.
Results: AI-driven software development projects demonstrated a mean reduction in time-to-market by 34.6%, an improvement in code quality by 70%, and a mean reduction in bug-fixing time by 57.7%. Productivity per sprint increased by over 70%, resource flexibility was higher (90.2% in AI projects vs. 67.8% in traditional projects), and developers reported higher satisfaction levels. These findings reinforce the concept that AI significantly enhances workflow and the achievement of optimal results.
Conclusion: AI substantially improves both the speed and quality of software development. Further research should expand to explore the experiences of different sectors, the application of AI-driven tools, their differentiation, and usage, as well as the ethical considerations to promote sustainable and innovative software engineering solutions.
کلیدواژه‌ها
KEYWORDS: AI-driven automation
software development
artificial intelligence (AI)
continuous integration (CI)
continuous delivery (CD)
automated testing
code generation
debugging
machine learning (ML)
software engineering

عنوان مقاله English

AI-Driven Automation for Transforming the Future of Software Development

نویسندگان English

Laith S. Ismail 1
Abeer Salim Jamil 2
Azimov Amantur Dastanbekovich 3
Ibraheem Hatem Mohammed Al-Dosari 4
Khdier Salman 5
1 Al-Turath University, Baghdad 10013, Iraq
2 Al-Mansour University College, Baghdad 10067, Iraq
3 Osh State University, Osh City 723500, Kyrgyzstan
4 Al-Rafidain University College Baghdad 10064, Iraq
5 Madenat Alelem University College, Baghdad 10006, Iraq
چکیده English

ABSTRACT
Background: Artificial Intelligence (AI) has recently emerged as a transformative innovation within the software industry, disrupting conventional approaches to application development by automating tasks, refining code, and enhancing resource efficiency. Prior research indicates the effectiveness of AI-powered tools across various domains. However, contemporary studies lack a detailed analysis of the diverse sectors utilizing AI tools for software development.
Objective: This article aims to identify the potential benefits and impacts of AI in software development, specifically regarding time-to-market, productivity, code quality, bug-fixing rates, resource flexibility, and developer satisfaction. The goal is to present fact-based information about AI’s impact on multiple industries and scopes of work.
Methods: A mixed-methods research design was employed to analyze quantitative data from 40 projects across healthcare, financial services, retail, technology, and e-commerce industries. Data were collected using various project management tools, automated testing environments, and online questionnaires addressed to developers. The study incorporated a comparative evaluation of AI-based projects and traditional projects, with statistical analysis.
Results: AI-driven software development projects demonstrated a mean reduction in time-to-market by 34.6%, an improvement in code quality by 70%, and a mean reduction in bug-fixing time by 57.7%. Productivity per sprint increased by over 70%, resource flexibility was higher (90.2% in AI projects vs. 67.8% in traditional projects), and developers reported higher satisfaction levels. These findings reinforce the concept that AI significantly enhances workflow and the achievement of optimal results.
Conclusion: AI substantially improves both the speed and quality of software development. Further research should expand to explore the experiences of different sectors, the application of AI-driven tools, their differentiation, and usage, as well as the ethical considerations to promote sustainable and innovative software engineering solutions.

کلیدواژه‌ها English

KEYWORDS: AI-driven automation
software development
artificial intelligence (AI)
continuous integration (CI)
continuous delivery (CD)
automated testing
code generation
debugging
machine learning (ML)
software engineering

References

Akomea-Frimpong, I., Dzagli, J. R. A. D., Eluerkeh, K., Bonsu, F. B., Opoku-Brafi, S., Gyimah, S., Asuming, N. A. S., et al. (2023). A systematic review of artificial intelligence in managing climate risks of PPP infrastructure projects.  Engineering, Construction and Architectural Management, ahead-of-print (ahead-of-print). https://doi.org/:10.1108/ECAM-01-2023-0016
Bajaj, Y., and Samal, M. (2023). 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, 11 (7), 345-350. https://doi.org/:10.22214/ijraset.2023.54628
Bird, C., Ford, D., Zimmermann, T., Forsgren, N., Kalliamvakou, E., Lowdermilk, T., and Gazit, I. (2023). Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools.  Queue, 20 (6), 10. https://doi.org/:10.1145/3582083
Bukhari, S., Tan, B., and Carli, L. D. (2023). Distinguishing AI- and Human-Generated Code: A Case Study. Proceedings of the 2023 Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses, Copenhagen, Denmark. https://doi.org/:10.1145/3605770.3625215
Cabrero-Daniel, B. (2023). AI for Agile development: a Meta-Analysis.  arXiv preprint, 2305.08093. https://doi.org/:10.48550/arXiv.2305.08093
Devalla, S., and Yogi, M. (2023). BUILDING TRUST IN AI -A SIMPLIFIED GUIDE TO SOFTWARE QUALITY. Journal of Soft Computing Paradigm, 5. https://doi.org/:10.36548/jscp.2023.3.001
Ernst, N. A., and Bavota, G. (2022). AI-Driven Development Is Here: Should You Worry?  IEEE Software, 39 (2), 106-110. https://doi.org/:10.1109/MS.2021.3133805
Fatah, O. R., and Qasim, N. (2022). The role of cyber security in military wars.  PCSIТS-V International Scientific and Practical Conference,  78 (06), 114-116.
Goel, A., Deshmukh, D. A. R., Kumar, M. Y., and Soi, M. A. (2023). Software Quality Assurace.  International Journal for Research in Applied Science and Engineering Technology, 11 (11), 1346-1352. https://doi.org/:10.22214/ijraset.2023.56760
Hashim, N., Mohsim, A., Rafeeq, R., and Pyliavskyi, V. (2019). New approach to the construction of multimedia test signals.  International Journal of Advanced Trends in Computer Science and Engineering, 8 (6), 3423-3429. https://doi.org/:10.30534/ijatcse/2019/117862019
Hiebl, M. R. W. (2021). Sample Selection in Systematic Literature Reviews of Management Research. Organizational Research Methods, 26 (2), 229-261. https://doi.org/:10.1177/1094428120986851
Jawad, A. M., Al-Aameri, M. G., & Qasim, N. H. (2023). Emerging Technologies and Applications of Wireless Power Transfer. Transport Development, 4 (19). https://doi.org/:10.33082/td.2023.4-19.12
Jawad, A. M., Qasim, N. H., and Pyliavskyi, V. (2022). Comparison of Metamerism Estimates in Video Paths using CAM's Models. IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), 10-12 Oct. https://doi.org/:10.1109/PICST57299.2022.10238685
Kumar, R., Naveen, V., Illa, P. K., Pachar, S., and Patil, P. (2023). The Current State of Software Engineering Employing Methods Derived from Artificial Intelligence and Outstanding Challenges. 1st International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP), 4-5 March 2023. https://doi.org/:10.1109/IHCSP56702.2023.10127112
Martínez-Fernández, S., Bogner, J., Franch, X., Oriol, M., Siebert, J., Trendowicz, A., Vollmer, A. M., et al. (2022). Software Engineering for AI-Based Systems: A Survey.  ACM Trans. Softw. Eng. Methodol., 31 (2), Article 37e. https://doi.org/:10.1145/3487043
Mr, N. (2023). Future Scope of Artificial Intelligence in Software Engineering.  International Journal of Science and Research (IJSR), 12, 1401-1402. https://doi.org/:10.21275/SR231113100032
Odeh, M. (2023). The Role of Artificial Intelligence in Project Management.  IEEE Engineering Management Review, 51 (4), 20-22. https://doi.org/:10.1109/EMR.2023.3309756
Omar S.S., N. J. M., Qasim N. H.,  Kawad R. T., Kalenychenko R. (2024). The Role of Digitalization in Improving Accountability and Efficiency in Public Services.  Revista Investigacion Operacional, 45 (2), 203-224.
Pan, Y., and Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends.  Automation in Construction, 122, 103517. https://doi.org/:10.1016/j.autcon.2020.103517
Peng, S., Kalliamvakou, E., Cihon, P., and Demirer, M. (2023). The impact of ai on developer productivity: Evidence from github copilot.  arXiv preprint, 2302.06590. https://doi.org/:10.48550/arXiv.2302.06590
Qasim, N., and Nataliia, L.-C. (2022). The Role of Drones for Evolving Telecommunication and Internet. International Scientific and Practical Conference: Problems of cyber security of information and telecommunication systems (PCSITS), Kyiv, Ukraine.
Qasim, N., Pyliavskyi, V., and Solodka, V. (2019). Development of test materials for assessment broadcasting video path.  arXiv preprint, 1907.11406. https://doi.org/:10.48550/arXiv.1907.11406
Qasim, N. H., and Jawad, A. M. (2024). 5G-enabled UAVs for energy-efficient opportunistic networking.  Heliyon, 10 (12), e32660. https://doi.org/:10.1016/j.heliyon.2024.e32660
Ray, B. (2023). Programming Language Processing: How AI can Revolutionize Software Development? Proceedings of the 16th Innovations in Software Engineering Conference, Allahabad, India. https://doi.org/:10.1145/3578527.3581766
Reddy, D. (2023). Data Engineering Challenges in AI automation. International Conference on Computing, Electronics & Communications Engineering (iCCECE), 14-16 Aug. https://doi.org/:10.1109/iCCECE59400.2023.10238496
Saklamaeva, V., and Pavlič, L. (2024). The Potential of AI-Driven Assistants in Scaled Agile Software Development. Applied Sciences, 14 (1). https://doi.org/:10.3390/app14010319
Sravanthi, J., Sobti, R., Semwal, A., Shravan, M., Al-Hilali, A. A., and Alazzam, M. B. (2023). AI-Assisted Resource Allocation in Project Management. 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 12-13 May. https://doi.org/:10.1109/ICACITE57410.2023.10182760
Yetiştiren, B., Özsoy, I., Ayerdem, M., and Tüzün, E. (2023). Evaluating the code quality of ai-assisted code generation tools: An empirical study on github copilot, amazon codewhisperer, and chatgpt.  arXiv preprint, 2304.10778. https://doi.org/:10.48550/arXiv.2304.10778
Yousif, O., Dawood, M., Jassem, F. T., and Qasim, N. H. (2024). Curbing crypto deception: evaluating risks, mitigating practices and regulatory measures for preventing fraudulent transactions in the middle east.  Encuentros: Revista de Ciencias Humanas, Teoría Social y Pensamiento Crítico, (22), 311-334. https://doi.org/:10.5281/zenodo.13732337

  • تاریخ دریافت 12 مرداد 1404