Artificial intelligence applied to the optimization of computer programs
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Published:
2023-01-30
Keywords:
Artificial intelligence, software, systems, program
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Abstract
The optimization of computer programs is an area of great importance in the technology industry. The application of artificial intelligence (AI) to this task can allow for significant improvements in program performance and efficiency. The aim of this paper is to explore the application of AI to the optimization of computer programs and discuss its potential benefits and risks. A review of existing literature on the application of AI to program optimization was conducted. Various studies and academic articles were examined to identify the main techniques and approaches used in this area. By using machine learning techniques and other AI methods, patterns can be identified and processes optimized in ways that could not be done manually. It is important that these issues are addressed in a responsible and ethical manner to ensure that their benefits are maximized and potential risks are minimized. In conclusion, the application of AI to the optimization of computer programs has the potential to transform the technology industry and significantly improve program performance and efficiency.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Erazo-Luzuriaga, A. F., Ramos-Secaira, F. M., Galarza-Sánchez, P. C., & Boné-Andrade, M. F. (2023). Artificial intelligence applied to the optimization of computer programs. Journal of Economic and Social Science Research, 3(1), 48-63. https://doi.org/10.55813/gaea/jessr/v3/n1/61
References
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Amazon. (2021). Amazon Personalize. Amazon Web Services.
Arora, D., & Jain, A. (2018). An Overview of Applications of Artificial Intelligence in Software Engineering. In 2018 2nd International Conference on Computing Methodologies and Communication (ICCMC), 791-794. Obtenido de https://doi.org/10.1109/ICCMC.2018.8463077
Bakshi, S., & Agrawal, S. (2019). Evolution of software engineering through machine learning and artificial intelligence. In 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), 1-4. Obtenido de https://doi.org/10.1109/I2CT45716.2019.9033824
Bansal, A., & Singh, N. (2020). A systematic mapping study on the usage of artificial intelligence in software engineering. Software: Evolution and Process, 32(10), e2266. Obtenido de https://doi.org/10.1002/smr.2266
Bishop, C. M. (2006). Pattern recognition and machine learning (Vol. 4). Springer, 4.
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
Chen, M., Mao, S., & Liu, Y. (2018). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. Obtenido de https://doi.org/10.1007/s11036-018-1059-5
Chui, M., Manyika, J., & Miremadi, M. (2016). Where machines could replace humans—and where they can’t (yet). McKinsey Quarterly, 1-12.
Comisión Europea. (2019). Comisión Europea. Obtenido de https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311-313.
Dresner Advisory Services. (2020). Wisdom of Crowds Business Intelligence Market Study.
Forbes. (2021). Forbes. Obtenido de https://www.forbes.com/sites/forbestechcouncil/2021/02/05/the-pros-and-cons-of-ai-in-decision-making/?sh=3e8f08194332
Gartner. (2019). Gartner Survey Shows Lack of Skills and Quality Data Are Big Barriers to Artificial Intelligence Adoption. Obtenido de https://www.gartner.com/en/newsroom/press-releases/2019-06-25-gartner-survey-shows-lack-of-skills-and-quality-data
GitHub. (2021). GitHub. Obtenido de https://github.com/tensorflow/tensorflow
Glover, F. (1989). Tabu search—part I. ORSA Journal on Computing, 1(3), 190-206.
Glover, F. (1989). Tabu search—part I. ORSA Journal on computing, 1(3), 190-206.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Longman Publishing Co., Inc.
Goodall, N. J. (2014). Machine ethics and automated vehicles. Springer Link.
Goyal, M., Singh, N., & Gupta, R. (2020). Artificial Intelligence in Software Engineering: A Systematic Mapping Study. Journal of Systems and Software, 170. Obtenido de https://doi.org/10.1016/j.jss.2020.110748
Harvard Business Review. (2021). What's Holding Back AI: Lack of Quality Data and Trust. Harvard Business Review. Obtenido de https://hbr.org/2021/02/whats-holding-back-ai-lack-of-quality-data-and-trust
Haykin, S. (2009). Neural networks and learning machines (Vol. 3). Pearson Upper Saddle River, NJ, USA.
Haykin, S. (2009). Neural networks and learning machines. Pearson Education.
Holland, J. H. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. MIT Press.
Horowitz, M. C. (2016). The promise and peril of military applications of artificial intelligence. Bulletin of the Atomic Scientists, 72(3), 166-171.
iFlytek. (2019). iFlytek. Obtenido de https://www.prnewswire.com/news-releases/an-independent-study-shows-iflyteks-speech-recognition-technology-improves-customer-service-efficiency-by-80-300814602.html
Insights, MIT Technology Review. (2020). Solving the Data Dilemma: How to Get Your Data Strategy Right. Obtenido de https://www.technologyreview.com/insights/2020/02/13/solving-the-data-dilemma-how-to-get-your-data-strategy-right/
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department.
Karthick, M., Vijayakumar, P., & Manikandan, M. (2021). Improving Performance of Distributed Database Systems Using Artificial Intelligence. Journal of Digital Information Management, 19(2), 85-92. Obtenido de Journal of Digital Information Management
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, 1942-1948.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95 - International Conference on Neural Networks, 4, 1942-1948.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
McKinsey & Company. (2018). McKinsey & Company. Obtenido de https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/how-artificial-intelligence-can-deliver-real-value-to-companies
Nagarajaiah, R., & Srinivasa, K. G. (2020). Artificial intelligence in software engineering. In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 145-148. Obtenido de https://doi.org/10.1109/ICACCCN51078.2020.9253524
Nguyen, P. H., Nguyen, V. L., Nguyen, H. M., Nguyen, Q. T., & Thai, M. T. (2020). A survey of machine learning for big data processing. Big Data Analytics, 7(1), 1-38. Obtenido de https://doi.org/10.1186/s41044-020-00034-2
Nilsson, N. J. (2009). Understanding AI: Its nature and future. AI Magazine, 30(4), 49-54. Obtenido de https://doi.org/10.1609/aimag.v30i4.2297
NVIDIA. (2021). NVIDIA. Obtenido de https://developer.nvidia.com/tensorrt
ResearchAndMarkets.com. (2020). Global Artificial Intelligence Platforms Market Forecast to 2025: Leading Players are Google, Amazon, Microsoft, IBM, Palantir Technologies, and Wipro. Obtenido de https://www.researchandmarkets.com/reports/5017837/global-artificial-intelligence-platforms-market
Russell, S. J., & Norvig, P. (2016). Inteligencia artificial: Un enfoque moderno. Pearson.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT Press.
The Business Research Company. (2020). Real-time Analytics Market Global Report 2020-30: Covid-19 Growth and Change. GlobeNewswire. GlobeNewswire. Obtenido de https://www.globenewswire.com/news-release/2020/06/24/2053103/0/en/Real-time-Analytics-Market-Global-Report-2020-30-Covid-19-Growth-and-Change.html
UPS ORION: UPS. (2020). UPS ORION: UPS.
Verma, S., Gupta, V., & Singh, R. (2020). A Systematic Literature Review of Artificial Intelligence in Software Engineering. Journal of King Saud University-Computer and Information Sciences, 32(3), 273-289. Obtenido de https://doi.org/10.1016/j.jksuci.2019.09.001