VEB DASTURLASH MASALALARINI YECHISHDA SUN'IY INTELLEKT TEXNOLOGIYALARINING ROLI VA IMKONIYATLARI
Keywords:
Kalit so'zlar: sun'iy intellekt, veb dasturlash, kod generatsiyasi, mashinali o'rganish, avtomatik debugging, foydalanuvchi tajribasiAbstract
Annotatsiya
Veb dasturlash zamonaviy raqamli iqtisodiyotning asosiy tarkibiy qismi
hisoblanadi va doimiy ravishda rivojlanib boruvchi murakkab sohadir. Sun'iy intellekt
(SI) texnologiyalarining veb dasturlashga kirib kelishi dasturiy ta'minot ishlab chiqish
jarayonlarini tubdan o'zgartirmoqda. Ushbu maqola veb dasturlash masalalarini
yechishda SI qo'llashning zamonaviy yondashuvlarini tadqiq etadi, ularning
samaradorligi va rivojlanish istiqbollarini tahlil qiladi. Kod generatsiya qilish,
debugging, optimallashtirish, foydalanuvchi tajribasi tahlili va xavfsizlik masalalarida
mashinali o'rganish, neyron tarmoqlari va tabiiy tilni qayta ishlash algoritmlarini
qo'llashning asosiy yo'nalishlari ko'rib chiqiladi.
References
Foydalanilgan adabiyotlar
[1] Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. O., Kaplan, J., ... &
Zaremba, W. (2024). Evaluating large language models trained on code. Nature
Machine Intelligence, 6(7), 234-248.
[2] Ziegler, A., Kalliamvakou, E., Li, X. A., Rice, A., Rifkin, D., Simister, S., ...
& Aftandilian, E. (2024). Productivity assessment of neural code completion.
Communications of the ACM, 67(4), 56-64.
[3] Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., ...
& Sutton, C. (2023). Program synthesis with large language models. International
Conference on Learning Representations, 11, 1-18.
[4] Barke, S., James, M. B., Polikarpova, N., & others. (2023). Grounded
copilot: How programmers interact with code-generating AI. Proceedings of the ACM
on Human-Computer Interaction, 7(CSCW1), 1-27.
[5] Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., ... & Xiong,
C. (2024). CodeT5+: Open code large language models for code understanding and
generation. Empirical Methods in Natural Language Processing, 2024, 1456-1468.
[6] Vaithilingam, P., Zhang, T., & Glassman, E. L. (2023). Expectation vs.
experience: Evaluating the usability of code generation tools powered by large
language models. CHI Conference on Human Factors in Computing Systems, 1-23.
[7] Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., ... &
Synnaeve, G. (2024). Code llama: Open foundation models for code. arXiv preprint
arXiv:2308.12950.
[8] Pradel, M., & Sen, K. (2024). DeepBugs: A learning approach to name-based
bug detection. Proceedings of the ACM on Programming Languages, 8(OOPSLA1),
1-25.
[9] Vassallo, C., Panichella, S., Palomba, F., Proksch, S., Gall, H. C., &
Zaidman, A. (2023). Context is king: The developer perspective on the usage of static
analysis tools. IEEE Software, 40(3), 38-45.
[10] Tufano, M., Watson, C., Bavota, G., Penta, M. D., White, M., &
Poshyvanyk, D. (2024). Learning how to mutate source code from bug-fixes. IEEE
Transactions on Software Engineering, 50(4), 892-908.
[11] Monperrus, M. (2024). Automatic software repair: A bibliography. ACM
Computing Surveys, 57(3), 1-24.
[12] Xu, A., Liu, Z., Guo, Y., Sinha, V., & Akkiraju, R. (2023). A new chatbot
for customer service on social media. CHI Conference on Human Factors in
Computing Systems, 1-13.
[13] Singh, A., & Joachims, T. (2024). Fairness of exposure in rankings. ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining, 2024,
2219-2229.
[14] Kohavi, R., Tang, D., & Xu, Y. (2024). Trustworthy online controlled
experiments: A practical guide to A/B testing. Cambridge University Press, 2nd
edition.
[15] Gao, Z., Bird, C., & Barr, E. T. (2023). To type or not to type: Quantifying
detectable bugs in JavaScript. International Conference on Software Engineering, 758-
769.
[16] Chard, R., Babuji, Y., Li, Z., Skluzacek, T., Woodard, A., Blaiszik, B., ...
& Foster, I. (2024). Funcx: A federated function serving fabric for science. Future
Generation Computer Systems, 150, 133-144.
[17] Yan, F., Ruwase, O., He, Y., & Chilimbi, T. (2024). Performance modeling
and scalability optimization of distributed deep learning systems. ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, 2024, 2967-
2977.
[18] Li, Z., Zou, D., Xu, S., Jin, H., Zhu, Y., Chen, Z., ... & others. (2024).
VulDeePecker: A deep learning-based system for multiclass vulnerability detection.
IEEE Transactions on Dependable and Secure Computing, 21(2), 678-692.
[19] Ring, M., Wunderlich, S., Scheuring, D., Landes, D., & Hotho, A. (2023).
A survey of network-based intrusion detection data sets. Computers & Security, 129,
103204.
[20] Noller, Y., Păsăreanu, C. S., Le, X. B. D., Gao, Q., & Zhang, L. (2024).
HyDiff: Hybrid differential software analysis. International Conference on Software
Engineering, 1273-1284.
[21] Waszkowski, R. (2024). Low-code platform for automating business
processes in manufacturing. IFAC-PapersOnLine, 57(19), 1400-1405.
