Published April 29, 2024 | Version v2
Journal article Open

Utilizing Deep Learning to Optimize Software Development Processes

  • 1. AMA University
  • 2. Carnegie Mellon University
  • 3. Microsoft
  • 4. Boston University
  • 5. Northeastern University

Description

This study explores the application of deep learning technologies in software development processes, particularly in automating code reviews, error prediction, and test generation to enhance code quality and development efficiency. Through a series of empirical studies, experimental groups using deep learning tools and control groups using traditional methods were compared in terms of code error rates and project completion times. The results demonstrated significant improvements in the experimental group, validating the effectiveness of deep learning technologies. The research also discusses potential optimization points, methodologies, and technical challenges of deep learning in software development, as well as how to integrate these technologies into existing software development workflows.

Files

v1n1a10.pdf

Files (349.9 kB)

Name Size Download all
md5:925a7cecf9e18b63e3a3a6bfe8816da3
349.9 kB Preview Download

Additional details

References

  • [1] PĂ©rez, Eduardo, et al. "Integrating AI in NDE: Techniques, Trends, and Further Directions." arXiv preprint arXiv:2404.03449 (2024).
  • [2] Yao, Jiawei, et al. "Ndc-scene: Boost monocular 3d semantic scene completion in normalized device coordinates space." 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE Computer Society, 2023.
  • [3] Chen, Jungang, Eduardo Gildin, and John E. Killough. "Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls." arXiv preprint arXiv:2310.06319 (2023).
  • [4] Li, Mingrui, et al. "DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM." arXiv preprint arXiv:2401.01545 (2024).
  • [5] Zhang, Yijun, and Baoquan Chen. "Cloud-based Bug Tracking Software Defects Analysis Using Deep Learning." Journal of Cloud Computing 10.1 (2021).
  • [6] Wang, Xiaosong, et al. "Advanced Network Intrusion Detection with TabTransformer." Journal of Theory and Practice of Engineering Science 4.03 (2024): 191-198.
  • [7] Wang, Jin, et al. "Research on Emotionally Intelligent Dialogue Generation Based on Automatic Dialogue System." arXiv preprint arXiv:2404.11447 (2024).
  • [8] Yao, Jiawei, Tong Wu, and Xiaofeng Zhang. "Improving depth gradient continuity in transformers: A comparative study on monocular depth estimation with cnn." arXiv preprint arXiv:2308.08333 (2023).
  • [9] Chen, Jungang, Eduardo Gildin, and John E. Killough. "Physics-informed Convolutional Recurrent Surrogate Model for Reservoir Simulation with Well Controls." arXiv preprint arXiv:2305.09056 (2023).
  • [10] Yao, Jiawei, et al. "Building lane-level maps from aerial images." ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024.
  • [11] Weng, Yijie, and Jianhao Wu. "Fortifying the Global Data Fortress: A Multidimensional Examination of Cyber Security Indexes and Data Protection Measures across 193 Nations." International Journal of Frontiers in Engineering Technology 6.2 (2024): 13-28.
  • [12] Wang, Han, et al. "Jointly Learning Selection Matrices For Transmitters, Receivers And Fourier Coefficients In Multichannel Imaging." arXiv preprint arXiv:2402.19023 (2024).
  • [13] Zhou, Yiming, et al. "Semantic Wireframe Detection." (2023).
  • [14] Zhu, Ziwei, and Wenjing Zhou. "Taming heavy-tailed features by shrinkage." International Conference on Artificial Intelligence and Statistics. PMLR, 2021.
  • [15] Read, Andrew J., et al. "Prediction of Gastrointestinal Tract Cancers Using Longitudinal Electronic Health Record Data." Cancers 15.5 (2023): 1399.
  • [16] Zhao, Peng, Chao Qi, and Dian Liu. "Resource-constrained Hierarchical Task Network planning under uncontrollable durations for emergency decision-making." Journal of Intelligent & Fuzzy Systems 33.6 (2017): 3819-3834.
  • [17] Zhao, Peng, et al. "HTN planning with uncontrollable durations for emergency decision-making." Journal of Intelligent & Fuzzy Systems 33.1 (2017): 255-267.
  • [18] Qi, Chao, et al. "Hierarchical task network planning with resources and temporal constraints." Knowledge-Based Systems 133 (2017): 17-32.
  • [19] Wang, Hong-Wei, et al. "Review on hierarchical task network planning under uncertainty." Acta Autom. Sin 42 (2016): 655-667.
  • [20] Liu, Dian, et al. "Hierarchical task network-based emergency task planning with incomplete information, concurrency and uncertain duration." Knowledge-Based Systems 112 (2016): 67-79.
  • [21] Liu, Tianrui, et al. "News recommendation with attention mechanism." arXiv preprint arXiv:2402.07422 (2024).
  • [22] Li, Yanjie, et al. "Transfer-learning-based network traffic automatic generation framework." 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2021.
  • [23] Liu, Tianrui, et al. "Image Captioning in news report scenario." arXiv preprint arXiv:2403.16209 (2024).
  • [24] Liu, Tianrui, et al. "Rumor Detection with a novel graph neural network approach." arXiv preprint arXiv:2403.16206 (2024).
  • [25] Chen, Jungang, Eduardo Gildin, and John E. Killough. "Optimization of Pressure Management Strategies for Geological CO2 Sequestration Using Surrogate Model-based Reinforcement Learning." arXiv preprint arXiv:2403.07360 (2024).
  • [26] Liu, Tianrui, et al. "Particle Filter SLAM for Vehicle Localization." arXiv preprint arXiv:2402.07429 (2024).
  • [27] Su, Jing, et al. "Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review." arXiv preprint arXiv:2402.10350 (2024).
  • [28] Ru, Jingyu, et al. "A Bounded Near-Bottom Cruise Trajectory Planning Algorithm for Underwater Vehicles." Journal of Marine Science and Engineering 11.1 (2022): 7.
  • [29] Zi, Yun, et al. "Research on the Application of Deep Learning in Medical Image Segmentation and 3D Reconstruction." Academic Journal of Science and Technology 10.2 (2024): 8-12.
  • [30] Chen, Jungang, et al. "Generating subsurface earth models using discrete representation learning and deep autoregressive network." Computational Geosciences 27.6 (2023): 955-974.