Pith. sign in

REVIEW 3 cited by

Utilizing Deep Learning to Optimize Software Development Processes

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2404.13630 v2 pith:PZ47XYOW submitted 2024-04-21 cs.SE cs.AIcs.CLcs.LG

Keqin Li , Armando Zhu , Peng Zhao , Jintong Song , Jiabei Liu This is my paper · ORCID
classification cs.SEcs.AIcs.CLcs.LG
keywords deepdevelopmentlearningsoftwarecodetechnologieserrorexperimental
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation

    cs.SE 2025-01 conditional novelty 5.0 of 10

    An LLM code optimizer using control-flow-graph differences and retrieved examples reports 7.3% average runtime reduction on 116 C++ programs versus zero-shot GPT-4o.

  2. DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network

    cs.CV 2024-12 reject novelty 4.0 of 10

    DTSGAN adapts SinGAN-style multi-scale generation to video with 3D convolutions and a sliding-window data update, claiming improved dynamic texture synthesis and diversity.

  3. Detecting and Classifying Defective Products in Images Using YOLO

    cs.CV 2024-12 reject novelty 2.0 of 10

    An unverifiable report that a YOLO variant with ResC2Net, SPPF, and PConv modules detects machine-part defects at mAP 0.91 without comparing to any baseline.

Pith tools