DeepDev-PERF: a deep learning-based approach for improving software performance
Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement, Neel Sundaresan, Chen Wu
Abstract
Improving software performance is an important yet challenging part of the software development cycle. Today, the majority of performance inefficiencies are identified and patched by performance experts. Recent advancements in deep learning approaches and the wide-spread availability of open-source data creates a great opportunity to automate the identification and patching of performance problems. In this paper, we present DeepDev-PERF, a transformer-based approach to suggest performance improvements for C# applications. We pretrain DeepDev-PERF on English and Source code corpora, followed by finetuning for the task of generating performance improvement patches for C# applications. Our evaluation shows that our model can generate the same performance improvement suggestion as the developer fix in 53
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get f13c961d-e5c2-4c72-bd8e-826b4815d4afCited by top-tier papers6
- Can Large Language Models Write Parallel Code?Daniel Nichols, Joshua Hoke Davis, Zhaojun Xie, Arjun Rajaram et al.HPDC 2024 · 30 citations
- Search-Based LLMs for Code OptimizationShuzheng Gao, Cuiyun Gao, Wenchao Gu, Michael R. LyuICSE 2025 · 11 citations
- PEACE: Towards Efficient Project-Level Efficiency Optimization via Hybrid Code EditingXiaoxue Ren, Jun Wan, Yun Peng, Zhongxin Liu et al.ASE 2025 · 5 citations
- Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree EditingLonghui Zhang, Jiahao Wang, Meishan Zhang, GaoXiong Cao et al.ACL 2025 · 1 citation
- SemRep : Generative Code Representation Learning with Code TransformationsWeichen Li, Jiamin Song, Bogdan Stoica, Arav Dhoot et al.ICML 2026
Related papers
- Automated Query Reformulation for Efficient Search based on Query Logs From Stack OverflowKaibo Cao, Chunyang Chen, Sebastian Baltes, Christoph Treude et al.ICSE 2021 · 63 citations
- Using Pre-Trained Models to Boost Code Review AutomationRosalia Tufano, Simone Masiero, Antonio Mastropaolo, Luca Pascarella et al.ICSE 2022 · 149 citations
- Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related TasksAntonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader-Palacio et al.ICSE 2021 · 9 citations
- CCT5: A Code-Change-Oriented Pre-trained ModelBo Lin, Shangwen Wang, Zhongxin Liu, Yepang Liu et al.FSE 2023 · 69 citations
- Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?Antonio Mastropaolo, Massimiliano Di Penta, Gabriele BavotaASE 2023 · 13 citations
