About Us

At the ByteDance Software Engineering Lab, we pride ourselves on being at the forefront of technological innovation. Our dedication is to crafting the premier development platform, with advanced technologies like Large Language Models, Generative AI, LangChain, Knowledge Graphs, Graph Neural Networks, and Reinforcement Learning. Our mission is to merge research and development efficiency with sophisticated domain models, driving forward practical business applications.

Where are we?

• Beijing / Shenzhen / Hangzhou / Shanghai, Mainland China 🇨🇳
• San Jose, United States 🇺🇸
• Singapore 🇸🇬

Contact us

We are hiring

• San Jose

  • Tech Lead Manager
  • Senior Software Engineer - Code AI/GPT
  • Senior Machine Learning Engineer - Code AI/GPT

• Singapore

  • Machine Learning Engineer

• Beijing

  • Senior Machine Learning Engineer
  • LLM for Code Research and Development
  • Data Mining Expert
  • Program Analysis Researcher

• Shenzhen

  • Senior Machine Learning Engineer
  • Data Mining Expert
  • Program Analysis Researcher

• Hangzhou

  • Senior Machine Learning Engineer
  • Data Mining Expert
  • Program Analysis Researcher

• Shanghai

  • Full Stack Software Engineer
  • Codebase Infrastructure Engineer

• Research Interns

  • Beijing

Teams

Code Intelligence

• Training, Evaluation and Deployment of LLMs for code
• The application of Code LLMs in the industry
• Static analysis and hyperscale code graphs
• Cutting-edge research in AI for software engineering and software engineering for AI.

Codebase Systems

We build codebase systems and related products including

• code hosting,
• code review,
• code analysis,
• code intelligence,
• CI/CD,
• Cloud IDE

and their synergy with AI.

Industrial Research

We also provide research intern positions and are looking for well-motivated current master students and PhD candidates to join our research group. Our research interests include:

• SE for AI and AI for SE.
• Software engineering and testing,
• Programming languages, compilers, program analysis, repair and synthesis,

Should you have any questions about internship opportunities, feel free to contact Dr. Chao Peng (email provided above).

Collaborated Universities

We have established cooperative relationship with top universities over the world.

Publications

1. Liu, Chenyan#, Yufan Cai#, Yun Lin*, Yuhuan Huang, Yunrui Pei, Bo Jiang, Ping Yang, Jin Song Dong, and Hong Mei (2024). “CoEdPilot: Recommending Code Edits with Learned Prior Edit Relevance, Project-wise Awareness, and Interactive Nature”. In Proceedings of ISSTA 2024. ACM, to appear.
2. Li, Bowen, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, ping yang, Dahua Lin, Chao Peng, Kai Chen (2024). “DevBench: A Comprehensive Benchmark for Software Development”. arXiv. [pdf] [tool].
3. Peng, Chao, Zhengwei Lv, Jiarong Fu, Jiayuan Liang, Zhao Zhang, Ajitha Rajan, Ping Yang (2024). “Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning”. In Proceedings of ICSE 2024 (Software Engineering in Practice). ACM, to appear.
4. Liang, Xiaoyun, Jiayi Qi, Yongqiang Gao, Chao Peng, Ping Yang (2023). “AG3: Automated Game GUI Text Glitch Detection based on Computer Vision”. In Proceedings of ESEC/FSE 2023 (Industry Track). ACM, 2023.
5. Jiang, Zongze, Ming Wen, Yixin Yang, Chao Peng, Ping Yang, Hai Jin (2023). “Effective Concurrency Testing for Go via Directional Primitive-constrained Interleaving Exploration”. In Proceedings of ASE 2023. IEEE, 2023.
6. Wang, Siwei, Xue Mao, Ziguang Gao, Yujun Gao, Qucheng Shen, and Chao Peng (2023). “NxtUnit: Automated Unit Test Generation for Go”. In Proceedings of EASE 2023 (Industry Track). 2023. [pdf]
7. Sun, Jingling, Ting Su, Kai Liu, Chao Peng, Zhao Zhang, Geguang Pu, Tao Xie, and Zhendong Su (2022). “Characterizing and Finding SystemSetting-Related Defects in Android Apps”. IEEE Transactions on Software Engineering. 2023. [pdf]
8. Lv, Zhengwei, Chao Peng, Zhao Zhang, Ting Su, Kai Liu, and Ping Yang (2022). “Fastbot2: Reusable Automated Model-based GUI Testing for Android Enhanced by Reinforcement Learning”. In Proceedings of ASE 2022 (Industry Track). ACM, 2022. [pdf] [tool: Android] [tool: iOS]
9. Peng, Chao, Yujun Gao, and Ping Yang (2022). “Automated Server Testing: an Industrial Experience Report”. In Proceedings of ICSME 2022 (Industry Track). IEEE, 2022. [pdf]
10. Peng, Chao, Zhao Zhang, Zhengwei Lv, and Ping Yang (2022). “MUBot: Learning to Test Large-Scale Commercial Android Apps like a Human”. In Proceedings of ICSME 2022 (Industry Track). IEEE, 2022. [pdf]
11. Wu, Qinyun, Huan Song and Ping Yang. “Real-World Clone-Detection in Go.” In Proceedings of MSR 2022 (Industry Track). ACM, 2022. [pdf]
12. Peng, Chao, Ajitha Rajan, and Tianqin Cai. “CAT: Change-focused Android GUI Testing.” In Proceedings of the 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2021. [pdf]
13. Cai, Tianqin, Zhao Zhang, and Ping Yang. “Fastbot: A Multi-Agent Model-Based Test Generation System Beijing Bytedance Network Technology Co., Ltd.” In Proceedings of the IEEE/ACM 1st International Conference on Automation of Software Test. 2020. [pdf]

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