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Open nowPosted 190 days ago

【实习】基于LLM的软件缺陷检测技术的探索和研究-质量技术

ByteDance19,258 open roles

Where
Hangzhou, Zhejiang, Chinese Mainland
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Your applicationOpen now【实习】基于LLM的软件缺陷检测技术的探索和研究-质量技术ByteDance · Hangzhou, Zhejiang, Chinese Mainland

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The clock on this job

Early applications get read.

11.4% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 23 days.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: posted 190 days ago

ByteDance median: 23 days open

The posting

团队介绍：字节跳动质量技术团队，我们聚焦质量领域前沿创新，打造稳定、高效、领先的质量技术，深度赋能抖音、今日头条、豆包、电商等亿级用户产品。 当前，我们通过AI来检测代码、架构、产品的缺陷、风险和效果，用AI重构质量保障模式，提升研发效能。聚焦智能缺陷检测、GUI agent、AI评测等前沿方向，希望通过质量技术的突破，用AI改变测试行业。欢迎加入，与我们一起，定义和构建下一代质量技术，推动行业前进。

课题介绍： 随着大模型技术在软件开发领域的广泛应用，代码开发效率显著提升，AI生成代码的质量不确定性，都为软件测试带来了全新挑战。传统静态分析方法在复杂业务逻辑校验、功能一致性验证等场景存在明显局限，而动态GUI测试与API测试则面临执行开销高、测试覆盖度不足等问题。 本课题旨在构建一种基于LLM的智能缺陷检测系统。通过对需求文档和技术方案的语义理解，结合跨端、跨仓的代码调用关系的刻画，产出业务逻辑与代码实现的精准对齐，在此基础上，构建具备对各类缺陷特征有充分感知的缺陷检测智能体，高效、精准的识别业务逻辑、架构性能、程序安全等复杂缺陷。

课题挑战： 1、公司多样化业务场景下，对多模态需求的语义理解和功能逻辑提取； 2、跨端、跨仓、多技术栈的代码链路分析； 3、海量缺陷数据的特征分析和提取； 4、支持大规模应用的Agent系统工程；

课题价值： 1、保证软件质量的要求下，构建一种以静态检测技术为主的软件测试模式，优化测试资源的配置和效率； 2、实现软件缺陷的早期发现，降低缺陷识别和修复的成本，显著提升研发效能。

1、2027届及以后毕业，博士在读，人工智能、计算机、数学相关专业优先； 2、对LLM在代码领域的应用有较大的兴趣，有一定实践经验者优先； 3、出色的问题分析能力，能够自主探索前沿技术，并积极应用到业务问题解决中； 4、良好的沟通联动能力，与团队持续探索&学习新技术，实现先进技术的不断落地； 5、优秀的代码能力、数据结构和基础算法功底； 6、在LLM、Agent架构领域有扎实的理论和实践基础，有发表顶会论文优先，有高影响力的开源项目者优先。

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