利用社交上下文嵌入，提升开发者社区中缺陷解决方案的推荐准确率。
A Hybrid LTR-based System via Social Context Embedding for Recommending Solutions of Software Bugs in Developer Communities
- 基于深度学习构建融合社交上下文的排序模型
- 在每条问题前10名推荐答案中正确率达78%
- 适合需要快速定位代码缺陷解决方案的开发者
Stack Overflow 等问答平台在帮助开发者解决软件错误和缺陷方面发挥关键作用。然而，在大量候选答案中搜索最优解耗时且效率低。本研究评估了数据挖掘与机器学习技术在该场景下的有效性，提出一种基于学习排序（LTR）的混合推荐系统，通过深入挖掘 Stack Overflow 的问答内容，利用众包知识推荐缺陷解决方案。采用自然语言处理、文本挖掘及推荐算法，结合社交上下文嵌入特征构建模型，实现对相关答案的有效提取与排序。实验表明，该模型在推荐每个问题前10个答案时，正确率接近78%。
原文摘要 · Abstract (English)
Questions and Answering forums such as Stack Overflow play an important role in supporting software developers in finding answers to queries related to issues such as software errors and bugs. However, searching through a large set of candidate answers could be time consuming and may not lead to the best solution. In this research, the effectiveness of data mining models and machine learning techniques to solve this kind of problems is evaluated. We propose a recommender system to aid developers in finding solutions to their software bugs by carefully mining Stack Overflow. The proposed model leverages the knowledge available through crowdsourcing the Q&A available in Stack Overflow to recommend a solution to software bugs. We use deep learning techniques to construct the required Learning-to-Rank (LTR)-based model using the social context embedding the Stack Overflow features. Text mining, natural language processing and recommendation algorithms are used to extract, evaluate and recommend the best relevant bug solutions. Additionally, our model achieves nearly 78% correct solutions when recommending the 10 best answers for each question.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。