通过嵌入项目图书馆知识图谱进行神经图书馆推荐

IF 6.5 1区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

IEEE Transactions on Software Engineering Pub Date : 2024-04-24 DOI:10.1109/TSE.2024.3393504

Bo Li;Haowei Quan;Jiawei Wang;Pei Liu;Haipeng Cai;Yuan Miao;Yun Yang;Li Li

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引用次数: 0

摘要

软件应用的繁荣给开发人员带来了激烈的市场竞争。利用第三方库（TPL）为开发中的项目增加新功能，缩短产品上市时间，已成为社会上流行的一种方式。然而，由于可供使用的第三方库数量巨大，开发人员如何有效、高效地识别最合适的第三方库是一项挑战。为了解决这一障碍，我们提出了一种名为 PyRec 的创新方法，为开发人员的项目推荐潜在有用的 TPL。以 Python 项目开发为例，PyRec 将 Python 项目、TPL、上下文信息以及这些实体之间的关系嵌入知识图谱。然后，它利用图神经网络从图中捕捉有用信息，从而提出 TPL 建议。与现有方法不同的是，PyRec 不仅能充分利用项目与库之间的交互信息，还能充分利用上下文信息，从而做出更准确的 TPL 推荐。我们基于 12,421 个 Python 项目进行了综合评估，这些项目涉及 963 个 TPL、9,675 个额外实体、121,474 条图书馆使用记录和 73,277 条上下文记录。与五种具有代表性的方法相比，PyRec 在所有情况下都显著提高了推荐性能。

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Neural Library Recommendation by Embedding Project-Library Knowledge Graph

The prosperity of software applications brings fierce market competition to developers. Employing third-party libraries (TPLs) to add new features to projects under development and to reduce the time to market has become a popular way in the community. However, given the tremendous TPLs ready for use, it is challenging for developers to effectively and efficiently identify the most suitable TPLs. To tackle this obstacle, we propose an innovative approach named PyRec to recommend potentially useful TPLs to developers for their projects. Taking Python project development as a use case, PyRec embeds Python projects, TPLs, contextual information, and relations between those entities into a knowledge graph. Then, it employs a graph neural network to capture useful information from the graph to make TPL recommendations. Different from existing approaches, PyRec can make full use of not only project-library interaction information but also contextual information to make more accurate TPL recommendations. Comprehensive evaluations are conducted based on 12,421 Python projects involving 963 TPLs, 9,675 extra entities, 121,474 library usage records, and 73,277 contextual records. Compared with five representative approaches, PyRec improves the recommendation performance significantly in all cases.

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来源期刊

IEEE Transactions on Software Engineering 工程技术-工程：电子与电气

CiteScore

9.70

自引率

10.80%

发文量

724

审稿时长

6 months

期刊介绍： IEEE Transactions on Software Engineering seeks contributions comprising well-defined theoretical results and empirical studies with potential impacts on software construction, analysis, or management. The scope of this Transactions extends from fundamental mechanisms to the development of principles and their application in specific environments. Specific topic areas include: a) Development and maintenance methods and models: Techniques and principles for specifying, designing, and implementing software systems, encompassing notations and process models. b) Assessment methods: Software tests, validation, reliability models, test and diagnosis procedures, software redundancy, design for error control, and measurements and evaluation of process and product aspects. c) Software project management: Productivity factors, cost models, schedule and organizational issues, and standards. d) Tools and environments: Specific tools, integrated tool environments, associated architectures, databases, and parallel and distributed processing issues. e) System issues: Hardware-software trade-offs. f) State-of-the-art surveys: Syntheses and comprehensive reviews of the historical development within specific areas of interest.