Automatic Multi-level Feature Tree Construction for Domain-Specific Reusable Artifacts Management

• URL: http://arxiv.org/abs/2506.03946v2
• Date: Sun, 06 Jul 2025 06:21:15 GMT
• Title: Automatic Multi-level Feature Tree Construction for Domain-Specific Reusable Artifacts Management
• Authors: Dongming Jin, Zhi Jin, Nianyu Li, Kai Yang, Linyu Li, Suijing Guan,
• Abstract summary: This paper proposes an automatic multi-level feature tree construction framework named FTBUILDER.<n>It automatically crawls domain-specific software repositories and merges their metadata to construct a structured artifact library.<n>It can save developers more time in selecting artifacts by 26% and improve the accuracy of artifact recommendations with GPT-4 by 235%.
• Score: 15.822095826931942
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid growth of open-source ecosystems (e.g., Linux) and domain-specific software projects (e.g., aerospace), efficient management of reusable artifacts is becoming increasingly crucial for software reuse. The multi-level feature tree enables semantic management based on functionality and supports requirements-driven artifact selection. However, constructing such a tree heavily relies on domain expertise, which is time-consuming and labor-intensive. To address this issue, this paper proposes an automatic multi-level feature tree construction framework named FTBUILDER, which consists of three stages. It automatically crawls domain-specific software repositories and merges their metadata to construct a structured artifact library. It employs clustering algorithms to identify a set of artifacts with common features. It constructs a prompt and uses LLMs to summarize their common features. FTBUILDER recursively applies the identification and summarization stages to construct a multi-level feature tree from the bottom up. To validate FTBUILDER, we conduct experiments from multiple aspects (e.g., tree quality and time cost) using the Linux distribution ecosystem. Specifically, we first simultaneously develop and evaluate 24 alternative solutions in the FTBUILDER. We then construct a three-level feature tree using the best solution among them. Compared to the official feature tree, our tree exhibits higher quality, with a 9% improvement in the silhouette coefficient and an 11% increase in GValue. Furthermore, it can save developers more time in selecting artifacts by 26% and improve the accuracy of artifact recommendations with GPT-4 by 235%. FTBUILDER can be extended to other open-source software communities and domain-specific industrial enterprises.

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