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

TL;DR

This paper introduces FTBUILDER, an automated framework for constructing multi-level feature trees from domain-specific software repositories, enhancing artifact management efficiency and recommendation accuracy.

Contribution

It presents a novel automated method for building multi-level feature trees using clustering and LLMs, reducing manual effort and improving artifact management.

Findings

Higher tree quality with 9% better silhouette coefficient

11% increase in GValue for the constructed tree

26% time savings in artifact selection

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.