一句话：首次提出基于大语言模型的FPA领域专家FPA-EX，自动从SRS抽取功能点条目，提升分析准确率与效率。
功能点分析（FPA）是软件开发前估算规模、成本与工期的重要步骤，但人工从软件需求规格说明（SRS）中拆分功能点条目效率低、成本高。本文首次提出基于AI的FPA领域专家FPA-EX，利用大语言模型自动抽取软件功能点条目。作者收集并标注了涵盖多领域SRS的778个问答对，构建FPA数据集；在此基础上提出密集监督微调（DSFT），在人类增强文本上进行条目级优化，以保证功能点输出精确；并设计ConceptAct提示（CAP）过程以支持正确逻辑推理。实验表明FPA-EX性能优越，F1分数比GPT-3.5高0.491；实际应用中显著提升FP分析师生产力，推动更智能的工作模式。
Estimating software functional size is a crucial initial step before development, impacting costs and timelines. This involves applying standard Function Point Analysis (FPA) to the Software Requirements Specification (SRS). However, manual analysis by Function Point (FP) analysts during the splitting of FP entries from SRS remains inefficient and costly. To address this issue, for the first time, we propose an AI-based domain expert for FPA, named FPA-EX. It employs a large language model (LLM), intelligently extracts software FP entries from SRS, providing automated support to enhance efficiency. Specifically, we construct a multi-domain FPA dataset through collecting and annotating 778 question-answer pairs related to various SRS. Based on this dataset, we present a novel densely supervised fine-tuning (DSFT) on LLM, which performs entries-level optimization over the human augmented text, ensuring precise FPs outputs. Finally, we design a ConceptAct Promting (CAP) process for correct logical reasoning. Experiments demonstrate the superior performance of FPA-EX, particularly higher than GPT3.5 by 0.491 on F1 scores. Furthermore, in practical application, FPA-EX significantly enhances the productivity of FP analysts, contributing to a shift towards more intelligent work patterns.
DOI 原文 ·
@inproceedings{paperbot8327,
title = {Emergence of A Novel Domain Expert: A Generative AI-based Framework for Software Function Point Analysis},
author = {Zheng Zhao and Hongxiang Jiang and Ran Zhao and Bing He},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695293}
}