一句话：提出Fine-SE，融合语义特征与专家特征进行软件工作量估算，并在工业与开源项目上验证其有效性。
可靠的工作量估算对软件计划与管理至关重要，但计划扑克、类比等传统方法依赖人工且主观，难以迁移到其他项目。已有深度学习估算研究多分别利用专家特征或语义特征，二者最优结合及有效专家特征尚不明确。作者访谈26名企业员工，考察13种候选专家特征，提出Fine-SE模型，同时利用任务文本的语义特征与专家特征进行估算。基于全球ICT企业17个工业项目和4个开源项目、超过3万个软件任务进行评测。结果显示Fine-SE在MAE、MMRE和性能指标上优于基线：项目内估算较专家估算提升32.0%–45.2%，相较Deep-SE和GPT2SP在工业项目提升8.9%–91.4%，表明融合两类特征的价值。
Reliable effort estimation is of paramount importance to software planning and management, especially in industry that requires effective and on-time delivery. Although various estimation approaches have been proposed (e.g., planning poker and analogy), they may be manual and/or subjective, which are difficult to apply to other projects. In recent years, deep learning approaches for effort estimation that rely on learning expert features or semantic features respectively have been extensively studied and have been found to be promising. Semantic features and expert features describe software tasks from different perspectives, however, in the literature, the best combination of these two features has not been explored to enhance effort estimation. Additionally, there are a few studies that discuss which expert features are useful for estimating effort in the industry. To this end, we investigate the potential 13 expert features that can be used to estimate effort by interviewing 26 enterprise employees. Based on that, we propose a novel model, called Fine-SE, that leverages semantic features and expert features for effort estimation. To validate our model, a series of evaluations are conducted on more than 30,000 software tasks from 17 industrial projects of a global ICT enterprise and four open-source software (OSS) projects. The evaluation results indicate that Fine-SE provides higher performance than the baselines on evaluation measures (i.e., mean absolute error, mean magnitude of relative error, and performance indicator), particularly in industrial projects with large amounts of software tasks, which implies a significant improvement in effort estimation. In comparison with expert estimation, Fine-SE improves the performance of evaluation measures by 32.0%-45.2% in within-project estimation. In comparison with the state-of-the-art models, Deep-SE and GPT2SP, it also achieves an improvement of 8.9%-91.4% in industrial projects. The experimental results reveal the value of integrating expert features with semantic features in effort estimation.
DOI 原文 ·
@inproceedings{paperbot11279,
title = {Fine-SE: Integrating Semantic Features and Expert Features for Software Effort Estimation},
author = {Yue Li and Zhong Ren and Zhiqi Wang and Lanxin Yang and Liming Dong and Chenxing Zhong and He Zhang},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3623349}
}