Multi-stream online transfer learning for software effort estimation: is it necessary?
Multi-stream online transfer learning for software effort estimation: is it necessary?
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DOI：
10.1145/3475960.3475988
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发表时间：
2021-08
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影响因子：
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通讯作者：
Leandro L. Minku
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作者：
Leandro L. Minku
随着时间的推移，描述软件项目的功能和它们所需的工作之间的关系发生变化，阻碍了机器学习模型的预测性能，这可能会影响软件工作估计(SEE)。为了应对这一点，大多数基于机器学习的SEE方法依赖于随着时间的推移接收大量的公司内部(WC)项目进行培训，成本高得令人望而却步。Dycom方法通过从跨公司(CC)项目转移知识来减少所需WC培训项目的数量。然而，它假定CC项目没有年表，并且在WC项目开始估计之前完全可用。鉴于考虑到年表以应对变化的重要性，也考虑到技术合作项目的年表可能是有益的。因此，本文调查了是否以及在什么情况下，将CC项目视为随着时间推移需要学习的多个数据流可能有助于改进SEE。为此，提出了Dycom的一个名为Oates的扩展，以实现多流在线学习，以便随着时间的推移可以学习传入的WC和CC数据流。然后，在一个案例研究中，使用从ISBSG存储库派生的四个不同场景，将Oates与Dycom和其他五种方法进行了比较。结果表明，在预先可用的CC项目数量较少的情况下，Oates提高了预测性能。因此，建议随着时间的推移将CC项目学习为多个数据流，以在这种情况下改进SEE。当预先可用的CC项目数量很大时，Oates获得了与最先进水平相似的预测性能。因此，CC数据流在此场景中是不必要的，但也不是有害的。
Software Effort Estimation (SEE) may suffer from changes in the relationship between features describing software projects and their required effort over time, hindering predictive performance of machine learning models. To cope with that, most machine learning-based SEE approaches rely on receiving a large number of Within-Company (WC) projects for training over time, being prohibitively expensive. The approach Dycom reduces the number of required WC training projects by transferring knowledge from Cross-Company (CC) projects. However, it assumes that CC projects have no chronology and are entirely available before WC projects start being estimated. Given the importance of taking chronology into account to cope with changes, it may be beneficial to also take the chronology of CC projects into account. This paper thus investigates whether and under what circumstances treating CC projects as multiple data streams to be learned over time may be useful for improving SEE. For that, an extension of Dycom called OATES is proposed to enable multi-stream online learning, so that both incoming WC and CC data streams can be learnt over time. OATES is then compared against Dycom and five other approaches on a case study using four different scenarios derived from the ISBSG Repository. The results show that OATES improved predictive performance over the state-of-the-art when the number of CC projects available beforehand was small. Learning CC projects over time as multiple data streams is thus recommended for improving SEE in such scenario. When the number of CC projects available beforehand was large, OATES obtained similar predictive performance to the state-of-the-art. Therefore, CC data streams are unnecessary in this scenario, but are not detrimental either.