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2021 | OriginalPaper | Chapter

Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder

Authors : Fumin Qi, Xiao-Yuan Jing, Xiaoke Zhu, Xiaodong Jia, Li Cheng, Yichuan Dong, Ziseng Fang, Fei Ma, Shengzhong Feng

Published in: Parallel and Distributed Computing, Applications and Technologies

Publisher: Springer International Publishing

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Abstract

In Software Effort Estimation (SEE) practice, the data drought problem has been plaguing researchers and practitioners. Leveraging heterogeneous SEE data collected by other companies is a feasible solution to relieve the data drought problem. However, how to make full use of the heterogeneous effort data to conduct SEE, which is called as Heterogeneous Software Effort Estimation (HSEE), has not been well studied. In this paper, we propose a HSEE model, called Dynamic Heterogeneous Software Effort Estimation (i.e., DHSEE), which leverages the adversarial auto-encoder and convolutional neural network techniques. Meanwhile, we have investigated the scenario of conducting HSEE with dynamically increasing effort data. Experiments on ten public datasets indicate that our approach can significantly outperform the state-of-the-art HSEE method and other competing methods on both static and dynamic SEE scenarios.

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previous chapter Blood Leukocyte Object Detection According to Model Parameter-Transfer and Deformable Convolution

next chapter A Novel Distributed Reinforcement Learning Method for Classical Chinese Poetry Generation

https://zenodo.org/communities/seacraft/search?keywords=effort.

https://sites.google.com/site/whuxyjfmq/home/see_osp_dataset.

https://github.com/CAAE-HSEE/DHSEE.

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Title: Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder
Authors: Fumin Qi
Xiao-Yuan Jing
Xiaoke Zhu
Xiaodong Jia
Li Cheng
Yichuan Dong
Ziseng Fang
Fei Ma
Shengzhong Feng
Publisher: Springer International Publishing
Book: Parallel and Distributed Computing, Applications and Technologies
Print ISBN: 978-3-030-69243-8

Electronic ISBN: 978-3-030-69244-5

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-69244-5_2

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