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.
Supported by National supercomputing center in Shenzhen.
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Acknowledgement
The authors would like to thank Dr. Federica Sarro of Department of Computer Science, University College London, UK for providing code of the LP4EE code. This research was supported by the National Key R&D Program of China (Grant No. 2018YFB0204403) and the Project of Chinese Postdoctoral Science Foundation No. 2019M652624.
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Qi, F. et al. (2021). Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder. In: Zhang, Y., Xu, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2020. Lecture Notes in Computer Science(), vol 12606. Springer, Cham. https://doi.org/10.1007/978-3-030-69244-5_2
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