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.

Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder

As an important tool of multi-way/tensor data analysis tool, Tucker decomposition has been applied widely in various fields But traditional sequential Tucker algorithms have been outdated because tensor data is growing rapidly in term of size To address this problem, in this paper, we focus on parallel Tucker decomposition of dense tensors on distributed-memory systems The proposed method uses Hierarchical SVD to accelerate the SVD step in traditional sequential algorithms, which usually takes up most computation time The data distribution strategy is designed to follow the implementation of Hierarchical SVD We also find that compared with the state-of-the-art method, the proposed method has lower communication cost in large-scale parallel cases under the assumption of the α–β model

Yichuan Dong

Papers

Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder

Distributed Dense Tucker Decomposition Based on Hierarchical SVD