Machine learning based software effort estimation using development-centric features for crowdsourcing platform

Multi-label text classification is a method for categorizing textual data based on features extracted from the original textual information. When it comes to modelling text structural properties, Graph Convolutional Network (GCN) has demonstrated outstanding performance. However, most existing graph-based models do not model the structure of a single text unit and do not consider the sequence information in each document (e.g., word order). To resolve these issues and fully utilize the text’s structural and sequential details, a text classification model called Sequential GCN with Multi-Head Attention (SGCN-MHA) is proposed in this paper. For each text, a separate text graph is constructed in which nodes are the words of the text, and the edges between nodes corresponding to the word relations. Then the GCN is used to extract the structural feature. To enable the word nodes in the document graph to hold contextual information, the BiLSTM is also applied to learn the sequential feature for each graph. Finally, the Multi-Head Attention mechanism is adopted to interact with these two features and then aggregate them to get access to critical information in the text. The efficiency of our approach has been tested on two standard datasets, including comparative and ablation experiments.