Story Point Estimation Using Issue Reports With Deep Attention Neural Network

Background: Estimating the effort required for software engineering tasks is incredibly tricky, but it is critical for project planning. Issue reports are frequently used in the agile community to describe tasks, and story points are used to estimate task effort. Aim: This paper proposes a machine learning regression model for estimating the number of story points needed to solve a task. The system can be trained from raw input data to predict outcomes without the need for manual feature engineering. Method: Hierarchical attention networks are used in the proposed model. It has two levels of attention mechanisms implemented at word and sentence levels. The model gradually constructs a document vector by grouping significant words into sentence vectors and then merging significant sentence vectors to create document vectors. Then, the document vectors are fed into a shallow neural network to predict the story point. Results: The experiments show that the proposed approach outperforms the state-of-the-art technique Deep-S which uses Recurrent Highway Networks. The proposed model has improved Mean Absolute Error (MAE) by an average of 16.6% and has improved Median Absolute Error (MdAE) by an average of 53%. Conclusion: An empirical evaluation shows that the proposed approach outperforms the previous work

ARTIFICIAL NEURAL NETWORK FOR TEXTURE CLASSIFICATION USING SEVERAL FEATURES: A COMPARATIVE STUDY

ABSTRACT Texture analysis plays an essential and a major rule in image classification and segmentation in a wide range of applications such as medical imaging, remote sensing and industrial inspection. In this paper, we review the well known approaches of texture feature extraction and perform a comparative study between them. These approaches are namely gray level histogram, edge detection, and co-occurrence matrices, besides Gabor and Biorthogonal wavelet transformations. The feed forward artificial neural network (ANN) with back-propagation algorithm (BPA) is used as a supervised classifier. Experiments are conducted on two different datasets taken from multi-class engineering surfaces produced by six machining processes and from Brodatz (1966) textures album respectively. The classification accuracy is tested for both datasets, while the quality of estimation is tested for surface roughness parameters of the machined surfaces dataset only based on the roughness parameters evaluated from a contact measurement test