A Backpropagation Neural Network for Splitting Identifiers

Abstract

Splitting identifiers is a task that has been addressed in the past few years in order to contribute toward improving the Feature Location task. Feature Location aims at determining the exact position of a specific feature within a source code. Several research studies have addressed the process of splitting multi-word identifiers. However, one of the endure gaps that still face the use of machine learning lies on using probabilistic algorithms which may seem insufficient compared to other sophisticated algorithms such as the Backpropagation Neural Network (BPNN). Therefore, this paper proposes a BPNN for the splitting identifiers task. A benchmark of source code dataset has been used in the experiments. In addition, different objective functions have been used including Tanh, Sigmoid and Softmax. Results showed that Softmax has outperformed the other objective funciton by achieving a 71.4% of f-measure. This results implies the usefulness of BPNN in terms of handling character-based problems.

A combination of least significant bit and deflate compression for image steganography

“…(0,0)𝑇(0,0)ℎ(0,0)𝑖(0,0)𝑠(0,0)(0,0)𝑚(0,0)𝑒(4,2)(0,0)𝑎(0,0)𝑔(7,1)(5,1)(3,3)(4,1)(7,1)(0,0) 𝑐(0,0)𝑟(7,1)(0,0)𝑡 The Huffman table can be initiated by considering only the unique characters along with their frequencies as shown in Table 3. After that, the Huffman tree is generated in which the unique characters are forming the nodes [26], [27]. Then, the smallest frequency nodes will start to initiate the edges as shown in Figure 2.…”

Design of a Machine Learning-based Approach for Fragment Retrieval on Models

“…According to the number of articles for each task, ML techniques are more commonly used for Bug Localization than for Traceability Link Recovery or for Feature Location. While 47% of the articles apply ML techniques for Bug [14], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59] [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85] [10], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97],…”