Automatic Categorization with Deep Neural Network for Open-Source Java Projects

Nguyen, Anh Tuan; Nguyen, Tien N.

doi:10.1109/icse-c.2017.118

Cited by 9 publications

References 9 publications

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A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

Santos

Vieira

Bravo

et al. 2021

J Software Evolu Process

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Deep learning has recently experienced explosive growth in use, largely due to advances in neural networks and the availability of large corpora of domain data. Project management activities generate and handle large volumes of data. Software engineering closely relates to project management, so software engineering projects must be prone to the use of neural networks. We seek to obtain an accurate vision of how neural networks are being used in software engineering projects through a systematic mapping study. We confirm that neural networks have already made their way into these projects; however, we show that their current uses are limited to certain repetitive and legacy tasks. Given uncovered ample room for expansion, we point out a few directions the industry and academy can lean toward to in the next years for taking better advantage of neural networks in software engineering projects and immediately advancing the field. We investigate if, how, and to what extent have neural networks been employed to the advancement of software engineering projects. As such, a systematic mapping study was conducted, which led to the conclusion that even though these algorithms have indeed been employed on several software engineering tasks, this employment so far has been shy, mostly relying on legacy types of neural networks. More modern variants, namely, deep learning algorithms, are slowly gaining momentum and should be the trend going forward.

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A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

Santos

Vieira

Bravo

et al. 2021

J Software Evolu Process

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A Multi-Module Based Method for Generating Natural Language Descriptions of Code Fragments

Gao

Jiang

et al. 2021

IEEE Access

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Code fragment natural language description generation, also known as code summarization, refers to obtaining a natural language sequence describing a given code fragment's functionality. It is broadly agreed that applying code summarization into production can significantly improve the efficiency of software development and maintenance. In recent years, syntactic analysis (SA) technology and Latent Dirichlet Allocation (LDA) has been widely used in code summarization and has achieved good results. However, most of the existing techniques focus on core code statements, and thus their generated code summarization lacks a logical description of the code fragment's holistic information. To this end, we propose a code summarization method based on multiple modules to generate natural language for each code statement by constructing a new type of natural language template. Meanwhile, to utilize the code fragment's holistic information, we adopt the code statement partition rules and cosine similarity measure to rank and optimize the weight of the overall information of the code fragment, and finally generate the holistic natural language description of the code fragment. The experimental results demonstrate that our method can generate more concise and logical natural language descriptions than existing models.

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Reinforcement-Learning-Guided Source Code Summarization Using Hierarchical Attention

Wang

Zhang

Sui

et al. 2022

IIEEE Trans. Software Eng.

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Automatic Categorization with Deep Neural Network for Open-Source Java Projects

Cited by 9 publications

References 9 publications

A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

A Multi-Module Based Method for Generating Natural Language Descriptions of Code Fragments

Reinforcement-Learning-Guided Source Code Summarization Using Hierarchical Attention

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