Deep Learning in Open-Source Software Ecosystems

in Special Issue   Posted on February 26, 2021 

Information for the Special Issue

Submission Deadline: Tue 15 Jun 2021
Journal Impact Factor : 1.857
Journal Name : Automated Software Engineering
Journal Publisher:
Website for the Special Issue: https://www.springer.com/journal/10515/updates/18898402
Journal & Submission Website: https://www.springer.com/journal/10515

Special Issue Call for Papers:

Aims

With the rapid development of software technology and people’s increasing enthusiasm for programming, the open, flexible and efficient community group development method has been recognized and popularized all over the world. A large number of excellent open-source software that brings together group wisdom has emerged and is widely used. Under such a development trend, many project participants collaborate with each other and their interests are related to each other, gradually forming various open-source software ecosystems which has become an important research content in software engineering. Due to the large scale of the open-source software ecosystems, the activities of its participants are complicated, and the capabilities of developers are different. Moreover, its data has the characteristics of heterogeneous, multi-modal and dynamics. These characteristics bring challenges to the research on open-source software ecosystems. So, there is a growing necessity to develop more optimized methodologies that are able to process these data and deal with various problems in software ecosystems. Meanwhile, deep learning is revolutionizing numrous research areas, such as computer vision, natural language processing, and pattern recognition, etc., because it is capable to extracting useful data, discriminating parameters, and knowledge representations, benefitting from the availability of the very large datasets. Thus, it is critical to explore deep learning techniques to address above challenges in open-source software ecosystem analytics.

This special issue aims to contribute to bring together the state-of-the-art deep learning research contributions that address the key aspects of open-source software ecosystem analytics. We focus on more accurate and explainable solutions which are potential for deployment in a range of future open-source software ecosystem applications.

Topics

The topics of interest include, but not limited to:

  • Deep learning models in open-source software ecosystem applications
  • Multimodal data acquisition in open-source software ecosystems
  • Novel datasets and benchmarks for heterogeneous open-source software ecosystem analytics
  • Group collaboration mechanism analysis via deep learning
  • Community characteristic in open-source software ecosystems
  • Understanding life cycle of the component modules of the open-source software ecosystems
  • Prediction and/or recommendation models to forecast software ecosystems issues or improve their health
  • Evolution of software ecosystems and their health
  • Prediction of software project characteristics
  • Other topics in open-source software ecosystems

Important Dates

Manuscript submission deadline: June 15, 2021

Round 1 Decisions: Sept 15, 2021

Guest Editors

Prof. Honghao Gao, Shanghai University, China (Lead Guest Editor)

Prof. Alex Zhang, University of Auckland, New Zealand

Prof. Ramón J. Durán Barroso, Universidad de Valladolid, Spain

Prof. Xiong Luo, University of Science and Technology Beijing, China

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