Yue Yu
ABSTRACT
The "Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of heterogeneous software resources have not been organized in a reasonable and efficient way. Software feature is an ideal material to characterize software resources. The effectiveness of feature-related tasks will be greatly improved, if a multi-grained feature repository is available. In this paper, we propose a novel approach for organizing, analyzing and recommending software features. Firstly, we construct a Hierarchical rEpository of Software feAture (HESA). Then, we mine the hidden affinities among the features and recommend relevant and high-quality features to stakeholders based on HESA. Finally, we conduct a user study to evaluate our approach quantitatively. The results show that HESA can organize software features in a more reasonable way compared to the traditional and the state-of-the-art approaches. The result of feature recommendation is effective and interesting.
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Index Terms
- Mining and recommending software features across multiple web repositories
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