Using Hierarchical Latent Dirichlet Allocation to Construct Feature Tree for Program Comprehension

• Published in: Scientific programming 2017-01, Vol.2017 (2017), p.1-11
• Main Authors: Li, Bin, Duan, Yucong, Liu, Xiangyue, Sun, Xiaobing
• Format: Article
• Language: English
• Subjects: Clustering; Dirichlet problem; Hierarchies
• ISSN: 1058-9244; 1875-919X
• DOI: 10.1155/2017/4382348

Program comprehension is an important task faced by developers during software maintenance. With the increasing complexity of evolving systems, program comprehension becomes more and more difficult. In practice, programmers are accustomed to getting a general view of the features in a software system and then finding the interesting or necessary files to start the understanding process. Given a system, developers may need a general view of the system. The traditional view of a system is shown in a package-class structure which is difficult to understand, especially for large systems. In this article, we focus on understanding the system in both feature view and file structure view. This article proposes an approach to generate a feature tree based on hierarchical Latent Dirichlet Allocation (hLDA), which includes two hierarchies, the feature hierarchy and file structure hierarchy. The feature hierarchy shows the features from abstract level to detailed level, while the file structure hierarchy shows the classes from whole to part. Empirical results show that the feature tree can produce a view for the features and files, and the clustering of classes in the package in our approach is better (in terms of recall) than the other clustering approach, that is, hierarchical clustering.