Software Architecture Recovery Using Integrated Dependencies Based on Structural, Semantic, and Directory Information

Shiva Prasad Reddy Puchala, Jitender Kumar Chhabra, Amit Rathee

Source Title: International Journal of Information System Modeling and Design (IJISMD)13(1)

ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781683181699|DOI: 10.4018/IJISMD.297060

MLA

Puchala, Shiva Prasad Reddy, et al. "Software Architecture Recovery Using Integrated Dependencies Based on Structural, Semantic, and Directory Information." IJISMD vol.13, no.1 2022: pp.1-20. http://doi.org/10.4018/IJISMD.297060

APA

Puchala, S. P., Chhabra, J. K., & Rathee, A. (2022). Software Architecture Recovery Using Integrated Dependencies Based on Structural, Semantic, and Directory Information. International Journal of Information System Modeling and Design (IJISMD), 13(1), 1-20. http://doi.org/10.4018/IJISMD.297060

Chicago

Puchala, Shiva Prasad Reddy, Jitender Kumar Chhabra, and Amit Rathee. "Software Architecture Recovery Using Integrated Dependencies Based on Structural, Semantic, and Directory Information," International Journal of Information System Modeling and Design (IJISMD) 13, no.1: 1-20. http://doi.org/10.4018/IJISMD.297060

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Abstract

Architecture recovery techniques study dependencies in source code and reconstruct architecture. Most techniques either use structural or semantic dependencies and it is observed that the use of directory information helps in improving architecture recovery. The research carried out to date has focused on using the semantic information in a very limited manner, and directory information in a trivial manner without considering directory hierarchy. Further, all three (structural, semantic, and directory-structure) are reported to be very useful in architecture recovery but have not been used in a combined manner at all. So, this paper proposes a new scheme for architecture recovery using a weighted combination of all three dependencies. A new approach is designed to effectively mine semantic dependencies and extract directory dependencies. Finally, different dependency schemes are evaluated with four clustering algorithms on three open-source projects. The obtained results show that the proposed scheme performs better than the existing approaches in architecture recovery.