Journal of Computer Science and Technology ›› 2018, Vol. 33 ›› Issue (6): 1278-1306.doi: 10.1007/s11390-018-1887-3

Special Issue: Data Management and Data Mining

• Regular Paper • Previous Articles     Next Articles

Mining Patterns from Change Logs to Support Reuse-Driven Evolution of Software Architectures

Aakash Ahmad1, Claus Pahl2, Ahmed B. Altamimi1, Abdulrahman Alreshidi1   

  1. 1. College of Computer Science and Engineering, University of Ha'il, Ha'il 2440, Saudi Arabia;
    2. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano 39100, Italy
  • Received:2017-11-28 Revised:2018-10-14 Online:2018-11-15 Published:2018-11-15
  • About author:Aakash Ahmad has received his Ph.D. degree in software engineering from Dublin City University, Dublin, Ireland, in 2015. He worked as a postdoctoral researcher at the IT University of Copenhagen and as a software engineer at Elixir Technologies. Dr. Ahmad is serving as an assistant professor at the College of Computer Science and Engineering, University of Ha'il, Ha'il, Saudi Arabia. Aakash's research interests include software architecture and software patterns for mobile and cloud computing systems.

Modern software systems are subject to a continuous evolution under frequently varying requirements and changes in systems' operational environments. Lehman's law of continuing change demands for long-living and continuously evolving software to prolong its productive life and economic value by accommodating changes in existing software. Reusable knowledge and practices have proven to be successful for continuous development and evolution of the software effectively and efficiently. However, challenges such as empirical acquisition and systematic application of the reusable knowledge and practices must be addressed to enable or enhance software evolution. We investigate architecture change logs-mining histories of architecture-centric software evolution-to discover change patterns that 1) support reusability of architectural changes and 2) enhance the efficiency of the architecture evolution process. We model architecture change logs as a graph and apply graph-based formalism (i.e., graph mining techniques) to discover software architecture change patterns. We have developed a prototype that enables tool-driven automation and user decision support during software evolution. We have used the ISO-IEC-9126 model to qualitatively evaluate the proposed solution. The evaluation results suggest that the proposed solution 1) enables the reusability of frequent architectural changes and 2) enhances the efficiency of architecturecentric software evolution process. The proposed solution promotes research efforts to exploit the history of architectural changes to empirically discover knowledge that can guide architecture-centric software evolution.

Key words: software architecture; software maintenance and evolution; evolution pattern; repository mining;

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