A survey of software architectural change detection and categorization techniques

Software architecture is defined as the structural construction, design decisions implementation, evolution and knowledge sharing mechanisms of a system. Software architecture documentation help architects with decision making, guide developers during implementation, and preserve architectural decisions so that future caretakers are able to better understand an architect’s solution. Many modern-day software development teams are focusing more on architectural consistency of software design to better cope with the cost-time-efforts, continuous integration, software glitches, security backdoors, regulatory inspections, human values, and so on. Therefore, in order to better reflect the software design challenges, the development teams review the architectural design either on a regular basis or after completing certain milestones or releases. However, many studies have focused on architectural change detection and classification as the essential steps for reviewing design, discovering architectural tactics and knowledge, analyzing software stability, tracing and auditing software development history, recovering design decisions, generating design summary, and so on.

In this paper, we survey state-of-the-art architectural change detection and categorization techniques and identify future research directions. To the best of our knowledge, our survey is the first comprehensive report on this area. However, in this survey, we compare available techniques using various quality attributes relevant to software architecture for different implementation levels and types. Moreover, our analysis shows that there is a lack of lightweight techniques (in terms of human intervention, algorithmic complexity, and frequency of usage) feasible to process hundreds and thousands of change revisions of a project. We also realize that rigorous focuses are required for capturing the design decision associativity of the architectural change detection techniques for practical use in the design review process. However, our survey on architectural change classification shows that existing automatic change classification techniques are not promising enough to use for real-world scenarios and reliable post analysis of causes of architectural change is not possible without manual intervention. There is also a lack of empirical data to construct an architectural change taxonomy, and further exploration in this direction would add much value to architectural change management.