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Over the past three decades software engineering researchers have produced a wide range of techniques and tools for understanding the architectures of large, complex systems. However, these have tended to be one-off research projects, and their idiosyncratic natures have hampered research collaboration, extension and combination of the tools, and technology transfer. The area of software architecture is rich with disjoint research and development infrastructures, and datasets that are either proprietary or captured in proprietary formats. This paper describes a concerted effort to reverse these trends. We have designed and implemented a flexible and extensible infrastructure (SAIN) with the goal of sharing, replicating, and advancing software architecture research. We have demonstrated that SAIN is capable of incorporating the constituent tools extracted from three independently developed, large, long-lived software architecture research environments. We discuss SAIN’s ambitious goals, the challenges we have faced in achieving those goals, the key decisions made in SAIN’s design and implementation, the lessons learned from our experience to date, and our ongoing and future work. 

Architectural decay imposes real costs in terms of developer effort, system correctness, and performance. Over time, those problems are likely to be revealed as explicit implementation issues (defects, feature changes, etc.). Recent empirical studies have demonstrated that there is a significant correlation between architectural “smells”—manifestations of architectural decay—and implementation issues. In this paper, we take a step further in exploring this phenomenon. We analyze the available development data from 10 open-source software systems and show that information regarding current architectural decay in these systems can be used to build models that accurately predict future issue-proneness and change-proneness of the systems’ implementations. As a less intuitive result, we also show that, in cases where historical data for a system is unavailable, such data from other, unrelated systems can provide reasonably accurate issue- and change-proneness prediction capabilities.