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The stakeholders of a system are legitimately interested in whether and howits architecture reflects their respective concerns at each point of itsdevelopment and maintenance processes. Having such knowledge available at alltimes would enable them to continually adjust their systems structure at eachjuncture and reduce the buildup of technical debt that can be hard to reduceonce it has persisted over many iterations. Unfortunately, software systemsoften lack reliable and current documentation about their architecture. Inorder to remedy this situation, researchers have conceived a number ofarchitectural recovery methods, some of them concern-oriented. However, thedesign choices forming the bases of most existing recovery methods make it sonone of them have a complete set of desirable qualities for the purpose statedabove. Tailoring a recovery to a system is either not possible or only throughiterative experiments with numeric parameters. Furthermore, limitations intheir scalability make it prohibitive to apply the existing techniques to largesystems. Finally, since several current recovery methods employnon-deterministic sampling, their inconsistent results do not lend themselveswell to tracking a systems course over several versions, as needed by itsstakeholders. RELAX (RELiable Architecture EXtraction), a new concern-basedrecovery method that uses text classification, addresses these issuesefficiently by (1) assembling the overall recovery result from smaller,independent parts, (2) basing it on an algorithm with linear time complexityand (3) being tailorable to the recovery of a single system or a sequencethereof through the selection of meaningfully named, semantic topics. Anintuitive, informative architectural visualization rounds out RELAX'scontributions. RELAX is illustrated on a number of existing open-source systemsand compared to other recovery methods.

Recover and RELAX: Concern-Oriented Software Architecture Recovery for Systems Development and Maintenance