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Tightly coupled and interdependent systems inhibit productivity by requiringdevelopers to carefully coordinate their changes, even when modifyingsubsystems that should be independent of one another. Poor architecturaldecisions frequently lead to the presence of large, change-prone source filesthat are at the center of complexes of technical debt. This kind of technicaldebt quickly incurs interest both through coordination costs anderror-proneness. In this paper, we present a new approach for decomposing these``large-active'' files to pay down critical technical debt. We package ourapproach as a refactoring recommendation system. Each recommendation isdetermined by analyzing patterns of co-change and mutual dependency amongfiles. Each recommendation corresponds to a responsibility the large-activefile has in relation to the rest of the system. By moving recommendedfunctionality from the large-active file into smaller files, developers canreduce the impact of a debt-laden file and clarify its essentialresponsibilities. A key advantage of this approach over prior work is that webetter focus effort; we avoid spending developer effort refactoring code thatis only superficially problematic. We achieve this by incorporating revisionhistory into both determining and ranking recommendations. Each recommendationcorresponds to some change-prone responsibility. We present some examples ofthis approach in action and outline our future plans.

Towards the Assisted Decomposition of Large-Active Files