Inconsistency Detection

The ArDoCo inconsistency detection approach uses trace link recovery to detect inconsistencies between natural-language architecture documentation and formal models. It identifies two kinds of issues:

(a) Unmentioned Model Elements (UMEs): components or interfaces that appear in the model but are never described in the documentation; (b) Missing Model Elements (MMEs): elements mentioned in the text that do not exist in the model.

The method runs a TLR procedure (namely SWATTR) and then flags any model element with no corresponding text link (a UME) or any sentence that refers to a non-modeled item (an MME).

• Detection strategy: Use the TLR results as a bridge. After linking as many sentences to model elements as possible, any “orphan” model nodes or text mentions indicate a consistency gap. For example, if the model has a “Cache” component with no sentence linked, that is an UME; if the doc talks about “Common” but the model lacks it, that is an MME.
• Results: The approach achieved an excellent F1 (0.81) for the underlying trace recovery. For inconsistency detection, it attained ~93% accuracy in identifying UMEs and ~75% for MMEs, significantly better than naive baselines. These results suggest that using trace links is a promising way to find documentation-model mismatches.