Inconsistency Detection

Note: As of ARDoCo V2, the terminology for inconsistency types has been updated for clarity:

• Text Entity Absent from Model (TEAM) - formerly “Missing Model Elements (MME)”
• Model Entity Absent from Text (MEAT) - formerly “Unmentioned/Undocumented Model Elements (UME)”

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) Model Entity Absent from Text (MEAT): components or interfaces that appear in the model but are never described in the documentation; (b) Text Entity Absent from Model (TEAM): 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 MEAT) or any sentence that refers to a non-modeled item (a TEAM).

• 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 a MEAT; if the doc talks about “Common” but the model lacks it, that is a TEAM.
• Results: The approach achieved an excellent F1 (0.81) for the underlying trace recovery. For inconsistency detection, it attained ~93% accuracy in identifying MEAT and ~75% for TEAM, significantly better than naive baselines. These results suggest that using trace links is a promising way to find documentation-model mismatches.