Detecting Underspecification in Software Requirements via k-NN Coverage Geometry
This addresses underspecification detection in software requirements, which is an incremental improvement over existing methods.
The authors tackled the problem of detecting missing requirement types in software specifications by proposing GeoGap, a geometric method that achieves 0.935 AUROC on the PROMISE NFR benchmark for projects with at least 50 requirements, matching a human-annotated ground-truth oracle.
We propose \geogap{}, a geometric method for detecting missing requirement types in software specifications. The method represents each requirement as a unit vector via a pretrained sentence encoder, then measures coverage deficits through $k$-nearest-neighbour distances z-scored against per-project baselines. Three complementary scoring components -- per-point geometric coverage, type-restricted distributional coverage, and annotation-free population counting -- fuse into a unified gap score controlled by two hyperparameters. On the PROMISE NFR benchmark, \geogap{} achieves 0.935 AUROC for detecting completely absent requirement types in projects with $N \geq 50$ requirements, matching a ground-truth count oracle that requires human annotation. Six baselines confirm that each pipeline component -- per-project normalisation, neural embeddings, and geometric scoring -- contributes measurable value.