Diff-based mutation testing is a mutation testing approach that only mutates lines affected by a code change under review. This approach scales independently of the code-base size and introduces test goals (mutants) that are directly relevant to an engineer’s goal such as fixing a bug, adding a new feature, or refactoring existing functionality. Google’s mutation testing service integrates diff-based mutation testing into the code review process and continuously gathers developer feedback on mutants surfaced during code review. To enhance the developer experience, the mutation testing service uses a number of manually-written rules that suppress not-useful mutants—mutants that have consistently received negative developer feedback. However, while effective, manually implementing suppression rules requires significant engineering time.

Lexical semantics theories differ in advocating that the meaning of words is represented as an inference graph, a feature mapping or a vector space, thus raising the question: is it the case that one of these approaches is superior to the others in representing lexical semantics appropriately?Or in its non antagonistic counterpart: could there be a unified account of lexical semantics where these approaches seamlessly emerge as (partial) renderings of (different) aspects of a core semantic knowledge base?In this paper, we contribute to these research questions with a number of experiments that systematically probe different lexical semantics theories for their levels of cognitive plausibility and of technological usefulness.The empirical findings obtained from these experiments advance our insight on lexical semantics as the feature-based approach emerges as superior to the other ones, and arguably also move us closer to finding answers to the research questions above.