Evaluating domain-specific metric thresholds: an empirical study

TLDR: This paper investigates whether and how thresholds vary across domains by presenting a large-scale study on 3,107 software systems from 15 domains, and analyzed the derivation and distribution of thresholds based on 8 well-known source code metrics.

Abstract: Software metrics and thresholds provide means to quantify several quality attributes of software systems Indeed, they have been used in a wide variety of methods and tools for detecting different sorts of technical debts, such as code smells Unfortunately, these methods and tools do not take into account characteristics of software domains, as the intrinsic complexity of geo-localization and scientific software systems or the simple protocols employed by messaging applications Instead, they rely on generic thresholds that are derived from heterogeneous systems Although derivation of reliable thresholds has long been a concern, we still lack empirical evidence about threshold variation across distinct software domains To tackle this limitation, this paper investigates whether and how thresholds vary across domains by presenting a large-scale study on 3,107 software systems from 15 domains We analyzed the derivation and distribution of thresholds based on 8 well-known source code metrics As a result, we observed that software domain and size are relevant factors to be considered when building benchmarks for threshold derivation Moreover, we also observed that domain-specific metric thresholds are more appropriated than generic ones for code smell detection