On the Diffuseness and the Impact of Multi‐Language Design Smells
Md. Shahrukh Ansari, Salman Abdul Moiz · Journal of Software Evolution and Process · 2026
ABSTRACT Multi‐language software systems integrate components written in different programming languages, offering flexibility but also introduces design challenges at language boundaries. These challenges often manifest as multi‐language design smells‐suboptimal design practices that can affect maintainability and reliability. Previous research proposed a detector for these smells and analyzed their quality impact; however, its accuracy and generalizability have not been independently validated. This study evaluates and refines the existing multi‐language design smell detector, reassesses smell prevalence using the improved tool, and reanalyzes their relationship with software quality attributes such as fault‐ and change‐proneness. The detector was first evaluated on 60 open‐source projects to test its generalizability, after which rule‐level inconsistencies were identified and corrected. The refined detector was then applied to multiple releases of nine Java Native Interface (JNI) systems for updated prevalence and impact analysis. The refined detector achieves perfect alignment with formal definitions and manual annotations. The reanalysis reveals revised distributions of smells and a much weaker statistical relationship with software faults than previously reported, whereas new evidence shows only limited associations with change‐proneness.