Research Information System
Software Quality Journal, vol.34, no.2, 2026 (SCI-Expanded, Scopus)
Open Source Software (OSS) has become a foundational component of modern software ecosystems; however, systematic and operationally consistent approaches for evaluating OSS quality prior to adoption remain fragmented. Although numerous OSS Quality Models (OSS-QMs) have been proposed, empirical evidence regarding their industrial uptake and the reasons behind limited adoption is scarce. This study addresses this gap by investigating four research questions concerning (RQ1) practical adoption barriers, (RQ2) evaluation needs, (RQ3) the actual level of OSS-QM use in practice, and (RQ4) the perceived potential of a tool-supported OSS quality meta-model. A structured perception-based survey was conducted with 87 practitioners experienced in OSS adoption and software quality evaluation. The instrument was theoretically grounded in prior systematic literature reviewson OSS quality models and meta-models, and responses were analyzed using descriptive statistics, chi-square tests of independence, and non-parametric comparisons. The findings show that although 96% of respondents emphasize the necessity of standardized measurement results and 94% highlight evaluator-level comparability, existing OSS-QMs are rarely adopted in practice, with 80% of respondents reporting that they do not use any OSS-QM. High perceived evaluation cost, structural and terminological fragmentation, and strong dependence on specialized expertise were identified as primary barriers. Inferential analysis further confirmed a strong alignment between perceptions of standardization and comparability. These findings suggest that the central challenge in OSS quality evaluation lies not in the absence of models but in the lack of interoperable and operationally consistent evaluation mechanisms. The principal contribution of this study is to provide practitioner-grounded empirical evidence explaining low industrial uptake and to identify tool-supported meta-modeling as a promising direction for improving comparability and practical adoption.