Code similarity tools are widely used in programming courses to support academic integrity, yet fixed percentage cut-offs are often applied without considering how score distributions vary across assignments, academic years, and detection tools. This study examines similarity patterns over three academic years (2023/2024–2025/2026) using two programming assignments and two detection tools. For each assignment-year-tool combination, we analysed empirical pairwise score distributions using central, dispersion, threshold-based, and upper-tail indicators. Because submissions occur in multiple pairs, we also performed a submission-level robustness analysis using per-submission mean, median, and maximum similarity. Assignment A1 showed descriptively higher central similarity in later academic years, and this pattern remained visible at the submission level, whereas Assignment A2 showed smaller year-to-year differences, particularly for JPlagJava. Maximum scores decreased across years for both tools, but broader upper-tail indicators decreased for JPlagJava and showed mixed patterns for SIMGruneJava. These differences are interpreted descriptively rather than as statistically established temporal changes. The findings support a context-aware, distribution-based interpretation of similarity scores rather than reliance on a single fixed threshold, accounting for assignment characteristics, year-to-year patterns, and tool-specific behaviour.