Profiling programmer proficiency using static analysis

Static analysis is ubiquitous for defect finding, yet teams often lack an explainable, code-centric view of how programmers actually use a language across projects and over time. We introduce proficiency profiles–interpretable, multi-view summaries that unify heterogeneous static-analysis signals (size, complexity, rule-based findings, and optional clone indicators) into standard, group, and comparing views. We present a general framework for constructing such profiles and demonstrate it in a reference tool, showing its applicability to student codebases in C and Java. The examples in the figures show deltas and alignment to a task-specific reference rather than claiming generalizable performance of the implementation. We release a machine-readable export and example bundles to facilitate reuse. Within these bounds, the profiles helped communicate programmers’ proficiency in education, staffing, and review workflows, where transparency is crucial.