Software source code authorship analysis has become an important area in recent years with promising applications in both the legal sector (such as proof of ownership and software forensics) and the education sector (such as plagiarism detection and assessing style). Authorship analysis encompasses the sub-areas of author discrimination, author characterization, and similarity detection (also referred to as plagiarism detection). While a large number of metrics have been proposed for this task, many borrowed or adapted from the area of computational linguistics, there is a difficulty with capturing certain types of information in terms of quantitative measurement. Here it is proposed that existing numerical metrics should be supplemented with fuzzy-logic linguistic variables to capture more subjective elements of authorship, such as the degree to which comments match the actual source code’s behavior. These variables avoid the need for complex and subjective rules, replacing these with an expert’s judgement. Fuzzy-logic models may also help to overcome problems with small data sets for calibrating such models. Using authorship discrimination as a test case, the utility of objective and fuzzy measures, singularly and in combination, is assessed as well as the consistency of the measures between counters.