Frequency Distribution of Error Messages

TL;DR

This paper analyzes the frequency distribution of programming error messages in Python and Java, finding they follow Zipf-Mandelbrot distributions, which can help compare languages or compilers quantitatively.

Contribution

It introduces a method to model error message frequencies with Zipf-Mandelbrot distributions and applies it to large datasets in Python and Java.

Findings

Error message frequencies follow Zipf-Mandelbrot distributions

The approach allows quantitative comparison of programming languages or compilers

Maximum-likelihood fitting effectively models the data

Abstract

Which programming error messages are the most common? We investigate this question, motivated by writing error explanations for novices. We consider large data sets in Python and Java that include both syntax and run-time errors. In both data sets, after grouping essentially identical messages, the error message frequencies empirically resemble Zipf-Mandelbrot distributions. We use a maximum-likelihood approach to fit the distribution parameters. This gives one possible way to contrast languages or compilers quantitatively.