One of the folk truisms of the compiler/source code analysis business is that error paths are short, i.e., when an error situation is detected (such as failing to open a file), few statements are executed before the functions returns.
Having repeated this truism for many decades, figure 2 from the paper APEx: Automated Inference of Error Specifications for C APIs jumped off the page at me; thanks to Yuan Kang, I now have a copy of the data.
The plots below (code+data) show two representations of the non-error/error path lengths (measured in statements within individual functions of libc; counting starts at a library call that could return an error value). The upper plot shows statement sequence lengths for error/non-error paths, and the lower is a kernel density plot of the error/non-error sequence lengths.
Another truism is that people tend to write positive tests, i.e., tests that do not involve error handling (some evidence).
Code coverage measurements (e.g., number of statements or branches that are executed by a test suite) often show the pattern seen in the plot below (code+data; thanks to the authors of the paper Code Coverage for Suite Evaluation by Developers for making the data available). The data was obtained by measuring the coverage of 1,043 Java programs executing their associated test suite (circles denote program size). Lines are fitted regression models for different sized programs.
If people are preferentially writing positive tests, test suites with low coverage would be expected to execute a greater percentage of statements than branches (an if-statement has two branches, taken/not-taken), i.e., the behavior seen in the plot above (grey line shows equal statement/branch coverage). Once the low hanging fruit is tested (i.e., the longer, non-error, cases), tests have to be written for the shorter, more likely to be error handling, cases.
The plot would also be explained by typical execution paths favoring longer basic blocks, but I don’t have any data that could show this one way or another.