CoREBench: studying complexity of regression errors

Abstract

Intuitively we know, some software errors are more complex than others. If the error can be fixed by changing one faulty statement, it is a simple error. The more substantial the fix must be, the more complex we consider the error. In this work, we formally define and quantify the complexity of an error w.r.t. the complexity of the error's least complex, correct fix. As a concrete measure of complexity for such fixes, we introduce Cyclomatic Change Complexity which is inspired by existing program complexity metrics. Moreover, we introduce CoREBench, a collection of 70 regression errors systematically extracted from several open-source C-projects and compare their complexity with that of the seeded errors in the two most popular error benchmarks, SIR and the Siemens Suite. We find that seeded errors are significantly less complex, i.e., require significantly less substantial fixes, compared to actual regression errors. For example, among the seeded errors more than 42% are simple compared to 8% among the actual ones. This is a concern for the external validity of studies based on seeded errors and we propose CoREBench for the controlled study of regression testing, debugging, and repair techniques.