GenTree: Using Decision Trees to Learn Interactions for Configurable Software

TL;DR

GenTree is a dynamic analysis tool that learns logical interactions between configuration options and code coverage in software systems, aiding development and testing.

Contribution

It introduces a novel iterative decision tree-based method to automatically learn configuration interactions in highly configurable software.

Findings

Efficiently finds precise interactions with minimal configuration sampling.

Works across multiple programming languages and systems.

Reduces complexity in understanding configurable software behaviors.

Abstract

Modern software systems are increasingly designed to be highly configurable, which increases flexibility but can make programs harder to develop, test, and analyze, e.g., how configuration options are set to reach certain locations, what characterizes the configuration space of an interesting or buggy program behavior? We introduce GenTree, a new dynamic analysis that automatically learns a program's interactions - logical formulae that describe how configuration option settings map to code coverage. GenTree uses an iterative refinement approach that runs the program under a small sample of configurations to obtain coverage data; uses a custom classifying algorithm on these data to build decision trees representing interaction candidates; and then analyzes the trees to generate new configurations to further refine the trees and interactions in the next iteration. Our experiments on 17 configurable systems spanning 4 languages show that GenTree efficiently finds precise interactions using a tiny fraction of the configuration space.