Differential Performance Debugging with Discriminant Regression Trees

TL;DR

This paper introduces a novel data-driven method using discriminant regression trees to identify and explain performance differences across input classes in programs, improving debugging efficiency.

Contribution

It presents a new algorithm for discriminant regression tree learning that clusters data into functional groups before applying decision trees, enhancing performance debugging.

Findings

Outperforms existing regression tree algorithms in speed and accuracy

Effectively identifies performance classes in Java program benchmarks

Uses novel clustering techniques for functional performance analysis

Abstract

Differential performance debugging is a technique to find performance problems. It applies in situations where the performance of a program is (unexpectedly) different for different classes of inputs. The task is to explain the differences in asymptotic performance among various input classes in terms of program internals. We propose a data-driven technique based on discriminant regression tree (DRT) learning problem where the goal is to discriminate among different classes of inputs. We propose a new algorithm for DRT learning that first clusters the data into functional clusters, capturing different asymptotic performance classes, and then invokes off-the-shelf decision tree learning algorithms to explain these clusters. We focus on linear functional clusters and adapt classical clustering algorithms (K-means and spectral) to produce them. For the K-means algorithm, we generalize the notion of the cluster centroid from a point to a linear function. We adapt spectral clustering by defining a novel kernel function to capture the notion of linear similarity between two data points. We evaluate our approach on benchmarks consisting of Java programs where we are interested in debugging performance. We show that our algorithm significantly outperforms other well-known regression tree learning algorithms in terms of running time and accuracy of classification.