Differential Performance Debugging With Discriminant Regression Trees

Authors

  • Saeid Tizpaz-Niari University of Colorado Boulder
  • Pavol Cerny University of Colorado Boulder
  • Bor-Yuh Evan Chang University of Colorado Boulder
  • Ashutosh Trivedi University of Colorado Boulder

Keywords:

Regression Trees, Functional Clustering, Spectrum-based Performance Bug Localization

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.

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Published

2018-04-26

How to Cite

Tizpaz-Niari, S., Cerny, P., Chang, B.-Y. E., & Trivedi, A. (2018). Differential Performance Debugging With Discriminant Regression Trees. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11875

Issue

Section

Main Track: Machine Learning Applications