Differential Performance Debugging With Discriminant Regression Trees
Authors: Saeid Tizpaz-Niari, Pavol Cerny, Bor-Yuh Evan Chang, Ashutosh Trivedi
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on benchmarks consisting of Java programs where we are interested in debugging performance. We show that our algorithm outperforms other well-known regression tree learning algorithms in terms of running time and accuracy of classification. |
| Researcher Affiliation | Academia | Saeid Tizpaz-Niari, Pavol ˇCern y, Bor-Yuh Evan Chang, Ashutosh Trivedi University of Colorado Boulder |
| Pseudocode | Yes | Algorithm 1: LEARNDISCRIMINANTREGRESSIONTREE(T , B, Bε) and Algorithm 2: KLINEARCLUSTERINGALGORITHM |
| Open Source Code | No | We implement our approach in the tool DPDEBUGGER and evaluate it on benchmarks consisting of a suite of Java programs. |
| Open Datasets | No | The paper describes datasets that were collected or constructed for the specific experiments (e.g., 'inputs...are PNG and JPEG images of various sizes', 'benchmarks were constructed', 'input data set for our experiments consists of asking for different plots'), but does not provide concrete access information (links, DOIs, or formal citations to publicly available datasets with author/year attribution) for these datasets. |
| Dataset Splits | Yes | The metric we are interested in is accuracy of classification based on 10-fold cross-validation. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions using Java programs and tools like M5Prime, GUIDE, and CART, but does not specify version numbers for any of the software dependencies. |
| Experiment Setup | Yes | Our tool applied spectral clustering with the number of clusters set to 3. (Charts4j) We applied K-linear clustering algorithm where we set the number of clusters to 5. (Snapbuddy) Our tool applied K-linear clustering algorithm with K = 2. (JFree Chart) |