Identification of Performance Changes at Code Level (Measurement Configuration Dataset)
<p><strong>Measurement Configuration Dataset </strong></p> <p><em>This is the anonymous reviewing version; the source code repository will be added after the review.</em></p> <p>This dataset provides reproduction data for performance measurement configuration at source code level in Java. The measurement data can be obtained using the precision-experiments repository https://anonymous.4open.science/r/precision-experiments-C613/ (Examining Different Repetition Counts) yourself. These data conatained here are the data we obtained from execution on i7-4770 CPU @ 3.40GHz.</p> <p>The analysis was tested on Ubuntu 20.04 and gnuplot 5.2.8. It will not work with older gnuplot versions.</p> <p>To execute the analysis, extract the data by</p> <pre><code class="language-bash">tar -xvf basic-parameter-comparison.tar tar -xvf parallel-sequential-comparison.tar</code></pre> <p>and afterwards build the precision-experiments repo and execute the analysis by</p> <pre><code class="language-bash">cd precision-experiments/precision-analysis/ ../gradlew fatJar cd scripts/configuration-analysis/ ./executeCompleteAnalysis.sh ../../../../basic-parameter-comparison ../../../../parallel-sequential-comparison</code></pre> <p>Afterwards, the following files will be present:</p> <ul> <li><strong>precision-experiments/precision-analysis/scripts/configuration-analysis/repetitionHeatmaps/heatmap_all_en.pdf</strong> (Heatmaps for different repetition counts)</li> <li><strong>precision-experiments/precision-analysis/scripts/configuration-analysis/repetitionHeatmaps/heatmap_outlierRemoval_en.pdf </strong>(Heatmap with and without outlier removal for 1000 repetitions)</li> <li><strong>precision-experiments/precision-analysis/scripts/configuration-analysis/histogram_outliers_en.pdf</strong> (Histogram of the outliers)</li> <li><strong>precision-experiments/precision-analysis/scripts/configuration-analysis/heatmap_parallel_en.pdf </strong>(Heatmap with sequential and parallel execution)</li> </ul>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0