Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2026
|
| Online Access: | https://doi.org/10.5281/zenodo.18872673 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866901429413740544 |
|---|---|
| author | Di Meglio, Sergio Starace, Luigi Libero Lucio Pontillo, Valeria Martins, Luana Di Nucci, Dario Palomba, Fabio |
| author_facet | Di Meglio, Sergio Starace, Luigi Libero Lucio Pontillo, Valeria Martins, Luana Di Nucci, Dario Palomba, Fabio |
| contents | <p>This repository contains the full replication package for the paper:</p> <p><em>“Smelly Bad Practices in Performance System Tests: Detection, Prevalence, and Lifetime”</em></p> <p>The package includes the implementation of the detection tool, all intermediate artifacts, and the datasets produced to answer the three research questions (RQ1, RQ2, and RQ3).</p> <p>The replication package is organized into four main folders:<br>.<br>├── pt-linter/<br>├── RQ1/<br>├── RQ2/<br>└── RQ3/</p> <p><strong>1. pt-linter/</strong><br>This folder contains the implementation of the detection tool developed in the paper.</p> <p>1.1 Tool Source Code<br>The full source code of the tool is provided.<br>Inside this folder, you will also find a dedicated README explaining:<br>- How to build the tool<br>- How to run the tool<br>- How to extend it with new bad practices<br>- How to reproduce the analyses</p> <p>1.2 reports_and_statistics/<br>This subfolder contains all the automatically generated output files produced by running the tool on the E2EGIT dataset of: 234 performance system tests.<br>For each analyzed test, the following artifacts are available: JSON and HTML reports.</p> <p><br><strong>2. RQ1/ — Taxonomy Construction</strong><br>This folder contains all artifacts related to Research Question 1, which concerns the identification and definition of bad practices through a grey literature review.</p> <p>2.1 Grey Literature Review Sources (.xlsx)<br>An Excel file listing all sources analyzed during the grey literature review.<br>For each source, the file includes:<br>- Source metadata<br>- Classification label: Useful (contributed to the preliminary taxonomy) / Not useful<br>This file ensures full traceability of the taxonomy construction process.</p> <p>2.2 Identified Bad Practices (.docx)<br>A Word document describing the 10 identified bad practices.<br>For each bad practice, the document includes: A table listing the supporting source references extracted from the grey literature.<br>This document represents the preliminary taxonomy used to guide tool development and subsequent analyses.</p> <p><br><strong>3. RQ2/ — Detection Accuracy and Prevalence </strong></p> <p>This folder contains artifacts related to Research Question 2, which evaluates the tool and studies the prevalence of bad practices.</p> <p>3.1 tool-validation/<br>This folder contains the material used for manual validation of the detection tool.<br>It includes: 31 .jmx files<br>These are the performance test files that were manually inspected for validation.<br>An additional .xlsx file containing:<br>- Manually labeled violations<br>- True positives<br>- True negatives<br>- Additional validation annotations<br>This dataset was used to compute the tool’s validation metrics (e.g., precision).</p> <p>3.2 history_recap_analysis.xlsx<br>This Excel file contains the results of the longitudinal analysis.<br>For each of the 234 tests, the file reports:<br>The presence of each bad practice<br>Its prevalence across different versions of the test over time</p> <p><br><strong>4. RQ3/ — Lifetime and Commit-Level Analysis</strong></p> <p>This folder contains artifacts related to Research Question 3, which investigates the lifetime and evolution of bad practices across commit events.</p> <p>The folder is structured according to the four commit event types defined in the paper:<br>RQ3/<br>├── improving/<br>├── introduction/<br>├── neutral/<br>└── pejorative/</p> <p>Each of these folders corresponds to a specific type of commit event.</p> <p>Each commit event folder (improving, introduction, neutral, pejorative) contains:<br>Six subfolders, one for each of the six analyzed bad practices.<br>Inside each bad practice folder, the following files are available:</p> <p>4.1 commit_analysis<br>Contains:<br>The list of commits classified under the given event type<br>For example:<br>In introduction/Excessive Listeners/commit_analysis, you will find all commits that introduced an Excessive Listeners violation.</p> <p>4.2 owner_author_analysis</p> <p>Contains analysis results related to:<br>- The owner tag<br>As described in the paper.</p> <p>4.3 project_startup_analysis<br>Contains analysis results related to:<br>- The project startup phase<br>As described in the paper.</p> <p><br>4.4 project_status_release_analysis<br>Contains analysis results concerning:<br>- Release status<br>As described in the paper.</p> <p>4.5 workload_newcomer_analysis<br>Contains data and results related to:<br>- Developer workload<br>- Newcomer status<br>As described in the paper</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18872673 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Smelly Bad Practices in Performance System Tests: Detection, Prevalence, and Lifetime: Online Appendix Di Meglio, Sergio Starace, Luigi Libero Lucio Pontillo, Valeria Martins, Luana Di Nucci, Dario Palomba, Fabio <p>This repository contains the full replication package for the paper:</p> <p><em>“Smelly Bad Practices in Performance System Tests: Detection, Prevalence, and Lifetime”</em></p> <p>The package includes the implementation of the detection tool, all intermediate artifacts, and the datasets produced to answer the three research questions (RQ1, RQ2, and RQ3).</p> <p>The replication package is organized into four main folders:<br>.<br>├── pt-linter/<br>├── RQ1/<br>├── RQ2/<br>└── RQ3/</p> <p><strong>1. pt-linter/</strong><br>This folder contains the implementation of the detection tool developed in the paper.</p> <p>1.1 Tool Source Code<br>The full source code of the tool is provided.<br>Inside this folder, you will also find a dedicated README explaining:<br>- How to build the tool<br>- How to run the tool<br>- How to extend it with new bad practices<br>- How to reproduce the analyses</p> <p>1.2 reports_and_statistics/<br>This subfolder contains all the automatically generated output files produced by running the tool on the E2EGIT dataset of: 234 performance system tests.<br>For each analyzed test, the following artifacts are available: JSON and HTML reports.</p> <p><br><strong>2. RQ1/ — Taxonomy Construction</strong><br>This folder contains all artifacts related to Research Question 1, which concerns the identification and definition of bad practices through a grey literature review.</p> <p>2.1 Grey Literature Review Sources (.xlsx)<br>An Excel file listing all sources analyzed during the grey literature review.<br>For each source, the file includes:<br>- Source metadata<br>- Classification label: Useful (contributed to the preliminary taxonomy) / Not useful<br>This file ensures full traceability of the taxonomy construction process.</p> <p>2.2 Identified Bad Practices (.docx)<br>A Word document describing the 10 identified bad practices.<br>For each bad practice, the document includes: A table listing the supporting source references extracted from the grey literature.<br>This document represents the preliminary taxonomy used to guide tool development and subsequent analyses.</p> <p><br><strong>3. RQ2/ — Detection Accuracy and Prevalence </strong></p> <p>This folder contains artifacts related to Research Question 2, which evaluates the tool and studies the prevalence of bad practices.</p> <p>3.1 tool-validation/<br>This folder contains the material used for manual validation of the detection tool.<br>It includes: 31 .jmx files<br>These are the performance test files that were manually inspected for validation.<br>An additional .xlsx file containing:<br>- Manually labeled violations<br>- True positives<br>- True negatives<br>- Additional validation annotations<br>This dataset was used to compute the tool’s validation metrics (e.g., precision).</p> <p>3.2 history_recap_analysis.xlsx<br>This Excel file contains the results of the longitudinal analysis.<br>For each of the 234 tests, the file reports:<br>The presence of each bad practice<br>Its prevalence across different versions of the test over time</p> <p><br><strong>4. RQ3/ — Lifetime and Commit-Level Analysis</strong></p> <p>This folder contains artifacts related to Research Question 3, which investigates the lifetime and evolution of bad practices across commit events.</p> <p>The folder is structured according to the four commit event types defined in the paper:<br>RQ3/<br>├── improving/<br>├── introduction/<br>├── neutral/<br>└── pejorative/</p> <p>Each of these folders corresponds to a specific type of commit event.</p> <p>Each commit event folder (improving, introduction, neutral, pejorative) contains:<br>Six subfolders, one for each of the six analyzed bad practices.<br>Inside each bad practice folder, the following files are available:</p> <p>4.1 commit_analysis<br>Contains:<br>The list of commits classified under the given event type<br>For example:<br>In introduction/Excessive Listeners/commit_analysis, you will find all commits that introduced an Excessive Listeners violation.</p> <p>4.2 owner_author_analysis</p> <p>Contains analysis results related to:<br>- The owner tag<br>As described in the paper.</p> <p>4.3 project_startup_analysis<br>Contains analysis results related to:<br>- The project startup phase<br>As described in the paper.</p> <p><br>4.4 project_status_release_analysis<br>Contains analysis results concerning:<br>- Release status<br>As described in the paper.</p> <p>4.5 workload_newcomer_analysis<br>Contains data and results related to:<br>- Developer workload<br>- Newcomer status<br>As described in the paper</p> |
| title | Smelly Bad Practices in Performance System Tests: Detection, Prevalence, and Lifetime: Online Appendix |
| url | https://doi.org/10.5281/zenodo.18872673 |