DOI: 10.5281/zenodo.4681243
title: Online Appendix of "An Extensive Study of Smell-Aware Bug Localization"

description: <strong>Prerequisites</strong> Python 3 + pandas, scipy, numpy, matplotlib JupyterLab or Jupyter Notebook <strong>Files Overview</strong> `smells/${PROJECT}-${VERSION}.xml`: Results of code smell detection by inFusion `exp/${GROUP}/${PROJECT}/${BLT}_${PROJECT}_${PROJECT}_${VERSION}/recommended/${NUMBER}.txt`: Bug localization result rankings when using `${BLT}` in Bench4BL `exp/${GROUP}/${PROJECT}/${BLT}_${PROJECT}_${PROJECT}_${VERSION}_output.txt`: Gold files in the rankings. `result/${CONFIGURATION}/${BLT}/_${METRIC}.csv`: Summary of smell-aware bug localization results using `$CONFIGURATION` and `$BLT`, evaluated by `$METRIC`, which can be generated in Step 3 bid: Target bug ID vid: Target version ID base: Metric value of the baseline approach value: Metric value when using the following alpha alpha: Best (but smallest) alpha value alphas: Best alpha values that maximizing the `$METRIC` (colon-separated) total: Metric value before divided by the number of bugs `cache/projects.csv.bz2`: A list of projects to be used group: Project group name in Bench4BL project: Project name nbugs: Number of bug reports nversions: Number of versions of the project nsources: Average number of source files (among versions) `cache/versions.csv.bz2`: A list of project versions (systems) vid: Version ID (`${PROJECT}-${VERSION}`) group: Project group name in Bench4BL project: Project name version: Version number nsources: Number of source files nbugs: Number of bug reports nsmells: Number of smell instances `cache/bugs.csv.bz2`: A list of bug reports to be used bid: Bug ID (`${PROJECT}-${NUMBER}`) group: Project group name project: Project name vid: Version ID to which the bug belongs version: Version number number: Bug number `cache/smells.csv.bz2`: A list of smell instances, generated from the XML files in `smell/` vid: Version ID granularity: Granularity of the smell (`class` or `method`) package: Package of the target module class: Class of the target module method: Target method if class-level smell, otherwise empty file: Source filename type: Type of the smell severity: Severity of the smell `cache/confs.csv.bz2`: A list of considered configurations, which can be generated in Step 3 name: Configuration name (basically `${GRANULARITY}_${AGGREGATOR}_${SELECTOR}`) granularity: Granularity parameter (g) aggregator: Aggregator parameter (a) selector: Type selector parameter (s) level: `basic` (1-150), `individual` (individual smells), `opt` (ideal (0)) `Bench4BL-patch/`: Patches for Bench4BL. See Step 1. Since the files generated from Steps 1-3 that are necessary to run the scripts in Step 4 are contained in the archive, you can start from Step 4 directly. <strong>Step 1. Running Bench4BL</strong> Follow the instruction of repository cloning and archive downloading in https://github.com/exatoa/Bench4BL. We used the commit of Aug 8, 2019. Add `Bench4BL-patch/VSM.jar` and `Bench4BL-patch/rVSM.jar` into `techniques/releases/` in Bench4BL. Overwrite `scripts/launcher_Tool.py` with `Bench4BL-patch/launcher_Tool.jar` so that the added two techniques become available. To run Bench4BL, the `Bench4BL-patch/Dockerfile` may be useful: % docker run -it -v /path/to/Bench4BL:/mnt/exp/Bench4BL bench4bl /bin/bash Put the generated results to `exp/`. <strong>Step 2. Preprocessing ranking files</strong> Run `preprocess.ipynb` so that the files will be generated in `preprocessed/`. `preprocessed/${BLT}/${PROJECT}_${VERSION}/${NUMBER}.csv`: Bug localization result rankings attached with additional oracle and smell information, which can be generated in Step 2 rank: Rank in the ranking gold: Whether the file is gold or not file_name: Source filename nscore: Similarity score obtained from the bug localization technique class_smells: List of class-level smells with their severity (comma-separated) method_smells: List of method-level smells with their severity (comma-separated) <strong>Step 3. Applying smell-aware bug localization</strong> Run `sabl.ipynb` so that the files in `result/` will be generated. <strong>Step 4. Analysis</strong> Run `analysis-rq1.ipynb` for RQ1, RQ3, and RQ4. Run `analysis-rq2.ipynb` for RQ2. Files generated by Step 2 are needed, which are not included in the archive. Run `analysis-rq3.ipynb` for RQ3.