Online Appendix of "An Extensive Study of Smell-Aware Bug Localization"

Prerequisites

Python 3 + pandas, scipy, numpy, matplotlib

JupyterLab or Jupyter Notebook

Files Overview

`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.

Step 1. Running Bench4BL

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/`.

Step 2. Preprocessing ranking files

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)

Step 3. Applying smell-aware bug localization

Run `sabl.ipynb` so that the files in `result/` will be generated.

Step 4. Analysis

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.