An Exploratory Study on the Impact of Change-proneness as a Metric in Black-box Test Suite Minimization

An Exploratory Study on the Impact of Change-proneness as a Metric in Black-box Test Suite Minimization

This is the replication package associated with the paper "An Exploratory Study on the Impact of Change-proneness as a Metric in Black-box Test Suite Minimization".

Replication Package Contents:

This replication package contains all the necessary data and code required to reproduce the results reported in the paper. We provide the results of the Accuracy, Total Minimization Time (MT) for all the minimization budgets (i.e., 25%, 50%, and 75%).

Data:

We provide in the Data directory the data used in our experiments, which is the source code of test cases (Java test methods) of 16 projects collected from Defects4J.

Code:

We provide in the Code directory the code (Python) and bash files required to run the experiments and reproduce the results.

Results:

We provide in the Results directory the detailed results for our approach (called CTM). _________________________________

Enumerate Commit Hash (Required for analyzing last stable version)

cd Code

python3 enumerate_commit_hash.py

Input: Data/defects4j/framework/projects

Output: Data/defects4j/framework/projects/{Project}/active-bugs-with-commit-numbers.csv

Get all repos of Defects4j

cd Code

python3 get_repos.py

input: none

output: Data/defects4j-repos

Extract Change Metrics

cd Code

python3 get_change_metrics_{inception or last_stable}.py

Input: Data/defects4j-repos

Output: Data/changes_{inception or last_stable}

Merge Changes

cd Code

python3 merge_analyzed_changes.py

Input: Data/changes_{inception or last_stabel}

Output: Data/accumulated_changes_{inception or last_stable}

Construct Test case - Class dependency mapping

cd Code

python3 construct_test_case_class_mapping.py

Input: Data/callgraphs

Output: Data/class-invocations

Calculate Change Proneness and Measure the Association with Test cases

cd Code

python3 calculate_test_case_change_proneness.py

Input: Data/accumulated_changes_{inception or last_stable}, Data/unique_test_cases.csv, Data/class_invocations

Output: Data/test_case_chane_proneness_{inception or last_stable}

Minimize

cd Code

python3 greedy_minimize.py

Input: Data/test_case_chane_proneness_{inception or last_stable}

Output: Result/results/greedy_{inception or last_stable}

Evaluate

cd Code

python3 evaluate.py

Input: Result/results/greedy_{inception or last_stable}

Output: Result/accuracy/greedy_{inception or last_stable}