This repository contains the code of our MSR 2021 paper "S3M: Siamese Stack (Trace) Similarity Measure".
You can find NetBeans dataset from paper to reproduce experiments on Figshare.
You can use other datasets that match the following format. The data presented as a list of report in the following JSON:
[
{
"bug_id": 1234,
"dup_id": 12,
"creation_ts": 1234567891234,
"stacktrace": {
"exception": ["java.lang.Exception"],
"frames": [
{"function": "java.util.ArrayList.get"},
{"function": "com.company.Class1.method1"},
{"function": "com.company.Class2.method2"},
{"function": "com.company.Class1.method2"}
]
}
},
{
"bug_id": 1235,
"dup_id": null,
"creation_ts": 1234567898765,
"stacktrace": {
"exception": ["com.company.MyException"],
"frames": [
{"function": "com.company.Class1.method2"},
{"function": "com.company.Class1.main"}
]
}
}
]
where
bug_id– identifier of reportdup_id– identifier of first report in group. If this report is the first in this group, this field should benullcreation_ts– timestamp of report creation
stacktrace field contains information about report content such as
exception classes and stack frames.
git clone [email protected]:akhvorov/S3M.git
cd S3M
pip install -r requirements.txt
cd src
python main.py --data_path path_to_reports.json
The script will produce some state files in event_state directory in one level with src.
This precomputed states will speed up data reading in the next runs.