This module provides a way to capture runtime information about the data accesses previously detected by a static analysis and to add the calls to the reverse engineering static analysis' JSON report.
%%% Cite the paper
@inproceedings{deRycke2025,
title = {Visualizing Data Access Traces in Microservices Using Animated Heat Treemaps},
author = {De Rycke, Maxime and Andr{\'e}, Maxime and Raglianti, Marco and Cleve, Anthony and Lanza, Michele},
booktitle = {Proceedings of the 13th Working Conference on Software Visualization (VISSOFT 2025): NIER Track},
year = {2025},
organization = {IEEE Computer Society Press},
doi = {https://doi.org/10.1109/vissoft67405.2025.00017}
}Captures the invocation call traces of data access code fragments provided by a static analysis report. These captured information include:
- The location of the data access code fragment;
- The timestamp of the data access invocation;
- The argument values provided to the data access.
| Language | Technology | Implementation status |
|---|---|---|
| JavaScript | Any | 🌕 |
INPUT
Given the following elements:
- <path/to/analysis/AnalysisNodeProf.js>: the location of the NodeProf analysis file (
./analysis/AnalysisNodeProf.js) - <path/to/node_project>: the location of the Node project root to instrument.
- <path/to/static_analysis_report.json>: the location of the JSON report resulting from the static analysis.
Run this command to start the dynamic analysis:
mx jalangi --analysis <path/to/analysis/AnalysisNodeProf.js> <path/to/node_project> --scope=all <path/to/static_analysis_report.json>
NOTE: You can prevent tracing code fragments below a given score by changing the MIN_FRAGMENT_SCORE value in helper/Constant.helper.js.
OUTPUT
The runtime information about the data access code fragments are saved in a log file located in ./TEMP/<service_name>.log (where <service_name> is the name of the service).
Each line represents a data access code fragment invocation, formatted as a JSON object as follows:
{"location":"https://<git host>/<user>/<project>/tree/<hash>/index.js#Ly1Cx1-Ly2Cx2","timestamp":<timestamp>,"argumentValues":["<value1>", "<value2>", "<valueN>"]}
{"location":"https://<git host>/<user>/<project>/tree/<hash>/index.js#Ly3Cx3-Ly4Cx4","timestamp":<timestamp>,"argumentValues":["<value1>", "<valueN>"]]}
{"location":"https://<git host>/<user>/<project>/tree/<hash>/index.js#Ly5Cx5-Ly6Cx6","timestamp":<timestamp>,"argumentValues":[]}
// ...Merges the traces captured at runtime with the static analysis report, resulting in a final report containing the data access code fragments (static) and their calls invocations (dynamic).
| Language | Technology | Implementation status |
|---|---|---|
| JavaScript | Any | 🌕 |
INPUT
Given the following elements:
- <path/to/static_analysis_report.json>: the location of the JSON report resulting from the static analysis.
- <path/to/traces_service1.log>: the location of the first traces/logs file
- <path/to/traces_service2.log>: the location of the second traces/logs file
- <path/to/traces_serviceX.log>: the location of the traces/logs file X
- ...
Run the command to merge static and dynamic information.
npm run mapping <path/to/static_analysis_report.json> <path/to/traces_service1.log> [<path/to/traces_service2.log> <path/to/traces_serviceX.log> ...]
OUTPUT
The result is a new JSON report, which is the JSON static analysis report plus the list of invocations calls for every data access code fragment.
You can find the result within the /output folder.
[
// A repository"location": "https://github.com/<user>/<repository>",
"directories": [
{
// A directory"location": "https://<git host>/<user>/<repository>",
"directories": [
// ...
],
"files": [
{
// A file"location": "https://github.com/<user>/<repository>/.../<file path>.js",
"linesOfCode": "<LoC>",
"codeFragments": [
{
// A code fragment"location": "https://<git host>/<user>/<project>/tree/<hash>/index.js#Ly1Cx1-Ly2Cx2",
"technology": {
"id": "<technology>"// E.g., javascript-api-express, javascript-db-mongo, javascript-db-redis.
},
"operation": {
"name": "<operation>"// E.g., CREATE, READ, UPDATE, DELETE, OTHER
},
"method": {
"name": "<method>"// E.g., post, get, findOne, sadd, etc.
},
"sample": {
"content": "<sample>"// E.g., a Redis key, a MongoDB object, etc.
},
"concepts": [
{
"name": "<concept>"// E.g., a route resource concept name, a Redis key name, a MongoDB, collection name, etc.
}
],
"heuristics": "<heuristics>", // The matching heuristics tracing."score": "<score>", // The computer likelihood score."calls": [
{
"timestamp": <timestamp>,"argumentValues": [
"<value1>",
"<value2>",
"<valueN>"
]
},
{
"timestamp": <timestamp>,"argumentValues": []
}
// ...
]
}
// ...
]
}
// ...
]
}
// ...
]
// ...
]See INSTALL file.
Tests are written using the Jest framework.
The tests are specified in the /test/unit directory and are named following the *.test.js pattern.
The configuration of Jest is stated in the /package.json file.
The tests running computes the code coverage.
Launch the unit tests.
npm run test_unit
- JavaScript
- NodeProf is used for the dynamic analysis.
- Winston for data accesses logging.
- Jest is used for unit testing.
The resulting report follows that model:
If you want to contribute to the project, please consider the following instructions:
- Any helping method or class must be named clearly (no abbreviations), especially integrating the type of detection, technology, and type of code fragment.
- More generally, any contribution must follow the conventions and keep the shape of previous contributions.
- Any contribution must be tested (unit and integration tests).
- Any contribution must be documented, especially by updating the
README.mdand theINSTALL.mdfile. - Any contribution must be approved via the pull request mechanism.
- More generally, any contribution must follow the conventions and keep the shape of previous contributions.