Retriever-Benchmark

Benchmark Retriever

This repository presents a benchmark for the view-based Retriever approach to reverse engineering software architecture models. This approach enables the reverse engineering of software architecture models from heterogeneous artifacts by extracting structural and behavioral views from existing software artifacts using rule-based processes. The views are then refined to ensure completeness and consistency, and model-driven composition connects the different views. These views provide a comprehensive understanding of software systems and support model-driven analysis and quality prediction.

Project Structures

Each benchmark project is structured as follows:

• The .retriever.yml file contains the configuration for running the retriever approach.
  • The repository value is the ID of a GitHub repository.
  • The current_version value is the latest version of the Retriever approach used to build the architectural models.
  • The rules values are the rules used to build the architectural models.
• The model_re folder contains the architectural model of the system that is automatically generated by the Retriever approach.
  • The pcm folder contains the Palladio Component Model (PCM) of the system.
  • The uml folder contains the PlantUML model.
• The model_gs folder contains our manual gold standards for the system.

Using the CLI

The easiest way to use our approach is to use the CLI that we have released. Here’s an example of how to call the CLI application with the given parameters:

./eclipse -i /path/to/input/directory -o /path/to/output/directory -r supported_rules

Here, replace /path/to/input/directory with the path to the root directory of the project you want to reverse engineer, /path/to/output/directory with the path to the output directory where you want to store the generated models, and supported_rules with the rules you want to use for reverse engineering.

GitHub Workflow

This GitHub workflow is designed for reverse-engineering case studies using the Palladio Reverse-Engineering Retriever. It automates the process of collecting information, generating the PCM (Palladio Component Model) and committing these results back to the repository. It ensures that the latest version is always used and that results are consistently integrated back into the repository.

The workflow can be dispatched manually, allowing to overwrite the benchmark parameter for all projects to force the retriever action to use the hyperfine benchmark to measure the execution time. In addition to that, the workflow is automatically triggered every day at 2:00 AM UTC to keep the results up-to-date. Since the workflow commits the analysis results back to the repository it requires the contents: write permission.

How it works

The workflow consists of 3 jobs:

1. Collect Information (collect_info)

This job determines the benchmark projects by searching for .retriever.yml files.

Outputs

• array: A list of directories that contain a .retriever.yml file.
• latest_version: The latest version tag of the Palladio-ReverseEngineering-Retriever on GitHub. Used to determine if the retriever action needs to be run for a project or not.

Steps

1. Checkout Repository
2. Find Directories: Finds directories containing .retriever.yml files and outputs an array of these directories.
3. Get Latest Retriever Version: Fetches the latest version of the Palladio-ReverseEngineering-Retriever from GitHub and outputs it.

2. Generate PCM (generate_pcm)

This job performs the actual benchmark on the projects found in the previous job. It is a matrix job that takes the output array of the collect_info job as an input (list of project directories). The job is executed in parallel on the projects and if one project fails, the others still continue. This ensures that the results of one project don’t affect other projects.

Steps

1. Checkout Benchmark Repository
2. Parse configuration: Parses .retriever.yml using yq and handles the overrideBenchmark input if the workflow is executed manually.
3. Checkout Repositories: Checks out the necessary repositories that get analyzed.
4. Execute Retriever: Runs the retriever action for the current project.
5. Download Results: Downloads the results from the retriever.
6. Prepare Upload and Render UML Diagrams: Prepares the results for upload and renders the UML diagrams.
7. Update Version: Updates the version in .retriever.yml to the latest version that was used for the analysis.
8. Upload Artifact: Uploads the results as an artifact for the next job.

Steps 3-8 are only executed if the current_version in the .retriever.yml file doesn’t match the latest_version that was determined in the previous job. This avoids unnecessary execution as the workflow is scheduled every day.

3. Commit Results (commit_results)

This job commits the results back to the benchmark repository.

Steps

1. Checkout Benchmark Repository
2. Download Artifacts: Downloads the artifacts prepared by the generate_pcm job.
3. Integrate Changes: Commits and pushes the changes back to the repository, using a commit message that includes the latest version of the Retriever tool. This updates the PCM, the README.md and the UML diagrams for all benchmarked projects.

Example usage

In order to add a new project to the benchmark repository, create a folder for the project and add the following .retriever.yml file:

repository: [GitHub repository id]
current_version: v5.2.0.202402260843
rules:
  - org.palladiosimulator.retriever.extraction.rules.maven
  - org.palladiosimulator.retriever.extraction.rules.spring
benchmark: 'false'
rules_path: '.'

The parameter current_version is updated by the benchmark after a successful run to the used version of the retriever to avoid unnecessary repeated analysis runs if the version hasn’t changed.

The parameters rules, benchmark and rules_path are mapped to the parameters of the used retriever action. Only the parameter rules is required.

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