fabric8-analytics/fabric8-analytics-common

[[fabric8-analytics-testing]] = Fabric8-Analytics Testing

:url-job: https://github.com/fabric8-analytics/fabric8-analytics-common/actions/workflows/ci-run-e2e.yml image:https://github.com/fabric8-analytics/fabric8-analytics-common/actions/workflows/ci-run-e2e.yml/badge.svg[CI Run e2e , link={url-job}]

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[[overview]] == Overview

Fabric8-Analytics is an open source analytics platform aimed primarily at assisting developers in getting insights and recommendations on the stacks used for developing applications. This platform can be used to get an overview and recommendations for single application components as well.

The following sections describe:

Tests available for testing your code in Fabric8-Analytics
Setting up CI/CD in CentOS for a new project
QA dashboard and code coverage reporter
Recommended coding standards

NOTE: The Fabric8-Analytics project has evolved from two different projects called Cucos and Bayesian. We are currently in the process of renaming the modules and updating documentation. Till then Cucos and Bayesian should be considered as synonyms for Fabric8-Analytics.

//// [[using-fabric8-analytics]] = Using Fabric8-Analytics You can use the following options to interact with Fabric8-Analytics:

API: To get up and running with the API please see the link:https://github.com/fabric8-analytics/fabric8-analytics-server/blob/master/README.md[API server README].
A widget in SonarQube: This is the developer facing integration point. For an in-depth example working with SonarQube see the link:https://github.com/fabric8-analytics/examples[examples repository].
NOTE: A link:https://github.com/fabric8-analytics/fabric8-analytics-sonarqube-plugin[special plugin] needs to be used for scanning the Maven projects.
link:Fabric8-Analytics https://github.com/fabric8-analytics/fabric8-analytics-jenkins-plugin[Fabric8-Analytics Jenkins plugin]: You can trigger Fabric8-Analytics from Jenkins. See link:https://github.com/fabric8-analytics/fabric8-analytics-sonarqube-plugin/blob/master/docs/running_from_jenkins.md[Triggering Fabric8-Analytics scan from Jenkins] for details.
Fabric8-Analytics is also built into the link:https://openshift.io[openshift.io] build pipeline.

[[developing-and-running-the-system]] = Developing and Running the System

We have detailed https://github.com/fabric8-analytics/fabric8-analytics-deployment/blob/master/README.md[documentation] that describes possibilities of running whole Fabric8-Analytics, doing code changes, running tests etc. ////

== Testing Fabric8-Analytics Code The following tests can be run to test your code in Fabric8-Analytics. Detailed information about each type of test is included in the corresponding folder in the repository.

Integration tests: The link:integration-tests/README.adoc[integration tests] for fabric8-analytics services can be run against existing deployment, or locally using docker-compose.
Performance tests: A basic set of link:perf-tests/performance_tests.adoc[performance tests] measures the component search and stack analysis at this moment.
link:a2t/README.md[Analytics API load tests] to measure component and stack analysis throughput.
UI tests: The link:ui-tests/ui_tests.adoc[UI tests] access OpenShift.io and check whether it is possible to create a new space, configure the project in that space, and get the stack and license analysis for this project.
VS Code visual tests: Visual (UI) tests for the Analytics extension for the Visual Studio Code. For further explanation please view the link:vscode-visual-tests/README.md[VS Code README document]
Database integrity tests: Database integrity tests are described in the <<database_integrity_tests,following document>>.

== Standard operating procedures In case of any issue with tests or with CI, please refer to the link:SOP.md[Standard operating procedures] document.

== QA Dashboard generator The <<about_qa_dashboard_code_coverage,QA dashboard>> provides a consolidated report on the status of code coverage, performance tests and CI jobs for all the repositories in Fabric8-Analytics that contain source files.

//// Commenting out below sections as they need to move to relevant sections

== Analytics CI/CD <>

== Coding standards Use the following scripts to check if the code follows PEP 8 and PEP 257 coding standards. These scripts can be run without any arguments: * `./run-linter.sh` : Use this script to check the indentation, line lengths, variable names, and white space around the operators. * `./check-docstyle.sh`: Use this script to check all documentation strings, their presence, and format. Ensure that you fix any warnings and errors reported by these scripts. List of directories containing source code, that needs to be checked, are stored in a file `directories.txt` == Code complexity measurement Use the following scripts to measure code complexity. These scripts can be run w/o any arguments: * `./measure-cyclomatic-complexity.sh`: Use this script to measure the cyclomatic complexity of all the Python sources found in the repository. See this table for further explanation on interpreting the results. * `./measure-maintainability-index.sh`: Use this script to measure the maintainability index of all the Python sources found in the repository. See the explanation of this measurement for more details. == Check for all possible issues The script named `check-all.sh` is to be used to check the sources for all detectable errors and issues. This script can be run w/o any arguments: --- ./check-all.sh --- Expected script output: --- Running all tests and checkers Check all BASH scripts OK Check documentation strings in all Python source file OK Detect common errors in all Python source file OK Detect dead code in all Python source file OK Run Python linter for Python source file OK Unit tests for this project OK Done Overal result OK --- An example of script output when one error is detected: --- Running all tests and checkers Check all BASH scripts Error: please look into files check-bashscripts.log and check-bashscripts.err for possible causes Check documentation strings in all Python source file OK Detect common errors in all Python source file OK Detect dead code in all Python source file OK Run Python linter for Python source file OK Unit tests for this project OK Done Overal result One error detected! --- == Dead code detection The script `detect-dead-code.sh` can be used to detect dead code in the repository. This script can be run w/o any arguments: ---- ./detect-dead-code.sh ---- Please note that due to Python's dynamic nature, static code analyzers are likely to miss some dead code. Also, code that is only called implicitly may be reported as unused. Because of this potential problems, only code detected with more than 90% of confidence is reported. List of directories containing source code, that needs to be checked, are stored in a file `directories.txt` == Common issues detection The script `detect-common-errors.sh` can be used to detect common errors in the repository. This script can be run w/o any arguments: ---- ./detect-common-errors.sh ---- Please note that only semantical problems are reported. List of directories containing source code, that needs to be checked, are stored in a file `directories.txt` ////

fabric8-analytics / fabric8-analytics-common

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