Code Quality with SonarQube

Source code quality analysis is an essential part of the Continuous Integration process. Together with automated tests it is the key element to deliver reliable software without many bugs, security vulnerabilities or performance leaks. Probably the best static code analyzer you can find on the market is SonarQube. It has a support for more than 20 programming languages. It can be easily integrated with the most popular Continuous Integration engines like Jenkins or TeamCity. Finally, it has many features and plugins which can be easily managed from extensive web dashboard.

However, before we proceed to discuss about the most powerful capabilities of this solution it is well worth to ask Why we do it? Would it be productive for us to force developers to focus on code quality? Probably most of us are programmers and we exactly know that everyone else expect from us to deliver code which meet business demands rather than looks nice 🙂 After all do we really want to break the build by not fulfilling not important rule like maximum line length – rather a little pleasure. On the other hand taking over source code from someone else who was not paying attention to any of good programming practice is also not welcome if you know what I mean. But be calm, SonarQube is the right solution for you. In this article I’ll to show you that carrying about high code quality can be a good fun and above all you can learn more how to develop better code, while other team members spend time on fixing their bugs 🙂

Enough talk go to action. I suggest you to run your test instance of SonarQube using Docker. Here’s SonarQube run command. Then you can login to web dashboard available under with admin/admin credentials.

docker run -d --name sonarqube -p 9000:9000 -p 9092:9092 sonarqube

You are signed in to the web dashboard but there are no projects created yet. To perform source code scanning you should just run one command mvn sonar:sonar if you are using maven in the building process. Don’t forget to add SonarQube server address in settings.xml file as you on the fragment below.


When SonarQube analyse finishes you will see new project with the same name as maven artifact name with your code metrics and statistics. I created sample Spring Boot application where I tried to perform some most popular mistakes which impact on code quality. Source code is available on GitHub. The right module for analyse is named person-service. However, the code with many bugs and vulnerabilities is pushed to v0.1 branch. Master branch has a latest version with the corrections performed basing on SonarQube analyse what I’m going to describe on the next section of that article. Ok, let’s start analyse with mvn command. We can be surprised a little – the code analyse result for 0.1 version is rather not satisfying. Although I spend much time on making important mistakes SonarQube reported only some bugs and code smells were detected and quality gate status is ‘Passed’.


Let’s take a closer look on quality gates in SonarQube. Like I mentioned before we would not like to break the build by not fulfiling one or group of not very important rules. We can achieve it by creating quality gate. This is a set of requirements that tells us whether or not going to deployment with new version od project. There is default quality gate for Java but we can change its thresholds or create the new one. The default quality gate has thresholds set only for new code, so I decided to create the one for my sample application minimum test coverage set on 50 percent, unit test success detection and ratings basic on full code. Now, scanning result looks a little different 🙂


To enable scanning test coverage in SonarQube we should add jacoco plugin to maven pom.xml. During maven build mvn clean test -Dmaven.test.failure.ignore=true sonar:sonar the report would be automatically generated and uploaded to SonarQube.


The last change that has to be done before application rescan is to installing some plugins and enabling rules disabled by default. The list of all active and inactive rules can be displayed in Quality Profiles section. In the default profile for Java the are more than 400 rules available and 271 active on start. I suggest you install FindBugs and Checkstyle plugins. Those plugins has many additional rules for Java which can be activated for our profile. Now there are about 1.1k inactive rules in many categories. Which of them should be activated depends on you, you can activate them in the default profile, create your new profile or use one of predefined profile, which were automatically created by plugins we installed before.  In my opinion the best way to select right rules is to create simple project and check which rules are suitable for you. Then you can check out the detailed description and disable the rule if needed. After activating some rules provided by Checkstyle plugin I have a report with 5 bugs and 77 code smells. The most important errors are visible in the pictures below.


All issues reported by SonarQube can be easily reviewed using UI dashboard for each project in the Issue tab. We can also install plugin SonarLint which integrates with most popular IDEs like Eclipse or IntelliJ and all those issue will be displayed there. Now, we can proceed to fix errors. All changes which I performed to resolve issues can be display on GitHub repository from branches v0.1 to v0.6. I resolved all problems except some checked exception warnings which I set to Resolved (Won’t fix). Those issues won’t be reported after next scans.


Finally my project looks as you could see in the picture below. All ratings have a score ‘A’, test coverage is greater that 60% and quality gate is ‘Passed’. Final person-service version is commited into master branch.


Like you see there are many rules which can be applied to your project during SonarQube scanning, but sometimes it would be not enough for your organization needs. In that case you may search for some additional plugins or create your own plugin with the rules that meet your specific requirements. In my sample available on GitHub there is module sonar-rules where I defined the rule checking whether all public classes have javadoc comments including @author field. To create SonarQube plugin add the following fragment to your pom.xml and change packaging type to sonar-plugin.

		<pluginDescription>For test purposes</pluginDescription>

Here’s the class with custom rule definition. First we have to get a scanned class node (Kind.CLASS), a then process first comment (Kind.TRIVIA) in the class file. The rule parameters like name or priority are set inside @Role annotation.

@Rule(key = "CustomAuthorCommentCheck",
		name = "Javadoc comment should have @author name",
		description = "Javadoc comment should have @author name",
		priority = Priority.MAJOR,
		tags = {"style"})
public class CustomAuthorCommentCheck extends IssuableSubscriptionVisitor {

	private static final String MSG_NO_COMMENT = "There is no comment under class";
	private static final String MSG_NO_AUTHOR = "There is no author inside comment";

	private Tree actualTree = null;

	public List<Kind> nodesToVisit() {
		return ImmutableList.of(Kind.TRIVIA, Kind.CLASS);

	public void visitTrivia(SyntaxTrivia syntaxTrivia) {
		String comment = syntaxTrivia.comment();
		if (syntaxTrivia.column() != 0)
		if (comment == null) {
			reportIssue(actualTree, MSG_NO_COMMENT);
		if (!comment.contains("@author")) {
			reportIssue(actualTree, MSG_NO_AUTHOR);

	public void visitNode(Tree tree) {
		if ( {
			actualTree = tree;


Before building and deploying plugin into SonarQube server it can be easily tested using junit. Inside the src/test/file directory we should place test data – java files which are scanned during junit test. For failure test we should also create file CustomAuthorCommentCheck_java.json in the /org/sonar/l10n/java/rules/squid/ directory with rule definition.

public void testOk() {
	JavaCheckVerifier.verifyNoIssue("src/test/files/", new CustomAuthorCommentCheck());

public void testFail() {
	JavaCheckVerifier.verify("src/test/files/", new CustomAuthorCommentCheck());

Finally, build maven project and copy generated JAR artifact from target directory to SonarQube docker container into $SONAR_HOME/extensions/plugins directory. Then restart your docker container.

docker cp target/sonar-plugins-1.0-SNAPSHOT sonarqube:/opt/sonarqube/extensions/plugins

After SonarQube restart your plugin’s rules are visible under Rules section.


The last thing to do is to run SonarQube scanning in the Continuous Integration process. SonarQube can be easily integrated with the most popular CI server – Jenkins. Here’s the fragment of Jenkins pipeline where we perform source code scanning and then waiting for quality gate result. If you interested in more details about Jenkins pipelines, Continuous Integration and Delivery read my previous post How to setup Continuous Delivery environment.

stage('SonarQube analysis') {
	withSonarQubeEnv('My SonarQube Server') {
		sh 'mvn clean package sonar:sonar'
stage("Quality Gate") {
	timeout(time: 1, unit: 'HOURS') {
		def qg = waitForQualityGate()
		if (qg.status != 'OK') {
			error "Pipeline aborted due to quality gate failure: ${qg.status}"

How to setup Continuous Delivery environment

I have already read some interesting articles and books about Continuous Delivery, because I had to setup it inside my organization. The last document about this subject I can recommend is DZone Guide to DevOps. If you interested in this area of software development it can be really enlightening reading for you. The main purpose of my article is to show rather practical site of Continuous Delivery – tools which can be used to build such environment. I’m going to show how to build professional Continuous Delivery environment using:

  • Jenkins – most popular open source automation server
  • GitLab – web-based Git repository manager
  • Artifactory – open source Maven repository manager
  • Ansible – simple open source automation engine
  • SonarQube – open source platform for continuous code quality

Here’s picture showing our continuous delivery environment.


The changes pushed to Git repository managed by GitLab server are automatically propagated to Jenkins using webhook. We enable push and merge request triggers. SSL verification will be disabled. In the URL field we have to put jenkins pipeline address with authentication credentials (user and password) and secret token. This API token which is visible in jenkins user profile under Configure tab.

webhookHere’s jenkins pipeline configuration in ‘Build triggers’ section. We have to enable option ‘Build when a change is pushed to GitLab‘. GitLab CI Service URL is the address we have already set in GitLab webhook configuration. There are push and merge request enabled from all branches. It can also be added additional restriction for branch filtering: by name or by regex. To support such kind of trigger in jenkins you need have Gitlab plugin installed.


There are two options of events which trigger jenkins build:

  • push – change in source code is pushed to git repository
  • merge request –  change in source code is pushed to one branch and then committer creates merge request to the build branch from GitLab management console

In case you would like to use first option you have to disable build branch protection to enable direct push to that branch. In case of using merge request branch protection need to be activated.


Merge request from GitLab console is very intuitive. Under section ‘Merge request’ we are selecting source and target branch and confirm action.


Ok, many words about GitLab and Jenkins integration… Now you know how to configure it. You only have to decide if you prefer push or merge request in your continuous delivery configuration. Merge request is used for code review in Gitlab – so it is useful additional step in your continuous pipeline. Let’s move on. We have to install some other plugins in jenkins to integrate it with Artifactory, SonarQube and Ansible. Here’s the full list of jenkins plugins I used for continuous delivery process inside my organization:

Here’s configuration on my jenkins pipeline for sample maven project.

node {

    withEnv(["PATH+MAVEN=${tool 'Maven3'}bin"]) {

        stage('Checkout') {
            def branch = env.gitlabBranch
            env.branch = branch
            git url: '', credentialsId: '5693747c-2f45-4557-ada2-a1da9bbfe0af', branch: branch

        stage('Test') {
            def pom = readMavenPom file: 'pom.xml'
            print "Build: " + pom.version
            env.POM_VERSION = pom.version
            sh 'mvn clean test -Dmaven.test.failure.ignore=true'
            junit '**/target/surefire-reports/TEST-*.xml'
            currentBuild.description = "v${pom.version} (${env.branch})"

        stage('QA') {
            withSonarQubeEnv('sonar') {
                sh 'mvn org.sonarsource.scanner.maven:sonar-maven-plugin:3.2:sonar'

        stage('Build') {
            def server = Artifactory.server "server1"
            def buildInfo = Artifactory.newBuildInfo()
            def rtMaven = Artifactory.newMavenBuild()
            rtMaven.tool = 'Maven3'
            rtMaven.deployer releaseRepo:'libs-release-local', snapshotRepo:'libs-snapshot-local', server: server
            rtMaven.resolver releaseRepo:'remote-repos', snapshotRepo:'remote-repos', server: server
   pom: 'pom.xml', goals: 'clean install -Dmaven.test.skip=true', buildInfo: buildInfo
            publishBuildInfo server: server, buildInfo: buildInfo

        stage('Deploy') {
            dir('ansible') {
                ansiblePlaybook playbook: 'preprod.yml'
            mail from: '', to: '', subject: "Nowa wersja start: '${env.POM_VERSION}'", body: "Wdrożono nowa wersję start '${env.POM_VERSION}' na środowisku preprodukcyjnym."


There are five stages in my pipeline:

  1. Checkout – source code checkout from git branch. Branch name is sent as parameter by GitLab webhook
  2. Test – running JUnit test and creating test report visible in jenkins and changing job description
  3. QA – running source code scanning using SonarQube scanner
  4. Build – build package resolving artifacts from Artifactory and publishing new application release to Artifactory
  5. Deploy – deploying application package and configuration on server using ansible

Following Ansible website it is a simple automation language that can perfectly describe an IT application infrastructure. It’s easy-to-learn, self-documenting, and doesn’t require a grad-level computer science degree to read. Ansible using SSH keys to authenticate on the remote host. So you have to put your SSH key to authorized_keys file in the remote host before running ansible commands on it. The main idea of that that is to create playbook with set of ansible commands. Playbooks are Ansible’s configuration, deployment, and orchestration language. They can describe a policy you want your remote systems to enforce, or a set of steps in a general IT process. Here is catalog structure with ansible configuration for application deploy.








Here’s my ansible playbook code. It defines remote host, user to connect and role name. This file is used inside jenkins pipeline on ansiblePlaybook step.

- hosts: pBPreprod
  remote_user: default

    - preprod

Here’s main.yml file where we define set of ansible commands to on remote server.

- block:
  - name: Copy configuration file
    template: src=config.yml.j2 dest=/opt/start/config.yml

  - name: Copy jar file
    copy: src=../target/start.jar dest=/opt/start/start.jar

  - name: Run jar file
    shell: java -jar /opt/start/start.jar

You can check out build results on jenkins console. There is also fine pipeline visualization with stage execution time. Each build history record has link to Artifactory build information and SonarQube scanner report.