Metrics

A lot of features are already included in the Neo4j graph database system but sometimes we want to extends its capabilities and implement functions and procedures by ourselves that we may reuse. In the following tutorial I will demonstrate how to implement a procedure for Neo4j, how to write and run tests using JUnit and an embedded graph database and last but not least how to setup Neo4j with Docker and our stored procedure installed in no time. ...

I have written about other software system analyzing and validation tools before but today I would like to introduce a new tool named jqAssistant that supports software architects, developers and analysts in a variety of tasks like analyzing given structures, validating architectural or quality constraints and generating reports. Therefore jqAssistant analyzes given projects or artifacts and stores the gathered information – that is enriched by a variety of existing plugin-ins – in a Neo4j graph database. ...

Mutation testing makes an interesting addition to the classical test coverage metrics. They seed mutations (errors) into the code, run the project’s tests afterwards and if the tests fail, the mutation is killed – otherwise it lived and we have a possible indication of an issue with our tests. In the following short tutorial. I’d like to demonstrate how to setup mutation tests with the PIT/Pitest library and Maven and generate reports. ...

There are several ways to aggregate and report application performance indicators in a Java application. One common way here is to use Java Management Extensions (JMX) and MBeans. The Yammer Metrics Library eases this task for us and simplifies the aggregation of different reports. In the following tutorial, we’re going to set up a full Java EE 7 web application by the help of Maven archetypes and we’re running the application on WildFly application server that is downloaded and configured completely by the WildFly Maven Plugin. ...

Remote Debug a Pod’s Java Process Simple steps for remote debugging a Java process running on a k8 pod: Edit deployment and add the following parameters to the Java start line: -agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=127.0.0.1:5005 Also add the following port mapping at the section container → ports in the deployment: - containerPort: 5005 protocol: TCP Safe, wait for the new pods and then add a port forward for port 5005 for this pod: kubectl port-forward podname 5005 ...