Die CQSE ist auf der Konferenz »The Architecture Gathering 2018« präsent - herzliche Einladung zum Fachsimpeln über Architektur- und Softwarequalität, einer Teamscale-Demo oder einem Plausch.

CQSE takes part in the conference »The Architecture Gathering 2018« - you are cordially invited to discuss architecture and software quality, watch a Teamscale demo or have a chat.

As more and more software applications are operated in the cloud, stakeholders of applications originally developed for another platform wonder how they can make their application cloud ready.

This article describes how we answer this question by analyzing cloud smells on code level and cloud requirements on architecture and infrastructure level during a software audit.

If you are using GitLab CI as your build server, you might be familiar with the [skip ci] syntax —it gives you the option to skip a build entirely by including this tag in your commit message.

However, there are scenarios when a build is still required but certain stages can be skipped. Let’s say you are preparing a new release, which requires a changelog entry.

In this post, I explain how to add a [skip tests] command for commit messages.

ConQAT was designed as a toolkit for rapid development and execution of software quality analyses. We started it as a vehicle for

performing research on automated software quality analysis back in 2005 at TUM and it also was one of the corner stones of CQSE when we started the company in 2009.

Over the last few years, Docker has largely been adopted to ease the deployment of applications.

One of the key benefits is bundling all required dependencies of an application in a single image that can be used right away without a huge installation and configuration overhead. Hence, we are using Docker for running our own Teamscale instances, as well as for Teamscale instances at the customer site.

Docker containers are also neat when testing and reviewing new features before they are merged back into master.

Especially on a local developer machine it is beneficial that the Docker image is small, because you don’t want to waste your SSD space with gigabytes of Docker image data.

Over the last years the Teamscale Docker image became quite large,…

A code quality control process must not be solely based on automated tools, rather it should combine tool-based analysis with a certain amount of human interaction.

There are endless variations of how a code quality control process can be implemented.

In this blog-post I’d like to give a few insights on how regular code assessments, named here monthly assessments, can lead to a feedback loop that improves the code quality control process.

Quality Control is one of the major services we do at CQSE. The aim of quality control is to stay (almost) clean of quality deficits or to achieve a continuous improvement regarding quality.

In most cases we focus on the quality of source code. To keep it simple, I will stick to code quality throughout this post. I will focus on two aspects of this process: Quality tasks and how these relate to quality goals.

Die CQSE GmbH ist im Finale um den Deutschen Gründerpreis. Die Jury ist beeindruckt von der rasanten Entwicklung des Unternehmens.

Der Analysesoftware-Hersteller CQSE GmbH ist für den renommierten Deutschen Gründerpreis nominiert. Das Garchinger Unternehmen zählt zu den Top-3-Finalisten in der Kategorie »Aufsteiger«. Damit bewertet die Jury die CQSE GmbH als eine der erfolgreichsten und vielversprechendsten Existenzgründungen der vergangenen Jahre in Deutschland.

Software is made of code. Well, yes, but not exclusively. Software engineering involves working with many other artifacts, such as models, architectures, tickets, build scripts, … and tests.

The goal of Teamscale is to provide meaningful and useful information about all aspects of software engineering. This is what we call »Software Intelligence«.

Consequently, in addition to providing profound insights into code quality, Teamscale performs many other sophisticated analyses, including architecture conformance analysis, analysis of issue tracker data, team evolution analysis, code ownership analysis, or data taint security analysis.