As a consultant, I often talk to customers who have large amounts of custom ABAP code in their SAP systems and spend equally large efforts on testing all of it over and over again, since it is hard to know what exactly to test after changing certain parts.

Since costs matter a lot these days, many said customers are looking for ways to spend their test budget more efficiently. One way to do so is using tools to spot the areas where testing is more likely to find bugs than in others. In this post, I will compare two such tools, namely SAP’s Business Process Change Analyzer (BPCA) and CQSE’s Teamscale, which offers Test Gap Analysis (TGA) and will provide features for Test Impact Analysis (TIA) in the future.

Many software projects use online tools like GitLab, GitHub, Jira, and Gerrit for collaboration between developers. They discuss about code, reviewing features, and check if the automated tests passed.

However, the impact of a merge on code maintainability is not easy to judge in such tools, because it is hard to make decisions from a simple code diff. Some introduced maintainability problems (such as new architecture violations or copy-pasted code) are impossible to spot when seeing only the changed code.

In this blog post, I illustrate how Teamscale results can be integrated easily in existing online-collaboration tools. This helps to make existing code-review processes more thorough and efficient.

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.