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Technical Articles

Here you will find our technical articles. These are also available in our INSIGHTS magazine, both in print and online.

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Agile Software Development

Agile vs. classic software development

Agile vs. classic software development

Classic approaches such as waterfall, V-model or spiral model or agile approaches such as Scrum or Kanban? At the beginning of a project, the question arises as to which of the existing process models is best suited for the current project.

Estimating in Agile Projects with Story Points

Estimating in Agile Projects with Story Points

This article highlights a type of effort estimation that is particularly useful in agile projects: Story Points.

UX design: colours on the web

UX design: colours on the web

About the effect of colors and what image they convey to us.

Welcome Change – The new Scrum Guide

Welcome Change – The new Scrum Guide

In November 2020, the time had come: The new Scrum Guide was published. Here you can find the fundamental changes at first glance.

Kanban in research projects: a short case study

Kanban in research projects: a short case study

Using Agile methods in classic research projects – is that possible? Andreas Lettner, expert and Agile Coach at RISC Software GmbH describes how a research project was led to success.

Agile & Test-Driven: Focus on the customer

Agile & Test-Driven: Focus on the customer

With the use of Test-Driven-Development, the concept of agile methods is also transferred to technical development.

Agile software development using DevOps workflows

Agile software development using DevOps workflows

Continuous integration, delivery, deployment, and improvement enable frequent releases without compromising quality.

Good software is green

Good software is green

Sustainability is a value that is becoming increasingly important in all areas of life, including the world of software development. Green" software development shows us strategies and ways of thinking to make our applications not only more environmentally friendly, but also cheaper and more robust at the same time.

Software Reengineering

Working with Fortran in 2020: Areas of application

Working with Fortran in 2020: Areas of application

The typical use case for Fortran as a programming language is computationally intensive numerical simulations, such as weather forecasts, flow simulations, stability calculations, and many more.

Working with Fortran in 2020: Parallelisation

Working with Fortran in 2020: Parallelisation

Since the trend of new CPUs is clearly towards ever higher numbers of processor cores, efficient parallelization is particularly important. With Fortran, the development of high-performance code is possible.

Working with Fortran in 2020: Do’s and Don’ts

Working with Fortran in 2020: Do’s and Don’ts

Due to the long development history of Fortran and to maintain compatibility with legacy code, there are numerous obsolete and sometimes obscure language features in current Fortran compilers. There are also selected capabilities offered by new Fortran standards. You can read a summary of do's and don'ts here.

C++20 Concepts (EN)

C++20 Concepts (EN)

A brief historical overview of the development of the programming language and the implementation of "Templates" and "Concepts".

Software-Reengineering: When will the legacy system become a problem?

Software-Reengineering: When will the legacy system become a problem?

Business-critical software is not immune to a certain aging process. However, an equivalent replacement is often not so easily available. But when is it time to replace the legacy system with the help of software reengineering?

Technical debt and legacy systems

Technical debt and legacy systems

How can you avoid a system or legacy system becoming a legacy system? Read the following article to learn how to identify legacy systems in time, remove them and ultimately avoid them.

Software Modernisation

Software Modernisation

For long-lived software systems, maintenance costs far exceed initial development costs. Escape the cost trap through timely proactive modernization measures. An evolutionary approach guarantees predictable costs, continuous releases and immediate customer benefits with manageable risk.

Reengineering: Engineering solution shines in new splendour through web porting

Reengineering: Engineering solution shines in new splendour through web porting

Classic desktop applications are increasingly being implemented as web versions in order to be available without installation. Software engineers at RISC Software GmbH transferred a complex software based on C++ to the web – and in a short time, thanks to an agile approach to the project.

Data Science and Prescriptive Analytics

Machine data analysis using artificial intelligence

Machine data analysis using artificial intelligence

The path from raw data to the finished model is usually further than expected. RISC Software GmbH developed a generic pipeline for AI-based data analysis for this purpose.

Can data science lead industrial companies out of the crisis?

Can data science lead industrial companies out of the crisis?

How it is possible to minimize costs, respond flexibly to fluctuations in demand, and avoid production downtime due to disruption.

Mastering the (industrial) Data (EN)

Mastering the (industrial) Data (EN)

How improved manufacturing is created from industrial and production process data.

Trust in Artificial Intelligence

Trust in Artificial Intelligence

In the future, when we create and use AI systems, we need to be aware not only of the opportunities, but also of the risks and challenges of using them. Guidelines can help us create AI systems that are not only efficient, but also ethical, safe, and trustworthy.

“OK Google: What is Natural Language Processing?”

“OK Google: What is Natural Language Processing?”

Natural Language Processing makes it possible to read, decode and understand human language by machine. Speech assistants, spelling correctors, email spam filters – NLP as a technology is omnipresent and already hides behind many processes and software applications deeply embedded in our everyday lives.

Exploratory Data Analysis with Time Series

Exploratory Data Analysis with Time Series

Time series are central to decision making, but bring many challenges to data analysis.

Automatic image segmentation of the abdominal aorta and stent grafts using artificial intelligence

Automatic image segmentation of the abdominal aorta and stent grafts using artificial intelligence

Researchers at RISC Software GmbH developed a method for medical segmentation of CT-A scans of abdominal aortic aneurysms using Deep Learning.

Mathematical modelling of production problems

Mathematical modelling of production problems

Mathematical models are predominantly used in science, especially in the natural sciences. Classic fields of application here are theoretical physics or, currently very prominent, epidemiology.

Making better decisions thanks to Prescriptive Analytics

Making better decisions thanks to Prescriptive Analytics

By combining forecasting models with optimization models, future decision options are simulated from company data and the best alternative course of action is selected.

Data Understanders: Leveraging enterprise data through intelligent Graph Databases

Data Understanders: Leveraging enterprise data through intelligent Graph Databases

Graph databases enable intuitive mapping of many real-world scenarios such as industrial manufacturing, traffic data analysis or IT infrastructure monitoring. This makes data not only more efficiently stored, but also much more usable.

Graph databases in practice

Graph databases in practice

Graph databases enable the mapping of many real-world scenarios such as: Traffic data analysis or IT infrastructure monitoring.

Data quality: From information flow to information content

Data quality: From information flow to information content

Making decisions is not always easy – one possible quick-win for companies can be derived from corporate data: forward-looking data quality management, a process that unfortunately often receives far too little attention.

Data Engineering – the solid basis for effective data utilization

Data Engineering – the solid basis for effective data utilization

Data engineering integrates data from a wide variety of sources and makes them effectively usable. This makes it a prerequisite for effective data science, machine learning and artificial intelligence, especially in the big data area.

Why even a good idea needs a feasibility study

Why even a good idea needs a feasibility study

To ensure that nothing stands in the way of a successful project launch and that the idea does not remain just an idea, a feasibility study should be carried out before implementation.

Time series analysis - but correct!

Time series analysis – but correct!

Time series data, for example machine data in industry or vital signs in medicine, are nowadays an important data source for the analysis of complex systems. However, for the development of practical models, the right choice of training data is a challenging task.

Optimisation has to be learned

Optimisation has to be learned

For complex problems in business and industry, optimization models and solution algorithms are used daily to make difficult decisions. Classical optimization methods are combined with machine learning to meet the high demands.

Transformer models conquer Natural Language Processing

Transformer models conquer Natural Language Processing

Progress in the field of Natural Language Processing is rapid and seems unstoppable. Between terms like Transformer, Transfer-Learning, BERT and T5 it is not always easy to keep up with the times.

Methods and tools for data preparation in the big data area

Methods and tools for data preparation in the big data area

Die Rolle von Big Data hat in zahlreichen Wirtschaftsbereichen stark an Bedeutung gewonnen. Es werden große Datenmengen gesammelt, die zur Optimierung herangezogen werden können. Hierbei kommen oft Methoden aus den Bereichen künstliche Intelligenz, maschinelles Lernen oder Statistik zum Einsatz.

Explainable Artificial Intelligence (XAI): How Machine Learning Predictions Become Interpretable

Explainable Artificial Intelligence (XAI): How Machine Learning Predictions Become Interpretable

Artificial intelligence is due to artificial neural networks that are modeled on the human brain. In certain situations, it is necessary to be able to explain the decision-making basis of the models from the field of deep learning for predictions.

With Natural Language Understanding (NLU) from text chaos to knowledge gain

With Natural Language Understanding (NLU) from text chaos to knowledge gain

Regardless of whether fault messages in manufacturing processes are to be analyzed, doctor's letters are to be filed in a structured manner, or products are to be suggested automatically, Natural Language Understanding (NLU) offers a broad spectrum of industry-specific and cross-industry applications for gaining knowledge from unstructured text data.

Stable power grid through planning and optimisation

Stable power grid through planning and optimisation

Which power plants must be used so that the load in a power grid is covered? How is the inaccuracy of the forecast for renewable energy sources taken into account? How should the charging and discharging cycle of energy storage systems in the power grid be coordinated with the current market price for electricity? These questions are part of the daily operational planning of a power system.

Abra CaTabRa: Automatically analyse and validate data and use it to train machine learning models

Abra CaTabRa: Automatically analyse and validate data and use it to train machine learning models

Data is collected to make predictions: Which target groups should my product be suggested to? What weight loss can I expect if I walk every day? When do I have to replace the wearing parts of my machines in order to have the shortest possible downtimes?

Data quality in practice

Data quality in practice

One of the central goals of data engineering is the preparation of data sets according to the requirements of the users or the subsequent process steps. The use of data can range from model training in the field of machine learning to improved internal company reporting based on an integrated database.

User Experience

6 steps to the optimal user experience

6 steps to the optimal user experience

In contrast to user interface (UI) design, UX design deals with the experience, i.e. the perception of the website. The focus is on the emotions that users feel when experiencing the web application.

UX design: colours on the web

UX design: colours on the web

About the effect of colors and what image they convey to us.

Intelligent Transport Systems

We need a mobility revolution!

We need a mobility revolution!

With which (transport) means do we manage to initiate the mobility revolution together and to change mobility habits towards environmentally friendly alternatives?

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