fortiss Center for Code Excellence

Center for Code Excellence

Expertise for outstanding software quality

The Center for Code Excellence (CCE) acts as a central point of contact for small and medium-sized companies when it comes to the analysis, development and implementation of modern methods, techniques and processes in software development. With our expertise in the areas of software engineering intelligence, software engineering management and software engineering for machine learning, we want to enable companies to develop outstanding, sustainable and future-oriented software and thus achieve code excellence.


To build customer trust and gain a competitive advantage, flawless software products and services are crucial. The Center for Code Excellence's (CCE) field-proven research activities in software engineering intelligence aim to strengthen quality assurance, develop thorough test procedures and support effective maintenance strategies.

In an environment where even minor software errors can lead to significant business disadvantages, this research focus equips small and medium-sized enterprises (SMEs) with the necessary tools and knowledge to ensure the optimal performance, reliability and resilience of their software.

Our research activities in the area of software engineering focus on methods such as Agile and DevOps, which respond quickly to changes and user feedback. This leads to optimized software development processes, promotes collaboration between departments and enables timely and high-quality updates. Continuous development in particular enables flexible and responsive behavior to market changes, thus cementing the longevity of the software

Machine learning (ML) is increasingly becoming an integral part of many enterprise solutions. ML project management differs significantly from traditional software development however.  With CCE's empirical research on software engineering for machine learning, we help companies introduce best practices in the field of MLOps.

With this approach we ensure the creation of highly accurate ML models that can be easily integrated into existing software systems. Version control for code and data enables iterative improvements without losing sight of the changes that were made. At the same time, continuous deployment makes sure that updates to the models occur promptly. An effectively managed machine learning development process enables faster and better integrated development.


CCE Trends

  • Trend radar
  • Knowledge base for new software technologies

For all those who want to know which software topics are currently hot, CCE Trends offer an overview to identify new technologies, obtain an independent, quantitative evaluation over time and obtain further information on similar technologies and toolchains.

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CCE Quick Checks

  • Potential analysis, benchmarking, retrospective of development activities

    With fortiss Quick Checks, you can query important components of successful software development processes to identify hidden potential in your company. We offer initial online insights into your development processes through automated process evaluations. Building on this, we offer specialized workshops and training materials tailored to your needs.

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CCE Blog

  • Transfer activities
  • Training materials
  • Surveys

Take part in the CCE surveys and events, discuss your challenge with others or present your contribution. The CCE blog also provides you with regular updates, articles and other knowledge and transfer materials.

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Your contact

Severin Kacianka

Dr. Severin Kacianka
Head of Center for Code Excellence

+49 89 3603522 286

Alexandros Tsakpinis

Alexandros Tsakpinis
DevOps and MLOps

+49 89 3603522 185


More information

Center for Code Excellence
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fortiss Hot Spot MLOps - Experiences in engineering AI-enabled systems, 2022

Scientific publications

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  • 2022 Feature Sets in Just-in-Time Defect Prediction: An Empirical Evaluation Peter Bludau and Alexander Pretschner In Proceedings of the 18th International Conference on Predictive Models and Data Analytics in Software Engineering, pages 22-31, Association for Computing Machinery. Details DOI BIB
  • 2022 PR-SZZ: How pull requests can support the tracing of defects in software repositories Peter Bludau and Alexander Pretschner In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 1-12, IEEE Computer Society. Details DOI BIB