Center for Code Excellence

Center for Code Excellence

Expertise for outstanding software quality

Center for Code Excellence

The Center for Code Excellence (CCE) is the starting point for small-to-medium enterprises for the analysis, development and transfer of modern software development methods, techniques and processes.

Reliable software solutions for sustainable quality standards

In order to consolidate customer trust and gain a competitive edge, it is crucial that software products and services function flawlessly. The Center for Code Excellence's (CCE) field-proven research activities in Software Engineering Intelligence aim to strengthen quality assurance, develop thorough testing procedures and support effective maintenance strategies.

In an environment where even small 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.

The following focal points are included:

  • Software Engineering Intelligence
  • Quality assurance
  • Testing
  • Maintenance

Efficient software management for agile and sustainable development

Our research in the field of software engineering management focuses on methods such as Agile and DevOps that respond quickly to changes and user feedback. This optimizes software development processes, promotes collaboration between departments and enables timely and high-quality updates. In an increasingly dynamic and complex software landscape, it is crucial to react flexibly to new requirements and challenges.

Continuous development ensures that software is flexible and responsive to market changes, ensuring its longevity. This adaptability not only ensures the quality of the software, but also strengthens its long-term competitiveness on the market.

The following focal points are included:

  • Software Engineering Management
  • Agile
  • DevOps
  • Continuous development

Optimized control of ML projects for seamless integration

Machine learning (ML) is increasingly becoming an integral part of business solutions. However, the management of ML projects differs significantly from traditional software development. With CCE's empirical research on software engineering for machine learning, we support organizations in adopting best practices in MLOps.

This approach ensures that ML models are accurate and integrate seamlessly into existing software systems. Version control for code and data enables continuous improvement without losing track. At the same time, continuous delivery ensures that model updates happen quickly. A well-managed development process leads to faster and better integrated development.

The following focus areas are included:

  • Software engineering for machine learning
  • Version control
  • Continuous delivery (CD)
  • Processes (MLOps)

More information

Whitepaper Code Excellence
Whitepaper Code Excellence
Flyer Center for Code Excellence
Flyer Center for Code Excellence

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Video interview "Software Engineering ist die Schlüsseldisziplin in der Digitalisierung" (Dr. Johannes Kroß, fortiss; Prof. Alexander Pretschner, fortiss and Technical University of Munich)- German
Dr. Severin Kacianka

Your contact

Dr. Severin Kacianka

+49 89 3603522 286
kacianka@fortiss.org

 Alexandros Tsakpinis

Your contact

Alexandros Tsakpinis

+49 89 3603522 185
tsakpinis@fortiss.org

Projects

Publications

  • 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, 2022. IEEE Computer Society. Details DOI BIB
  • 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, 2022. Association for Computing Machinery. Details DOI BIB