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.
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:
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:
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:
![[Translate to English:] fortiss Whitepaper Center for Code Excellence](/fileadmin/user_upload/Dateien_MIT_Referenzen_-_Zuordnen/Bilder/fortiss_CCE_Whitepaper.png "[Translate to English:] fortiss Whitepaper Center for Code Excellence")
