Describing a methodology, approach, or focus that prioritizes code above other considerations, such as user experience, business objectives, or system architecture. A code-centric approach often emphasizes efficiency in coding, code reuse, and adherence to specific coding standards. It can lead to well-structured, optimized, and maintainable code. However, it might overlook end-user needs or business goals if not balanced with other priorities. A project adopting this approach heavily emphasizes the development and maintenance of the software's source code.
Code-centric meaning with examples
- The initial phases of the project followed a code-centric approach, resulting in a modular and efficient core library. The development team focused on crafting clean and optimized code, even before finalizing the user interface. This approach helped them build robust functionality, despite neglecting the needs of the end users at the onset. The strategy, however, required an eventual shift to embrace a more user-centric process for the best final results.
- Before the refactor, the team adopted a very code-centric approach, which led to tightly-coupled components. While each function worked efficiently, the system's architecture was difficult to adapt or scale, hindering the project's progress. The business needs were not initially considered, and changes to requirements created many issues for the developers. The code-centric strategy was very helpful for the developer's work, but less so when trying to bring business benefits to clients.
- The company's legacy system was a good example of code-centric development, prioritizing functional code over ease of use. The application was fast, with minimal code-level bugs, but was extremely difficult for users to navigate, and the documentation was lacking. Even though it could handle enormous volumes of data, usability issues and the absence of features rendered it cumbersome and inefficient for most users. The goal of an upgrade was to balance speed with features.
- Initially, the software engineering firm prioritized code-centric development to develop its new AI platform, focusing on optimizing machine learning algorithms. This allowed for rapid prototyping and experimentation with different models. However, the lack of user-friendly interfaces and integration challenges with other systems slowed deployment to market. Ultimately, it was realized that this project required both code focus and attention to deployment.
- The team employed a code-centric strategy. The developers concentrated on creating secure, reliable, and highly performant software, however, during a phase of the project where more user needs were considered, changes were made, and it was found that some business requirements were not understood at the outset. This approach was helpful to the developers, but required rethinking of the system and the code's functionality to add what was missing. The final product was a success.