Knowledge Distribution

Good knowledge sharing makes R&D teams more robust

Agile software development is teamwork and teamwork relies on coordination, communication and sharing of knowledge. Not all members in the project team have the same level of experience and knowledge. If team leadership (and/or HR) does not balance these knowledge asymmetries, it will impact the work results that then will rarely meet expectations. Could this have been avoided? Yes!

With the DETANGLE Analysis Suite, the essential questions about knowledge distribution and knowledge transfer can be derived and answered directly from the code. In doing so, you will get to know

• whether new employees are effectively trained and integrated by their colleagues
• whether experienced developers pass on their knowledge and delegate parts of their work
• whether important code areas are worked on by individual developers alone
• whether the work distribution favors burn-out or bore-out challenges

Relying on individual top developers might be efficient in the short term, but it has a high risk if they need a longer absence  or quit. Another impact against the robustness of the development process is when talented developers leave the company because they have not been sufficiently trained and integrated. That is an expensive misinvestment and could have been avoided. A good distribution of knowledge to more than just a few team members at a time, and the transfer of knowledge even beyond this circle, provides peace of mind, efficiency and security for project and R&D management. It also ensures more self-confidence and satisfaction for the younger developers, who may feel lost at the beginning and bored later on.

From our projects we know that the performance of software engineering becomes more "robust" and reliable in the long term through a meaningful knowledge distribution between all team members. 

Why are gaps in documentation counted as technical debt?

A lack of documentation of system-relevant code areas basically leads to reduced efficiency in code development, because it takes more time to understand the code. It can become dramatic in case of illness or termination of the responsible developer. This sustained loss of knowledge further amplifies the quality risks and inefficiencies in code maintenance and innovation. Documentation gaps and insufficient quality of it are therefore counted as technical debt.

Lack of documentation leads to errors and additional maintenance efforts especially in the following constellations:

• Inherently complex code areas
• code areas that are difficult to understand due to unstructured programming
• coupled code areas due to lack of modularization
• code areas that were edited by many developers

Why does code traceability lead to better software quality?

Every change in the code should be for a reason, and this reason should be known and documented. For each new software version, all features and bug fixes must not only be known, they must also be verifiably found in the code and be understandable. With methodical best practices and appropriate tools, this is part of the important version management. Version management in turn creates the relevant project knowledge that helps to predetermine the quality of future software versions.

The DETANGLE Analysis Suite pulls analysis data from these tools (e.g. code repositories, issue trackers, DevOps platforms) and checks for unique traceability in the code to features and bug fixes. Even features that are "lost" in the code can be identified. This significantly increases the traceability of the code and avoids unintended problems due to side effects from maintenance measures and the addition of new features.