As developers, we spend more than 50% of our time trying to understand the systems we work on before we can make any changes or improvements. We analyze the impact of modifications, investigate errors, and design new features. However, we rarely reflect on the decision-making process itself.
In most cases, we focus on evaluating whether a decision was correct, but not on analyzing whether the way we made that decision was the best. To optimize our work, it is essential to improve the decision-making process itself.
The decision-making process in software engineering
In general terms, the decision-making process in software follows these steps:
We evaluate the problem: Before taking any action, denmark telegram data we must understand the context of the problem.
We explore the system: We investigate the components of the system and how they interact with each other.
We talk to other people or the system: We gather information through documentation, customer information, peers, or direct testing in the code.
We share information: We organize what we have learned so that we can make informed decisions.
We synthesize the information obtained: We draw conclusions from the analyzed data.
We use development tools: We interact with the system through tools that allow us to verify and validate our hypotheses.
However, in practice, this process is often highly manual and relies on monolithic tools that are not optimized for system exploration.
Problems in current decision making
In most development teams, decision-making faces significant obstacles that affect the efficiency and quality of work. Some of the main problems include:
Reliance on monolithic tools: Most development tools today are designed for generic use and are not tailored to the specific needs of each context. This limits the ability of teams to effectively visualize key data and make informed decisions.
Unstructured Scanning: Most developers scan systems through manual inspection and code reading without a defined methodology, making the process inefficient and error-prone.
Inaccurate architectural diagrams: Architectural diagrams are often generated manually, which introduces biases and interpretation errors. In addition, over time, they often become outdated and do not accurately reflect the actual structure of the system.
Lack of optimization in code understanding: It is assumed that the only way to understand a system is by reading large volumes of code, when in reality there are more efficient alternative approaches to exploring and visualizing information.