Introduction

Embarking on a journey from the medical field to the dynamic world of software engineering is undoubtedly a significant shift. As a former medical assistant with a background in Kinesiology, I have brought a unique perspective to the realm of programming. In this blog post, we'll explore how you can leverage your scientific mindset to approach debugging in computer programming methodically, just as you would in a scientific investigation.

Let's begin!

Understanding the Problem

1. Ask the Right Questions:

• In medicine, diagnosis begins with asking the right questions. Ask questions about the patient's symptoms, past medical history, and other pertinent medical information.
• In software engineering, the problem-solving process begins by thoroughly understanding the problem. Start by searching unknown terms, noticing error messages, and visualizing problem statements.

2. Define the Problem Statement:

• In both medicine and software engineering, clearly define the problem you are facing by rewording the problem statement. Break it down into smaller, more manageable parts, just as you would identify specific symptoms to diagnose a medical condition.

Hypothesis

3. Formulate Hypotheses:

• Drawing parallels with your medical background, formulate hypotheses about the potential causes of the issue. Think about different scenarios that could lead to the observed problem
  • Medicine example: "The patient's rash could be caused by his recent upper respiratory tract infection, allergic reaction, or simply from excessively dry skin."
  • Software engineering example: "The styling changes not being affected could be caused by unlinked CSS styling sheet, CSS specificity is not high enough, or the browser is caching the styling changes."

4. Prioritize Hypotheses:

• Just as a medical professional prioritizes potential diagnoses based on severity, prioritize your hypotheses. Based on your knowledge and past experiences, start with the most likely sources of the problem.

Experimentation

5. Design Controlled Experiments:

• In medicine, try to treat the patient by testing your hypothesis and excluding as much bias as possible. It is important to have low variability in treatment plans so it is easier to figure out each treatment plan is most effective.
• In software engineering, develop a systematic approach to test your hypotheses. This may involve writing test cases, isolating specific parts of the code, or utilizing debugging tools.

6. Collect and Analyze Data:

• In medicine, execute your experiments and collect data.
• In the software realm, this involves observing the program's behavior, examining logs, and understanding the outputs of your experiments.

Evaluation

7. Evaluate Results:

• Analyze the data collected during your experiments. Does it support or refute your hypotheses? Just as in medicine, not all symptoms may lead to the same diagnosis; similarly, not all issues may have a single cause.

8. Refine or Discard Hypotheses:

• If your initial hypotheses are proven incorrect, refine or discard them. Be open to adjusting your understanding of the problem based on new insights gained during the debugging process.

Conclusion

Transitioning from the medical field to software engineering is not just a change in profession; it's an evolution in problem-solving methodologies. By applying the scientific method to debugging, individuals can bring a systematic and analytical approach to resolving programming challenges. My background in Kinesiology has equipped me with a diverse skill set that, when applied to coding, can foster a unique and effective approach to problem-solving. By embracing this interdisciplinary perspective, individuals can find that debugging is not merely fixing errors but a scientific exploration into the intricacies of code.

Thanks for reading!🚀