I chose this major because I idolized an acquaintance who worked at Google. I fell in love with programming—the logic of guiding a computer to an output. But after years in the field as a software engineer and automation tester, I started to wonder: “Where is the science?” Building software or guiding a computer to do what I want felt like a craft, not a science.

What makes a science, a science?

According to Peter J. Denning, a renowned computer scientist and author of the article “The Science in Computer Science,” any field must meet the following criteria to be considered a science:

1. Purposeful Focus: Science focuses on a specific, real-world subject to figure out how it works, how we can use it, and how to deal with its effects. The subject can be either natural or artificial.

2. Organized Knowledge: Science isn’t just a pile of random facts; it is organized into a logical system (like theories and laws).

3. Experimentation: Science relies on hands-on testing. You don’t just “think” something is true; you run experiments to prove it.

4. Reproducibility: If you do an experiment and get a result, someone else should be able to do the exact same thing and get the result. If it only happens once, it’s not science.

5. Falsifiability: A science requires every scientific claim to be capable of being proven wrong. If there is no possible evidence that could prove an idea wrong, it’s a matter of faith, not science.

6. Predictive Power: Science doesn't just explain the past; it predicts what will happen in the future.

What makes Computer Science a science?

Let’s look at how CS stacks up against Denning’s checklist:

1. Does it have Purposeful Focus? → Yes. CS focuses on the fundamental laws of computation, answering two core questions:

   • Computability: Can this problem be solved by an algorithm? (In other words: Is it even possible for a computer to solve this, regardless of how powerful the hardware is?)

   • Complexity: How much time or memory does a solution need as the problem grows? Here, "time" isn't measured in seconds, and "memory" isn't measured in gigabytes. Instead, we measure the rate of growth—how the number of required steps or storage slots explodes as your data expands. These laws remain true whether you use a normal computer, a supercomputer (not a quantum computer), or a pen and paper.

2. Does it have Organized Knowledge? → Yes. CS is a logical system by using Mathematical Certainty to prove hypotheses and organize thousands of individual coding experiences into universal laws. An idea becomes a "Theory" in CS - like Complexity Theory - when it moves beyond a specific piece of code and is proven to be true for all computation. This creates a rigorous, structured map that applies regardless of the hardware or programming language used.

3. Does it have Experimentation? → Yes. We don't just guess if a solution is efficient. In CS, we use rigorous testing to validate our claims. For example, when comparing two sorting algorithms, we run them against massive, varied datasets to measure their actual execution time. These experiments provide the empirical evidence needed to prove that one method is mathematically superior to another in a real-world environment.

4. Does it have Reproducibility? → Yes. In science, someone else must be able to get the same result. In CS, we use open-source code and Docker containers so that other scientists can run our exact algorithms on their own machines to see if the results hold up.

5. Does it have Falsifiability? → Yes. CS requires that every one of its scientific claims be capable of being proven wrong. For instance, if I claim, "This algorithm is the fastest possible way to sort a list," that statement is falsifiable because it can be challenged. One could prove me wrong by discovering a faster counter-example or providing a mathematical proof that contradicts my claim.

6. Does it have the Predictive Power? → Yes. CS predicts how a system will behave before it is even built. For example, we can mathematically predict that if we double the data input, a certain algorithm will take four times longer to run O(n^2). We don't have to build it to know it will happen.

CS = science + mathematics + engineering

While Software Engineering was born out of the 'software crisis' of the 1960s, the dawn of the digital age has solidified its role as one of the backbones of modern CS. Today, as it drives business transformation, the overlapping presence of science, math, and engineering within the field can still be confusing. To provide clarity amidst this complexity, Denning defined the distinct roles of these three perspectives:

1. Science focuses on increasing the understanding of computers through experimental methods.

2. Mathematics focuses on deriving proofs for the theories to define what is logically possible.

3. Engineering focuses on designing systems (or software) suited to humans’ needs.

This post was inspired by the work of Peter J. Denning, inventor of the working-set model for virtual memory and author of "Great Principles of Computing" and “Computation Thinking.”