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Common Running Time

There are some common running times when analyzing an algorithm:

O(1) – Constant Time Constant time means the running time is constant, it’s not affected by the input size.

O(n) – Linear Time When an algorithm accepts n input size, it would perform n operations as well.

O(log n) – Logarithmic Time Algorithm that has running time O(log n) is slight faster than O(n). Commonly, algorithm divides the problem into sub problems with the same size. Example: binary search algorithm, binary conversion algorithm.

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The problem

Let's consider the following code:

class UseCase() {
    suspend operator fun invoke(param1: String) = Unit
}

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// Add into root project, make sure you have Gradle Enterprise plugin
val buildScanApi = project.extensions.findByName("buildScan") as BuildScanExtension
subprojects {
// If you want to limit it to certain module(s), wrap it with: if (name == "module-name") { }
    val fingerprinter = serviceOf<org.gradle.internal.fingerprint.classpath.ClasspathFingerprinter>()
    tasks.withType(org.jetbrains.kotlin.gradle.dsl.KotlinCompile::class.java).configureEach task@{
        doFirst {
            var classLoader: ClassLoader = [email protected]
            while (classLoader is java.net.URLClassLoader) {
                val fingerprints = mutableSetOf<Array<String>>()