PA4: Runtime, Measured and Modeled (open)

This assignment will give you experience working with big-Ο/θ/Ω representations, practice matching them to implementations, and perform measurements of the runtime of different methods.

This assignment is inspired by a combination of a lab in Swarthmore College’s CS35, and by a similar assignment by Marina Langlois in CSE12 at UCSD

Read the whole writeup before starting – there are several different pieces of the assignment you will need to hand in. In particular, you will submit:

Make sure you know how to generate a PDF (Google Docs is a good option) and ask for help if you don’t, well in advance of the deadline!

Big-O Justification

Indicate whether the following assertions are true or false, and give a justification:

  • n + 5n2 + 8n4 is O(n)
  • n! + n2 is O(n * log n)
  • log n + n * log n + log(log n) is Ω(n)
  • n2 + n/4 + 6 is Θ(n3)
  • 1/(n50) + log32 is Θ(1)
  • 1/(n50) + log32 is O(1)

If you are justifying the positive direction, give choices of n0 and C. For big-Θ, make sure to justify both big-O and big-Ω, or big-O in both directions.

These slides give a definition of big-Θ and big-Ω, which were not covered in detail in class. The strategies we showed in class for big-O can also be used for big-Ω, and big-Θ simply combines the two.

If you are justifying the negative direction, indicate which term(s) can’t work because one is guaranteed to grow faster or slower than the other.

As a quick guide, here is an ordering of functions from slowest-growing (indeed, the first two shrink as n increases) to fastest-growing that you might find helpful:

  • f(n) = 1/(n2)
  • f(n) = 1/n
  • f(n) = 1
  • f(n) = log(n)
  • f(n) = sqrt(n)
  • f(n) = n
  • f(n) = n2
  • f(n) = n3
  • f(n) = n4
  • … and so on for constant polynomials …
  • f(n) = 2n
  • f(n) = n!
  • f(n) = nn

Provide this written up on the first page of pa4.pdf.

List Analysis

Consider the two files and, which are the starter files from PA2. Answer the following questions, and justify them with one or two sentences each:

  • Give a tight big-O bound for the best case running time of prepend in ArrayStringList
  • Give a tight big-O bound for the best case running time of prepend in LinkedStringList
  • Give a tight big-O bound for the worst case running time of prepend in ArrayStringList
  • Give a tight big-O bound for the worst case running time of prepend in LinkedStringList
  • Give a tight big-O bound for the best case running time of add in ArrayStringList
  • Give a tight big-O bound for the best case running time of add in LinkedStringList
  • Give a tight big-O bound for the worst case running time of add in ArrayStringList
  • Give a tight big-O bound for the worst case running time of add in LinkedStringList

In all cases, give answers in terms of the current size of the list, and assume that the list has some non-empty size n. That is, you shouldn’t consider the empty list as a best case; instead think about the best case based on other factors like size, capacity, and nodes.

Notable points to consider:

  • Creating an array takes time proportional to the length of the array
  • When considering the running time of a method, make sure to take into account any helpers methods it uses!

Example for get in the LinkedStringList class:

The get method is O(1) in the best case, when the index is 0. In this case
it will do constant work checking the index and immediately return the
first element, never entering the while loop.

The get method is O(n) in the worst case, because the index could be at
the end of the list (for example, index n - 1). In this case, the while
loop will run n times, spending constant time on each iteration, resulting
in overall O(n) number of steps taken.

Provide this written up on the second and third pages of pa4.pdf.

Mystery Functions

We have provided you with a .jar file that contains implementations of the following methods:

	public static void f1(int n) {
		int a = 0;
		for (int i = 0; i < n; i += 1) {
			a = i;
	public static void f2(int n) {
		int a = 0;
		for(int i = 0; i < n; i += 2) {
			a = i;
	public static void f3(int n) {
		int a = 0;
		for(int i = 0; i < n * n; i += 1) {
			a = i;
	public static void f4(int n) {
		int a = 0;
		for(int i = 0; i < n; i += 1) {
			for(int j = i; j < n; j += 1) {
				a = i + j;
	public static void f5(int n) {
		int a = 0;
		for(int i = 0; i < n * n; i += 1) {
			for(int j = 0; j <= i / 2; j += 1) {
				a = i + j;
	public static void f6(int n) {
		int k = 1, a = 0;
		for(int i = 0; i < n; i += 1) {
			for(int j = 0; j <= k * 2; j += 1) {
				a = i + j;
			k = k * 2;

However, in that file, they are called mysteryA-F, and they are in a different order, and we don’t provide the source of that file. You have two tasks: determining a big-Θ bound for each method labeled 1-6 analyzing the source above, and determining which mystery method A-F corresponds to the implementations above by measuring against provided (but hidden) implementation.

Identifying Bounds from Code

Determine a big-Θ bound for each function, and justify it with a few sentences. Give only the most relevant term, so use, for example Θ(n), not Θ(4n + 2) Provide this written up on the fourth page of pa4.pdf.

Measuring Implementations

You will write a program to:

  • Measure the mystery methods
  • Use your measurements to match the mystery methods to the sources above
  • Generate several graphs to justify your work

You have a lot of freedom in how you do this; the deliverables you need to produce are specified at the end of this section. There are a few methods that we require that you write in order to do this, and they will help guide you through the measurement process.

The measure Method

You must write the following two methods in the Measure class:

public static List<Measurement> measure(String[] toRun, int startN, int stopN)`
public static String measurementsToCSV(List<Measurement> measurements)

where Measurement is defined in

  • measure should work as follows:

    1. It assumes each string in toRun is one of the letters A-F.

    2. For each of the implementations to run, it runs the corresponding mysteryX method stopN - startN times, providing a value of n starting at startN and ending at stopN each time.

    3. For each of these runs, it measures the time it takes to run. You can do this by using the method System.nanoTime() (see an example in discussion code)

    4. For each of the measured runs, it creates a Measurement whose valueOfN field is the value that was used for the given run, whose name field is the single-letter string of the implementation that ran, and whose nanosecondsToRun field is a measurement, and adds it to a running list of measurements.

    5. The final result is the list of measurements.


This call:

		measure(new String[]{"A", "B"}, 40, 100);

Should produce a list that has 122 measurements, 61 of which will have name equal to "A" and 61 of which will have name equal to "B". Each of the 61 for each name will have a different valueOfN from 40 to 100, and each will have a different number of nanoseconds (as was measured).

The measurementsToCSV method

The measurementsToCSV method takes a list of measurements (for example, as returned from measure) and generates a comma-separated-values String of the measurements. It should have the following format, where the first row is a literal header row and the other rows are example data. Note that this data is completely made up, and may not match your measurements.

You might choose to put all of the measurements for a single letter together:

... many rows for A ...
... many rows for B ...

You might also choose to put all of the measurements for a single round of n together:

... many alternating rows of A, B ...

Either layout is fine, do what makes sense to you, or what matches your measure function best, etc.

Strategies for Measuring

You can use the measure and measurementsToCSV methods to produce data about how the functions behaved in terms of their runtime. You should fill in the main method with whatever you find useful for using your measuring methods to compare the mystery implementations. You have total choice in how you implement this – you can add new helpers, print the CSV format out to a file, copy/paste it into a spreadsheet, use a tool you like for plotting, etc. The goal is to use measurements to identify the different implementations. Feel free to look up documentation for writing Strings out to files and use it, or use System.out.println and copy/paste the output, etc. It’s probably pretty expedient to copy the data into Excel or a Google Sheet.

There are a few high-level strategies to consider:

  • If an implementation is very slow, it could take a really long time to measure it for large n. If you notice something is taking a long time, stop the program and try the same mystery methods on a smaller input range. Does the smaller range tell you anything useful?
  • Some of the methods might have similar big-O bounds, but have different constants that can be measured in terms of absolute time.
  • Some of the methods might take vastly different times to run on certain inputs, so plotting them next to one another will show one with a flat line at 0 and the other with some interesting curve. Make sure to check what the relative numbers are when inspecting the output.

You will use these measurements to figure out which mystery method matches the implementations above, and generate three graphs to justify your answers.

Avoiding Obscuring Optimizations

On many platforms and Java versions, simple methods like the above get optimized to run much faster than their theoretical number of steps might suggest. Java is pretty smart – it can, while running, figure out how to make them run quickly enough that empirical measurements become hard to make. If you’re seeing that even on values of n in the hundreds of thousands, you get effectively constant behavior, you should try disabling these optimizations to get more useful measurements for distinguishing the implementations.

Instructions for doing this are in the Turning Off Java Optimizations section of this Google Doc (scroll to the end):

Note that this will make all the mystery methods run a lot slower, so you may want to decrease the values of n you use after making this change to avoid waiting a long time.

Submission Instructions

There are four artifacts to submit for this PA:

By Tuesday (2/4), 11pm

  • The pa4-code assignment in Gradescope, where you will submit your final code for performing measurements, along with a README describing how you measured things. This README should just contain a few sentences describing how you ran your program to generate data and what decisions you made
  • The pa4-written assignment in Gradescope, where you will submit a single PDF file called pa4.pdf.
    • The first page should have your big-O justifications, which should take up one page (you don’t have to write a page of text! But don’t put any other answers on the first page)
    • The second and third pages should have your List analysis, which should take up pages 2 and 3 (you don’t have to write two pages of text! But don’t put any other answers on the second or third pages)
    • The rest of the pages should have your matchings for the mystery functions, along with your graphs and justifications
      • The BigO bounds for each implementation f1-6.
      • A listing that matches each of mysteryA-F to an implementation f1-6 above
      • Three graphs that justify a few choices above. These don’t need to exhaustively describe all of your matchings, but they must be generated from real data that you measured using measure, and they must show an interesting relationship that helps justify the matching.

If you want a guide on how to get from the CSV data to a graph, look here:

Grade Breakdown

Note that this assignment is mostly manually graded, so there’s little value in submitting after the deadline.

(70 total points)

  • 16 points measure and measurementsToCSV [autograded]
  • 2 points README describing how you measured [manually graded]
  • 12 points initial big-O justifications [manually graded]
  • 16 points list method analysis [manually graded]
  • 24 points matching activity [manually graded]
    • 12 points for complexity bounds on f1-6
    • 6 points for a correct matching
    • 6 points for 3 relevant graphs