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CS210: Software Development
This assignment was written by Ali Malik originally for CS106L, a class he taught at Stanford
University. I have adapted it to work in Java for CSc210.
Due: Wednesday 11/20 by 11:30PM
Submission:
• WikiRacer.java
• WikiScraper.java
• MaxPQ.java - Implementation of a max-heap priority queue.
Overview
This assignment has multiple goals:
• Implement a priority queue backed by a binary Max-heap
• Practice using Sets
• Implement a program using multiple interacting data structures
• Learn about performance optimization strategies including parallelization.
• Implement memoization for computationally expensive function calls.
• See the importance of the above performance optimization techniques.
You will implement a program that finds a Wikipedia link ladder between two given pages.
A wikipedia link ladder is a list of Wikipedia links that one can follow to get from the start
page to the end page. It is based on the popular game WikiRace, which you can play online
here. The objective is to race to get to a target Wikipedia page by using links to travel from
page to page. The start and end page can be anything but are usually unrelated to make the
game harder.
Assignment
This assignment is broken up into different parts. But before breaking it into separate parts, it
is helpful to get a large overview of the assignment. A broad pseudocode of the algorithm is
as follows:
To find a ladder from startPage to endPage:
Make startPage the currentPage being processed.
Get set of links on the currentPage.
If endPage is one of the links on the currentPage:
We are done! Return the path of links followed to get here.
Otherwise visit each link on currentPage in an intelligent way and search
each of those pages in a similar manner.
Part One: Internet Connectivity
For this assignment we have provided some starter code. Part of this assignment requires
fetching webPages in order to be able to look at the HTML to find the various links on a page.
You are responsible for testing this on your machine by Wednesday 11/13. We will not help,
debug, or even look at issues with this code after that date. One of the benefits of Java, is that
it should work independent of what kind of computer it is running on, but as this is the first
time this assignment has been given in Java, it is important to test this crucial aspect of the
assignment. I will not detail how to test this part of the assignment. That is your job. Read
through the starter code to understand which method is responsible for fetching the HTML,
test the inputs/outputs to this method to make sure they work as expected. This part tests
code reading/understanding/testing. The only hint I would like to mention is that most, if
not all, web browsers allow you to see the HTML of any webpage.
Part Two: Scraping the HTML to find links
Congratulations! You have already finished this part. This is what you did for the drill.
But note the function name has changed, since you need to do a few other tasks before
finding the actual wikiLinks. Fill in the private static Set scrapeHTML(String
html) method with the solution you came up with for the drill. As a reminder, this function
goes through a String of the HTML of a webpage and returns a Set which contains
the names of all of the valid wikiLinks found in that HTML. For our purposes, a valid link is
any link that:
• is of the form ”/wiki/PAGENAME”
• PAGENAME does not contain either of the disallowed characters ’#’ ’:’
You have examples in the drill testing code. Another example follows the brief HTML description
below.
HTML
If you are curious, here is a quick overview of HTML. HTML (Hypertext Markup Language)
is a language that lets you describe the content and structure of a web page. When a browser
loads a webpage, it is interpreting the content and structure described in the HTML of the
page and displaying things accordingly. There are many parts to HTML, but we are mainly
focused on how hyperlinks are formatted. A section of HTML on a Wikipedia page might
look like:


The sea otter (Enhydra lutris) is a
marine mammal native to the coasts of the
northern and eastern North Pacific Ocean.


which would display the following:
The sea otter (Enhydra lutris) is a marine mammal native to the coasts of the northern and
eastern North Pacific Ocean.
where the blue text would be a link to another wikipedia article. Here we can see that to
specify a piece of text as a hyperlink, we have to surround it with the
tag. This would look like this:
link text
Example


In topology, the long line (or
Alexandroff line) is a
topological space somewhat similar to
the real line, but in a certain way "longer". It
behaves locally just like the real line, but has different large-scale
properties (e.g., it is neither
Lindel¨of nor
separable). Therefore, it serves as one of
the basic counterexamples of topology
[1].
Intuitively, the usual real-number line consists of a countable number of line
segments [0,1) laid end-to-end, whereas the long line is constructed from an
uncountable number of such segments. You can consult
this book for more
information.


In the above case, your method should return a Set containing:
Topology
Topological_space
Real_line
Lindel%C3%B6f_space
Separable_space
3
Part Three: findWikiLadder
Now it is time to find the actual wiki ladder! You will need to fill in the function:
private static List findWikiLadder(String start, String end)
This function takes in the name of a start page and the name of the target page and returns a
List that will be the link ladder between the start page and the end page. For example,
a call to findWikiLadder("Mathematics", "American literature") might return a list
that looks like Mathematics, Alfred North Whitehead, Americans, Visual art of the United states,
American literature. To clarify, this function takes in link names, not actual URLs to these articles.
These can be easily converted, see private static String getURL(String link) in
WikiScraper.java.
We want to search for a link ladder from the start page to the end page. The hard part in
solving a problem like this is dealing with the fact that Wikipedia is enormous. We need to
make sure our algorithm makes intelligent decisions when deciding which links to follow so
that it can find a solution quickly. A good first strategy to consider when designing algorithms
like these is to contemplate how you as a human would solve this problem. Let’s work with
a small example using some simplified Wikipedia pages.
Suppose our start page is Lion and our target page is Barack Obama. Let’s say these are
the links we could follow from the Lion page:
• Middle Ages
• Federal government of the United States
• Carnivore
• Cowardly Lion
• Subspecies
• Taxonomy (biology)
Which link would you choose to explore first? Clearly, some of these links look more promising
than others. For example, the link to Federal government of the United States looks like
a winner since it is probably close to Barack Obama. In our algorithm, we want to capture
this idea of following links to pages “closer” in meaning to the target page before those that
are more unrelated. How can we measure this similarity?
One idea to determine “closeness” of a page to the target page is to look at the links in
common between that page and the target page. The intuition is that pages dealing with
similar content will often have more links in common than unrelated pages. This intuition
seems to pan out in terms of the links we just considered. For example, here are the number
of links each of the pages above have in common with the target Barack Obama page:
4
Pages Links common with Barack Obama page
Middle Ages 0
Federal government of the United States 5
Carnivore 0
Cowardly Lion 0
Subspecies 0
Taxonomy (biology) 0
In our code, we will use a List to represent a ladder of links. Our pseudocode
looks like this:
Finding a link ladder between pages startPage and endPage:
Create an empty priority queue of ladders (a ladder is a List).
Create/add a ladder containing {startPage} to the queue.
While the queue is not empty:
Dequeue the highest priority partial-ladder from the front of the queue.
Get the set of links on the current page. The current page is the page
at the end of the just dequeued ladder.
If the endPage is in this set:
We have found a ladder!
Add endPage to the ladder you just dequeued and return it.
//Part Four optimizations. Ignore this comment until you get to part Four.
For each neighbor page in the current page’s link set:
If this neighbor page hasn’t already been visited:
Create a copy of the current partial-ladder.
Put the neighbor page String name on top of the copied ladder.
Add the copied ladder to the queue.
If while loop exits, no ladder was found so return an empty List
Note: this priority queue dequeues the ladder with the highest priority number! This is
different from what you did for PatientQueue. This priority queue should be backed by a
binary MAX-heap.
It is tempting to see this pseudocode and jump into coding right away. That would be
a poor choice. You need to first understand what this pseudocode is doing and why. Read
through it a few times to ensure you understand the purpose of each step.
You will need to follow the above pseudocode and write a new implementation of a priority
queue that will dequeue the ladder with the highest priority. Note that you will not need
all of the methods that were required for PA9. You only need to implement the methods that
are required for PA10. This class should be called MaxPQ.java and written in the empty file
that we have provided in the starter code.
Remember that after completing Part Two, you have a very valuable tool. The WikiScraper
class now has a method public static Set findWikiLinks(String link) which
takes in a link name like Barack Obama and returns a Set of all of the wiki links
from that page.
5
Part Four: Now make it fast!
Run your program on any of the below testcases. You will see that your program is extremely
slow. Let’s make it fast! I am going to describe my process when speeding up this program. I
first had a working implementation like you (hopefully) have at this point. It also ran really
slow. What do you do in this situation? Well, two things: 1. Read through your code to search
for inefficiencies 2. Run some tests to see what is taking so long!? This can be sophisticated
or as easy as adding print statements and seeing which parts of your code take really long. I
encourage you to do this right now.
I wanted to require you to time how long it takes to find a ladder between Emu and
Stanford University without the optimizations you are about to perform, but I will not require
you to turn this time in. You should though! It will make you very proud of the memoization
you are about to perform.
The part that needs speeding up is fetching the HTML of the different webpages. Add
the below lines of code right where the comment that says ”Part Four optimizations” is in the
pseudocode above.
links.parallelStream().forEach(link -> {
WikiScraper.findWikiLinks(link);
});
The above code assumes you have named your Set of links from the current page
as ’links’.
The above code simply calls findWikiLinks on each link in the set. This is worthless until
you have memoized that function call! Now implement memoization as we saw in class for
the Fibonacci sequence. Remember the whole point of this process is to save results of your
function calls. i.e. if you have already called findWikiLinks(”Emu”), don’t do the work again,
just return the result you previously calculated!
Memoization should only require 4-10 lines of code.
Now find a ladder between Emu and Stanford University. It should be way faster!
Test Pages
Here are some good test pages to try your algorithm on in the early stages:
• Start page: Fruit
• End page: Strawberry
• Expected return: Fruit, Strawberry
• Notes: This should return very quickly since it is a one link jump
• Start page: Milkshake
• End page: Gene
• Expected return: Milkshake, Carbohydrate, DNA, Gene
• Notes: This ran in about 30 seconds on my computer.
• Start page: Emu
• End page: Stanford University
• Expected return: Emu, Savannah, United States, University of California, Berkeley, Stanford
University
• Notes: This ran in about 30 seconds on my computer.
You don’t need to match this ladder exactly but your code should run in roughly the same
amount of time as times specified. If not, chances are your priority queue is not working
correctly, you did not implement memoization correctly, your scrapeHTML function is ineffi-
cient, or you have another inefficiency somewhere in your program.
Hints
• Make sure you understand the pseudocode or you will have a difficult time testing and
fixing issues in your code. It is impossible to debug code you do not understand.
• You should NOT edit the main, fetchHTML, getURL functions. If you do edit main()
for testing, be sure to revert it back to its original state. Keep in mind you can create an
entirely seperate file to test and call WikiRacer.findWikiLadder(...).
• Start early!
• Test the various pieces of your program very well. If you do not ensure that each piece
works perfectly, you will find very difficult to detect issues with the whole. Use the
debugger and/or various print statements.
Grading Criteria
We are not providing testcases for this PA. It is impossible since we are allowing different
ladders.
We encourage you to write your own JUnit testcases to ensure your classes work properly,
but we will not be collecting or grading these test files. We will test your classes with our own
testcases.
Your grade will consist of similar style and code clarity points we have looked for in earlier
assignments.
Write your own code. We will be using a tool that finds overly similar code. Do not look at
other students’ code. Do not let other students look at your code or talk in detail about how
to solve this programming project. Do not use online resources that have solved the same or
similar problems. It is okay to look up, ”How do I do X in Java”, where X is indexing into an
array, splitting a string, or something like that. It is not okay to look up, ”How do I solve {a
programming assignment from CSc210}” and copy someone else’s hard work in coming up
with a working algorithm.

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