Description
Question 0 (8 points):
Purpose: To force the use of Version Control in Assignment 6
Degree of Diculty: Easy
You are expected to practice using Version Control for Assignment 6. This is a tool that you need to be
required to use, even when you don’t need it, so that when you do need it, you are already familiar with it.
Do the following steps.
1. Create a new PyCharm project for Assignment 6.
2. Use Enable Version Control Integration… to initialize Git for your project.
3. Download the Python and text les provided for you with the Assignment, and add them to your
project.
4. Before you do any coding or start any other questions, make an initial commit.
5. As you work on each question, use Version Control frequently at various times when you have implemented an initial design, xed a bug, completed a question, or want to try something dierent. Make
your most professional attempt to use the software appropriately.
6. When you are nished your assignment, open the terminal in your Assignment 6 project folder, and
enter the command: git –no-pager log (double dash before the word ’no’). The easiest way to do
this is to use PyCharm, locate PyCharm’s Terminal panel at the bottom of the PyCharm window, and
type your command-line work there.
Note: You might have trouble with this if you are using Windows. Hopefully you are using the department’s network lesystem to store your les. If so, you can log into a non-Windows computer (Linux or
Mac) and do this. Just open a command-line, cd to your A6 folder, and run git –no-pager log there.
If you did all your work in this folder, git will be able to see it even if you did your work on Windows.
Git’s information is out of sight, but in your folder.
Note: If you are working at home onWindows, Google for how to make git available on your commandline window. You basically have to tell the command-line app where the git app is.
You may need to work in the lab for this; Git is installed there.
What to Hand In
After completing and submitting your work for Question 1, open a command-line window in your Assignment 6 project folder. Run the following command in the terminal: git –no-pager log (double dash before the word ’no’). Git will output the full contents of your interactions with Git in the console. Copy/paste
this into a text le named a6-git.log, or a6-git.txt
If you are working on several dierent computers, you may copy/paste output from all of them, and submit
them as a single le. It’s not the way to use git, but it is the way students work on assignments.
Be sure to include your name, NSID, student number, course number and laboratory section at the top of
all documents.
Evaluation
• 8 marks: The log le shows that you used Git as part of your work for Assignment 6. For full marks,
your log le contains
– Meaningful commit messages.
– At least two commits per function for a total of at least 24 commits.
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Question 1 (42 points):
Purpose: To build programming skills by implementing the linked list ADT. To learn to implement an ADT
according to a description. To learn to use testing eectively. To master the concept of reference. To
master the concept of node-chains. To gain experience thinking about dierent problem cases.
Degree of Diculty: Moderate There are a few tricky bits here, but the major diculty is the length of the
assignment. Do not wait to get started! This question will take 10-12 hours to complete.
1. On Moodle you will nd four Python scripts:
• node.py
• LList.py
• llist_unit_test.py
• llist_integration_test.py
Download them and add them to your project for Assignment 6 (and to Git). See below for a section
on how to use the test scripts.
2. It is your task to implement all the operations in the LList.py script. Currently, the operations are
stubs, meaning that the functions are dened but do nothing useful yet. They return trivial values, and
you’ll have to modify them.
3. The Linked List operations are described in the course readings, in lecture, and below.
The linked list ADT
The Linked List ADT is very much like the node-based Queue ADT we studied in class. The create()
operation is as follows:
✞ ☎
def create ():
“””
Purpose
creates an empty list
Return
: return an empty list
“””
llist = {}
llist [’size ’] = 0 # how many elements in the list
llist [’head ’] = None # the node chain starts here ; initially empty
llist [’tail ’] = None # the last node in the chain
return llist
✝ ✆
A linked list is a dictionary with the following keys:
size This keeps track of how many values are in the list.
head This is a reference to the rst node in the node chain. An empty Linked List has no node chain, which
we represent with None.
tail This is a reference to the last node in the chain. If the list is empty, this is None.
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The Linked List ADT operations
When you open the LList.py document, you will nd a complete description of all the operations you
have to implement. Here is a brief list of them, with a few hints:
create() Creates an empty Linked List data structure. This is already complete!
is_empty(alist) Checks if the given Linked List alist has no data in it.
Hint: Stack and Queue have this one.
size(alist) Returns the number of data values in the given Linked List alist.
Hint: Stack and Queue have this one.
add_to_front(alist, val) Adds the data value val into the Linked List alist at the front.
Hint: This is similar to Stack’s push().
add_to_back(alist, val) Adds the data value val into the Linked List alist at the end.
Hint: This is similar to Queue’s enqueue.
value_is_in(alist, val) Check if the given value val is in the given Linked List alist.
Hint: Just walk along the chain starting from the head of the chain until you nd it, or reach the end
of the chain.
get_index_of_value(alist, val) Return the index of the given val in the given Linked List alist.
Hint: Like value_is_in(alist, val), but if you nd it, return the count of how many steps you took
in the chain.
retrieve_data_at_index(alist, idx) Return the value stored in Linked List alist at the index idx. Walk along
the chain, counting the nodes, and return the data stored at idx steps.
Hint: Start counting at index zero, of course!
set_data_at_index(alist, idx, val) Store val into Linked List alist at the index idx.
Hint: Walk along the chain, counting the nodes, and store the new value as data at the node idx steps
in. Start counting at index zero, of course!
remove_from_front(alist) Removes and returns the rst value in Linked List alist.
Hint: This is similar to Queue’s dequeue.
remove_from_back(alist) Removes and returns the last value in Linked List alist. This is not similar to
the Queue or Stack operations!
Hints: Break the problem into 3 parts, and get each part working and tested before you go on to the
next part.
• First, deal with trying to remove from an empty list.
• Second, deal with removing the last value in a list if size is exactly 1, and no bigger.
• Third, deal with the general case. The last value is easy to obtain, but the node in front of it has to
become the new end of the chain. You have to walk down the chain to do that! Draw a diagram,
and convince yourself that you have to walk along the chain to the end of it to do this properly.
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insert_value_at_index(alist, val, idx) Insert the data value val into Linked List alist at index idx. The new
value is in the chain at idx steps from the front of the chain. The node that used to be at idx comes
after the new node.
Hints: Break the problem into parts.
• Deal with cases where idx has an invalid value: too big, or too small.
• You can call add_to_front() if idx is 0. There is a similar use for add_to_back().
• Deal with the general case. You have to walk down the chain until you nd the correct place, and
connect the new node into the chain. Draw a diagram, and convince yourself that you have to
walk along the chain to the end of it to do this properly.
Note: If the given idx is the same as the size of the Linked List, at the value to the end of the list. This
is most easily done using add_to_back().
delete_item_at_index(alist, idx) Delete the value at index idx in Linked List alist. Here you unhook the
node from the chain.
Hints: Break the problem into 3 parts, and get each part working and tested before you go on to the
next part.
• Deal with cases where idx has an invalid value: too big, or too small.
• You can call remove_from_front() if idx is 0. There is a similar use for remove_from_back().
• Deal with the general case. You have to walk down the chain until you nd the correct place, and
disconnect the appropriate node from the chain. Draw a diagram!
Because of the similarity between the Linked List ADT and the node-based Queue and Stack ADTs, some
of the operations will be similar, if not exactly the same. You may borrow from Queue and Stack (the nodebased implementations), being careful to realize that copy/paste may only be the start of your work, not
the end!
How to FAIL to complete this question
Write all your code all at once, without any testing as you go. Then run the test scripts to nd that you’ve
got a lot of errors to x. Good luck!
How to complete this question
Write one function at a time, starting with a simple node-chain version (like A5). Test your node-chain
version, and when you are sure it works, adapt it for the linked list operation. Then run the unit test script,
with a limit on the number of failures reported. Debug each operation one at a time, then when the unit
test script reports no errors in the operation, move on to the next.
Testing using the given test scripts
We’ve provided two test scripts, one for unit testing, and one for integration testing. They are fairly well
documented, so you can open them up and have a look. These scripts consist of denitions of simple
Python functions, all of which have the same basic task:
1. First, create a context for the test. This usually involves creating a LList record somehow. As we’re
doing unit testing on the LList ADT, we sometimes create node and LList records without using their
create() methods. This is valid in a test script, but not in an application; we can violate the ADT
Principle in testing the ADT.
2. Second, check how one other operation works in the given context. In our scripts, we use assertions,
which check for something that should be true, and which alert us when that thing is not true.
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For example, here’s a unit test:
✞ ☎
def test_create_key_size ():
# each test function sets up an independent context for the test
# here , we use the ADT to create a LList
allist = List . create ()
# then the test uses assert , giving the test condition and the reason
# here we check that the record returned has one of the necessary fields
assert ’size ’ in allist , ” create (): new LList record ; ’size ’ MISSING !”
✝ ✆
It’s a simple test of the create() operation. A list is created, and then an assertion is used to check if the
key ’size’ is one of the keys in the LList record (dictionary). Notice how the assertion replaces the familiar
if-statement that we would expect to see in the testing we have done so far. An assertion like this only
reports a problem if the condition is false, and the string indicates the reason for the test (check the key)
and the problem (key is missing). This particular test will pass using the script LList.py that we’ve given
you; it may fail if you tinker with the create() operation.
Here’s a test from the integration test script:
✞ ☎
def test_integration_add_to_back_is_empty ():
# an integration test tests how operations work together
# first set up a list with a bunch of nodes in the node chain
thellist = List . create ()
stuff = ’HEY STOPSIGN THANK -YOU TURN – AROUND DOING – DOING HORSESHOE TURTLE ’. split ()
for word in stuff :
List . add_to_back ( thellist , word )
# now check if a single aspect worked properly
result = List . is_empty ( thellist )
assert result is False , ” checking is_empty () after add_to_back (); returned True !”
✝ ✆
This test creates a list, adds a bunch of strings to the list, then checks is_empty().
The test scripts have no code that calls any of the functions, so if you run the script as a normal Python
program, no testing is done. These scripts are intended to be conducted using a popular Python library
called pytest, which is installed on all computer science systems, and should be part of the default installation when you installed Anaconda. It’s primarily a command-line tool, meaning that we can run tests
from the command line. The pytest application works as follows:
1. Running pytest on the command-line with no command-line arguments:
✞ ☎
UNIX$ pytest
✝ ✆
• reads all les in the current folder with the word test in the name
• calls any function with the word test in the function name
• watches for assertions, counts the ones that pass and the ones that fail
• each failed assertion generates a full report, which displays the entire function, and the location
of the error. See below for hints about how to manage the output.
• unlike normal Python, pytest records an assertion failure, but does not stop
• ends with a summary, e.g,
== 51 failed, 28 passed in 0.90 seconds ==
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2. Running pytest with a script named on the command-line ✞ ☎
UNIX$ pytest llist_unit_test . py
✝ ✆
• reads only the named le, then does the above only for this le
• you can put as many lenames as you like, but they should be test scripts.
3. Running pytest, using command-line arguments to limit the output: ✞ ☎
UNIX$ pytest — tb = no llist_unit_test . py
✝ ✆
• this commandline argument NEEDS 2 dashes! It won’t work if you use just one
• eliminates almost all of the reporting, just reports progress, and nal result
• runs all test scripts if no script given on command line
4. Running pytest to limit the testing: ✞ ☎
UNIX$ pytest — maxfail =10 llist_unit_test . py
✝ ✆
• this commandline argument NEEDS 2 dashes! It won’t work if you use just one
• keeps working until 10 test failures are accumulated, then stops
• saves you time when you only have completed some of the operations
• you can change the value to anything you
• when maxfail=1, you just get the rst failure.
5. You can use pytest in PyCharm, too. Just do the following:
• Open the Settings/Preferences | Tools | Python Integrated Tools settings dialog
• In the Default test runner eld select “pytest” It may appear as “py.test”
• Click OK to save the settings.
Now you can choose to run this script using pytest from PyCharm’s Run menu! PyCharm will organize
the test results in a new interface panel at the bottom of the window. You can click on any of the tests,
and see the report for that test. PyCharm’s interface allows you to choose to hide or show tests that
pass, and you can even choose to let PyCharm run our tests automatically as you type.
When you run the unit test script on the le as given on Moodle, you should see that 54 of the tests pass
(and about 100 tests fail) before you have added any code to LList.py. That’s because the function stubs
we’ve written give the correct answer for those 54 test cases. Likewise, 17 of the 79 integration tests should
pass before you’ve added any code. The tests that are passing by default may fail if you add code that
does not work properly.
The pytest application generates a lot of output, and if you run it on the command-line you’ll probably only
see the end of the output. Limiting the output while you work through unit testing is certainly a good idea.
You may write your own test scripts (using the simple format we’ve used before, or you can try your hand
at pytest). Feel free to use A5Q1 (to_string()) if you need visual assistance (we advise you don’t display a
LList without any formatting; it probably won’t be too helpful to you). When you think you’ve got a working
operation completed, run the unit test script. When you think you have all the operations working and
passing the unit tests, run the integration test script.
You will know you are done when you see something like this: ✞ ☎
UNIX$ pytest — tb = no
…
=== 233 passed in 0.41 seconds ===
✝ ✆
Be careful! It’s fairly easy to write a loop that never terminates, and you might think pytest stopped early,
when it’s stuck half way through. If you don’t see a nal report, pytest is still running.
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What to Hand In
Hand in your LList.py program, named LList.py. It should contain only the Linked List ADT operations,
and nothing else (no test code, no extra functions).
Do not submit llist_unit_test.py, llist_integration_test.py or node.py.
Be sure to include your name, NSID, student number, course number and laboratory section at the top.
Note: If you submit a le that is not named LList.py exactly, you may receive zero marks. Our scoring
script will use import LList as List, and if your submission is named something else, the import will fail
and we will not try to gure why.
Evaluation
• Your solution to this question must be named LList.py, or you will receive a zero on this question.
• 12 marks: You completed an implementation for all 12 operations. One mark per operation. The implementation does not need to be correct, but it should be relevant, and it should be code written
with an eort to demonstrate good style and internal documentation. The mark will be deducted if
there is no implementation, or if the function does not demonstrate good programming style, or if
your function violates the Node ADT.
• 30 marks: When our scoring script runs using your implementation of LList.py, no errors are reported.
Partial credit will be allocated as follows:
Number of tests passed Marks Comment
0-71 0 the given LList script already passes 71 tests total
72-100 5
101-125 10 using the hints about Stack and Queue gets you here
126-150 15
151-175 20 passing all unit tests gets you here at least
176-200 24
201-232 27 this is actually pretty good
233 30
For example, if your implementation passes 209 tests, you’ll get 27/30. If your implementation passes
156 tests, you’ll get 20/30. Our test script is based on the test scripts distributed with the assignment,
but may not be exactly the same.
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