COMP2402 W24Lab3Specifications-优快云博客

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COMP2402 W24Lab3Specifications
(Some) SSet Implementations
Prelab: due on brightspace by Friday February 16th, 3:00pm (no lates)
Programming: due on gradescope by Wednesday February 28th, 3:00pm (24h late ok)
Postlab: due on brightspace.ca by Wednesday March 6th, 3:00pm (no lates)
Topic focus: Lec 1- 11
Feedback on Lab 1 Feedback
Hi all, thank you to the 83 (!!) of you that took the time to provide feedback after Lab 1; I know
you are all very busy! I am reading over your feedback carefully, and making some adjustments
to the course in response. You can read more about your feedback and the changes I will and
not make (and why) here.
Changes from Lab 1 & Lab 2
The specifications are still highly structured in the same way as Labs 1 and 2, but as with Lab 2,
a lot of the non-programming details are just links to previous documentation. This is so that you
don’t feel you have to read such a long document unless you need the resources (in which
case, you can follow the links to them.)
I have added a section at the very end: Autograder Runtimes for Sample Solutions, which
provides screenshots of the autograder as it ran on sample solutions. This is to give you a
sense of some runtimes you might strive for. For what it’s worth, I ran this tests during Lab 2
crunch times.
Any randomization in the autograder is now “fixed,” as in, the input was randomly generated
initially by me, but then fixed for the autograder. As such, everyone is being tested on exactly
the same input every single time. Any variability in the autograder can now definitely be
attributed to load/gradescope rather than variability in the tests (which I suspect was negligible
anyways, but just to be safe.)
There are still no hidden tests. I will wait to see if the variability of the autograder is resolved
before I go hiding any tests. In some sense, the variability of the autograder is serving as a
“hidden test” in the sense that you have to sometimes look at your code and determine its
time/space complexity and decide whether it matches the desired complexity rather than relying
on the occasionally flaky tests.
The GeneSet autograder has more structured feedback; more info in Geneset.
Lab Objectives
The labs in this course are meant to give you the opportunity to practice with the topics of this
course in a way that is challenging yet also manageable. At times you may struggle and at
others it may seem more straight-forward; keep trying and practicing and you will improve.
Specifically, Lab 3 aims to improve your

Implementation Skills
Implement operations (add, remove, find, etc.) for linked-list-based data structures
similar to the SkiplistList and SkiplistSet (and, SLList and DLList, to some extent.)
Design Skills
Use object-oriented programming concepts such as abstraction and polymorphism to
provide flexibility in your choices of which interface implementation to use.
Critical Thinking
Demonstrate a solid understanding of the pros and cons of various implementations of
the list (array-based, linked-list-based, and SkiplistList-based) and of the situations when
a sorted set might be a better choice than a list. This includes considering the time- and
space-complexity of various operations in different data structures.
Algorithmic Thinking
Apply algorithmic thinking to design efficient algorithms for common linked-list-based
operations, such as findPredNode(i), iteration over nodes using pointers and integers,
careful pointer manipulation, and using nested structures to store and access data.
Error Handling
Implement appropriate error handling and boundary checks to ensure the robustness of
the data structures.
Testing
Determine the necessary tests to ensure your algorithms are correct and efficient.
Planning
Maintain or adopt good academic and programming habits through the pre-lab.
Reflection
Reflect on your choice of data structures and algorithms through the post-lab.
Assignment Components
Details of each component follow later in the specifications.
(6.7 points) Prelab (complete on brightspace)
(80 points) Programming portion (submit on gradescope.ca)
a. (40 points) GeneSet Implementation
b. (10 points) JumbleC implementation
c. (10 points) Genes Implementation
d. (10 points) GenesOrder implementation
e. (10 points) Mutations implementation
(13.3 points) Postlab (complete on brightspace)
Grading Criteria & Submission Guidelines
See the Grading Criteria and Submission Guidelines of Lab 1; they are the same.
Collaboration & Academic Integrity
Individual work is expected. Any collaboration should be explicitly mentioned and
acknowledged at the top of each file. It is okay to discuss high-level approaches with
your peers, or low-level syntax-type questions, but you must construct your solution
on your own (as in: you have to formulate the code of your solution on your own.)
Consider the analogy of writing an essay. You might talk with a peer about the high-level
concepts of your thesis, or you might ask them about grammar or even phrasing of
individual sentences. But you should not be writing the essay sentence-by-sentence with
someone else’s help; in the end you have to sit down and write that thing on your own.
Plagiarism will result in severe consequences. Ensure that all code and
documentation are your own work. Do not send code to or receive code from any
source except for course staff or the textbook, even if you change a thing here or there.
It helps to keep the analogy of an essay in mind; it is not okay to take a paragraph from
a friend and then rearrange some of the words or replace some with a thesaurus. It’s not
even okay to paraphrase each sentence. It is not okay to send your essay to a peer.
Similarly here, you cannot start with code that is not yours and then “make it your own”
with minor edits. Automated tools for detecting plagiarism will be employed in this
course.
The same restrictions apply to AI programmers (such as chatGPT, copilot). You can
use them to help with basic syntax (e.g. “spelling” and “grammar”) or to understand
broad concepts (e.g. getting feedback on a thesis) but you have to formulate your
solution in code on your own (e.g. you have to write that essay yourself.)
Note that contract cheating sites are known, unauthorized, and regularly monitored.
Some of these services employ misleading advertising practices and have a high risk of
blackmail and extortion.
Every student should be familiar with the Carleton University student academic integrity
policy. Academic integrity is upheld in this course to the best of Prof Alexa’s abilities, as
it protects the students that put in the effort to work on coursework within the allowable
parameters. Potential violations must be reported to the Dean of Academic Integrity. If
you ever have questions about what is or is not allowable regarding academic integrity,
please do not hesitate to reach out to course staff. We are happy to answer.
Copyright
Prof Alexa is the exclusive owner of copyright and intellectual property of all course materials,
including the labs. You may use course materials for your own educational use. You may not
reproduce or distribute course materials publicly for commercial purposes, or allow others
to, without express written consent.
Workflow
In a perfect world, this is how you would complete Lab 3:
Attend or watch the relevant lectures that are listed in the heading of this document.
Read the lab objectives listed on the second page of this document. For each data
structure listed there, review its important algorithms as well as the time- and space
complexity of its methods. This should give you some pros and cons for each.
Carefully read each problem detailed in the Programming section of this document.
a. Make sure you understand the problem.
b. Try the given examples by hand to get a better understanding of the problem.
c. Pay special attention to any special cases or edge cases.
d. Try more examples of your own devising if you need them.
e. Donot start programming yet!
f.
Consider attending or watching the lab’s workshop video, posted on brightspace.
Once you have completed steps 1-3, you are ready to do the prelab (brightspace).
Complete the programming portion of the lab.
a. Take this one problem at a time. Any order should be okay.
b. Remember what you learned in Steps 1-4 as you brainstorm solutions.
c. Test locally at frequent intervals. Do not write a whole program then test it
afterwards. See the section on Local Tests to help you here.
d. Submit to gradescope.ca whenever you have made good progress, but do not
use gradescope.ca as your only tests. Gradescope keeps your most recent score
unless you select a different submission to be active. See the section on the
Gradescope Autograder to help you debug here.
e. If you’re stuck on a problem for more than 30 minutes, ask for help using the How
to Get Help section. Move on to something else until help has arrived.
Once the late programming deadline has passed, complete the post-lab (brightspace).
a. If you did well on the programming portion, this is not meant to take too long.
b. There are resources available to help you posted on brightspace under the Lab 2
module. There is a solutions walk-through video for each part, and also a debrief
document where I walk through the problem solving process and learning
engagement I was hoping you would experience. You might consider looking at
these before completing the prelab if you had trouble with any of the
programming parts.
c. You do not have to have completed the programming parts in order to do the
postlab. If you were stuck on a problem, this is an opportunity to look at the
sample solution videos, to figure out what went wrong for you, and to still learn
what you were meant to learn.
Coding Environment Setup
Lab 1’s Coding Environment Setup will work with Lab 3 (replace Lab1/l1 with Lab3/l3). The
file structure and many of the files will be the same, with new and different files as well.
Programming Components
Programming Notes
String compareTo might be helpful to compare strings lexicographically (alphabetically).
Comparing elements can take more time for certain types of elements than others.
Comparing two integers or doubles is basically O(1) time, as is comparing two
Characters. But comparing two Strings requires potentially looping through both strings
from front to back, which takes time linear in the length of the String. While Java has
optimized String comparison as much as possible, there is only so much it can do if two
strings of length k are exactly the same except for one character (say, the last one.)
Recall from Lab 2 that repeated String appends (i.e. the + operator) is costly and can
often be avoided by using StringBuffer and its append method.
Note that the ods SSet interface has more than just the find operation, but also findLT
and findGT that may be of use in this lab. Also you might take a look at its iterator.
AbstractList is the abstract java List interface. All of our textbook (ods) List
implementations implement this interface (either implementing it explicitly, as the
ArrayDeque does, or by implementing a subclass as the DLList does.) This allows us to
choose different implementations of the same interface; we know there is a guarantee
that we can call the methods and they will have the promised behaviour, but we do not
need to know the inner workings of the implementation (or, which implementation you
choose to use.) This is polymorphism and abstraction at work.
In order to make a new array of type Node in java, you need to do something like this:
next = (Node [])Array.newInstance(Node.class, h+1);
Anytime you use this you will need to add @SuppressWarnings(“unchecked”)
prior to the method or inner class that creates the array. You can see examples of this
scattered throughout the GeneSet class, should you need to do it yourself.
Implementation Practice [40 marks]
GeneSet [40 marks]
GeneSet represents a set of Strings (where a String can ostensibly represent a gene; no
biology knowledge necessary for the lab). Your task is to implement the following methods
according to the specifications below, using the starter code and lecture to guide you. The
provided implementations are incomplete and incorrect, but should be enough to compile and
run the (failing) local and autograder tests.
The data structure you use is a greatly simplified version of a SkiplistSSet, with randomization
removed. Instead of determining the heights of elements via “coin flips,” we will determine the
heights of elements based on the pattern of search paths we construct as the SSet gets used.
As with a SkiplistSSet, a GeneSet is a dummy Node that links to a singly-linked list of
Nodes. Each node u has its data (String s) and an array next of u.height+1 pointers to its
next nodes at levels 0, 1, 2, …, u.height. That is, u.next[r] represents u’s “shortcut”
at level-r, as with the SkiplistSSet. For example, a loop that would iterate over just the level
r shortcuts would look something like this:
u = dummy;
while( u.next[r] != null )
u = u.next[r]
As with a SkiplistSSet, not every node will have shortcuts at every level; a node’s height
represents the highest level r for which it has shortcuts; in other words, it is
u.next.length-1 (since heights are 0-based.)
Generally, we want our levels and heights to satisfy the four properties below:
all elements are in level 0 of our skiplist, in sorted order;
the elements at level r are all contained at level r-1 (but probably not vice-versa);
the number of elements at level r is about half the number of elements at level r-1; and
when searching for an element x, we take at most ~1 shortcut “right” per level before
dropping down to the next lower level.
In lecture, we saw the first saw the “fixed/deterministic” skiplist that allowed us to maintain these
properties, but not without some restructuring cost on an add/remove. We then saw the
SkiplistSSet that used randomization to get properties 3 and 4 in expectation (and maintained
properties 1 and 2 always.)
In this lab, we will also aim to always maintain properties 1 and 2, and we will try to maintain 3
and 4 in a “just-in-time” basis. In particular, we will focus on property 4: if, in an add, remove, or
find operation, we take two shortcuts at some level r (in “violation” of property 4), we will
“just-in-time” add in a shortcut to level r+1 that jumps over the two shortcuts we just took. The
idea is that this new shortcut can’t be used on the current operation, but it can be used on future
operations, and hopefully the cost of adding in the new shortcut can be amortized over the adds
and removes that made the shortcut necessary.
This is the general idea. Let’s get to some details.
Inner Class
Node
String
Node[]
int
Fields
● Node dummy
● int n
● int h
(Represents a Node in our set.)
s
next
The non-empty String in this Node.
The Nodes to which this Node has shortcuts.
height() Returns the height of this Node (it’s next.length-1)
The dummy Node for the set (also known as a sentinel).
The number of elements in the set.
The maximum height of any element (Node)in the set.
Implemented Methods & Constructor
● GeneSet()
Initializes the fields so that this set is empty (just a dummy Node of length 32).
Note that the length of the dummy Node is not the same as the height of the GeneSet.
● public int size()
Returns the number of elements in the set.
See the main method for examples of how to use size.
● public void clear()
Clears the set so that it contains no elements.
See the main method for examples of how to use clear.
● public String toString()
Returns a String representation of the set that is helpful for debugging purposes.
See the main method for examples of how to use the toString method.
● protected Node findPredNode(String s)
Returns the Node that precedes element s in the skiplist. This is for use in the iterator,
so please do not modify it.
● public Iterator iterator()
Returns an Iterator that iterates over the elements (Strings) of the set.
See the main method for examples of how to use the iterator.
● public int[] getHeights()
Returns an array of length n+1 with the heights of the n+1 nodes (including the dummy
node.) This is for testing purposes, so please do not modify it.
See the main method for examples of how to use the getHeights method.
● public Node getNode(int i)
Returns the ith element of our set stored in level 0 of the skiplist. This is for testing
purposes, so please do not modify it.
See the main method for examples of how to use the getNode method.
Methods For You to Implement
Note: For all of these methods, I recommend getting them to work with just level 0 and level 1.
The autograder has tests for solutions working with just those levels. Get the kinks out for levels
0 and 1 before proceeding to more levels. There are even some tests with just levels 0, 1, and
2, so that is another next step to take after getting levels 0 and 1 working.
● public void resize(Node u, int newHeight)
Resizes the u.next array to have length newHeight, if newHeight>u.height is
greater than its current length. Does nothing if newHeight≤u.height.
Update the height of the GeneSet (h) if necessary.
This would typically be a private method, but it is public so that the autograder can test it.
If you’ve implemented resize, it is a very good idea to test this locally and on the autograder
before proceeding. Bugs in resize will be hard to track down later.
The add, remove and find methods will all share similar logic that finds the predecessor node
of input s; usually I’d recommend working on a findPredNode method whose logic you can
reuse in add, remove and find, we would not be able to test such a method until we have an
add method that allows us to construct a set in the first place! As such, I recommend you work
on add first, but then remember that the logic of add may be easily transferred to the other
methods.
● public boolean add(String s) throws IllegalArgumentException
Adds the element s to the set, if it is not already there.
Throws an IllegalArgumentException if s is the empty string or null.
Returns true if the element is newly added; false if it was already in the set.
At any point if you traverse two existing level-r shortcuts (i.e. next[r] pointers), add a
shortcut over those two edges at level (r+1). This may require resizing various nodes.
If you’ve implemented add(s), it is a very good idea to test this locally and on the autograder
before proceeding. If your add isn’t working, none of the other methods are likely to work.
● public String find(String s)throws IllegalArgumentException
Returns the smallest element in the set that is ≥ s (in alphabetical order); returns null
if no such element exists (i.e. if s > all elements of the set.)
Throws an IllegalArgumentException if s is the empty string or null.
At any point if you traverse two existing level-r shortcuts (i.e. next[r] pointers), add a
shortcut over those two edges at level (r+1). This may require resizing various nodes.
● public boolean remove(String s)throws IllegalArgumentException
Removes s from the set; returns true if s was removed, false if s was not in the set.
Throws an IllegalArgumentException if s is the empty string or null.
At any point if you traverse two existing level-r shortcuts (i.e. next[r] pointers), add a
shortcut over those two edges at level (r+1). This may require resizing various nodes.
If you’ve implemented find and remove it is a very good idea to test this locally and on the
autograder before proceeding.
● public boolean equals(Object o)
Returns whether this GeneSet is equal to o’s GeneSet.
Returns false if o is not an instance of GeneSet.
This should not only check whether the sets contain the same elements, but also
whether they contain Nodes of the same heights, and the same shortcuts.
Desired Complexity
In the following table, let
● ndenotethe number of elements in our set;
● hdenotethe maximum height of any node.
marks
method
time complexity
(extra) space
complexity
8
8
8
resize(u,t)
add(s)
find(s)
O(t)
O(h+n/2h)A
[O(nh) occasionally, not often]
O(h+n/2h)A
[O(nh) occasionally, not often]
O(t)
O(n)
[occasionally]
O(n)
[occasionally]
8 remove(s) O(h+n/2h)A
[O(nh) occasionally, not often]
O(n)
[occasionally]
8 equals(o) O(n) O(1)
Examples(admiremyasciiart,please)
Resize
Node u u.height (pre) method t=u.height(post) Node u (post)
_
_ ||->
|s|||->
1 resize(u,1) 1 _
_ ||->
|s|||->
_
_ ||->
|s|||->
1 resize(u,0) 1 _
_ ||->
|s|||->
_
_ ||->
|s|||->
1 resize(u,2) 2 _
||->
_ ||->
|s||_|->
Add
GeneSet method returns GeneSet (post)
L0: | add(A) true L0: |-A
L0: |-A add(A) false L0: |-A
L0: |-A add© true L0: |-A-C
L0: |-A-C add(E) true L1: |—C-
L0: |-A-C-E
L1: |—C-
L0: |-A-C-E
add(G) true L1: |—C—
L0: |-A-C-E-G
L1: |—C-----
L0: |-A-C-E-G-I
add(I) false L1: |—C—G-
L0: |-A-C-E-G-I
L1: |—C—G-
L0: |-A-C-E-G-I
add(K) true L2: |-------G—
L1: |—C—G----
L0: |-A-C-E-G-I-K
L0: |-K add(I) true L0: |-I-K
L0: |-I-K add(G) true L0: |-G-I-K
L0: |-A-C-E-G-I-K add(L) true L1: |—C—G—K-
L0: |-A-C-E-G-I-K-L
L2: |-------K
L1: |-------K
L0: |-C-E-I-K
add(K) false L2: |-------K
L1: |-------K
L0: |-C-E-I-K
L2: |-------K
L1: |-------K
L0: |-C-E-I-K
add(L) true L2: |-------K
L1: |-------K
L0: |-C-E-I-K-L
L0: |-A-C-E-G-I-K add(“”) IllegalArgumentException
Find
GeneSet method returns GeneSet (post)
L0: | find(A) null L0: |
L0: |-A find(A) A L0: |-A
L0: |-A-C find(B) C L0: |-A-C
L0: |-A-C-E-G-I-K find© C L1: |—C-------
L0: |-A-C-E-G-I-K
L1: |—C-------
L0: |-A-C-E-G-I-K
find(L) null L1: |—C—G—K
L0: |-A-C-E-G-I-K
L1: |—C—G—K
L0: |-A-C-E-G-I-K
find(L) null L2: |-------G—
L1: |—C—G—K
L0: |-A-C-E-G-I-K
L0: |-A-C-E-G-I-K find(“”) IllegalArgumentException
Remove
GeneSet method returns GeneSet (post)
L0: | remove(A) false L0: |
L0: |-A remove(A) true L0: |
L0: |-A-C-E-G-I-K remove(B) false L0: |-A-C-E-G-I-K
L0: |-A-C-E-G-I-K remove(A) true L0: |-C-E-G-I-K
L0: |-C-E-G-I-K remove(E) true L0: |-C-E-G-I-K
L0: |-C-E-G-I-K remove(G) true L1: |—E—
L0: |-C-E-I-K
L1: |—E—
L0: |-C-E-I-K
remove(L) false L1: |—E—K
L0: |-C-E-I-K
L1: |—E—K
L0: |-C-E-I-K
remove(L) false L2: |-------K
L1: |—E—K
L0: |-C-E-I-K
L2: |-------K
L1: |—E—K
L0: |-C-E-I-K
remove(K) true L1: |—E-
L0: |-C-E-I
L2: |-------K
L1: |-------K
L0: |-C-E-I-K
remove(K) true L1: |—E-
L0: |-C-E-I
L0: |-C-E-I-K remove(“”) IllegalArgumentException
Equals
GeneSet this GeneSet o method returns
L0: | null this.equals(o) false
L0: | “ABCD” this.equals(o) false
L0: | L0: | this.equals(o) true
L0: |-A L0: | this.equals(o) false
L0: |-A L0: |-B this.equals(o) false
L0: |-A-C L1: |—C
L0: |-A-C
this.equals(o) false
Interface Practice [40 marks]
The 4 problems in this section ask you to choose the best data structures (interfaces), if any, to
solve each problem correctly and efficiently (with regards to time and space). As with Labs 1
and 2, the characters generated contain any combination and number of the four characters A,
U, C, G. Ensure your implementation is efficient and handles different scenarios gracefully.
JumbleC (extends Jumble) (a.k.a. Jumble Returns)
Method Signature
public static String jumble(InputGenerator gen)
Method Behaviour
Returns a jumbled version of the input sequence generated by InputGenerator
gen after it has been jumbled as follows:
Initially the “cursor” is at index 0.
Ascharacters are generated, add them at the cursor.
𝑛
2
characters generated thus far, or to 0 if this would move the cursor left of 0.
If an AUC is generated, “rewind” the cursor ⌊ ⌋ positions, where n is the number of
If an AUG is generated, “fast forward” the cursor ⌊ ⌋ positions, where n is the number of
𝑛
4
characters generated thus far, or to n if this would move the cursor right of n.
Desired Complexity & Constraints
O(n log n)A time, where n is the number of characters generated.
O(n log n)space.
Examples
characters generated by gen
output
n
d
UUUAUCG UUUGAUC 7 1
UUAUCGGGAUGUU UUAGGGAUGUCUU 13 2
UAUCAUCAUCAUC
AUCUAAUCAUCUC
(no characters) “”
Testing & Autograder
13
0
4
0
There are limited local tests in JumbleC.main and tests/JumbleCTest.java; see Lab 1 for
testing instructions. Submit JumbleC.java to gradescope; see Lab 1 for submission
instructions.
Genes
Method Signature
public static int genes(InputGenerator gen, int k)
Method Behaviour
Returns the number of different genes (substrands) of length k > 0 in the sequence generated
by a given InputGenerator.
Desired Complexity & Notes
O(k2n)A time, where n is the number of characters generated.
O(kd) space, where k is the integer input parameter, and d is the solution.
Since there are 4 bases, d ≤ 4k and thus log d = O(k).
Even within these bounds, do your best to minimize wasteful time or space operation in
order to pass all the performance tests.
Examples
characters generated by gen
AAAA
k
3
output (d)
1
n
4
AAAAU
UGCGAAUAUA
UUUUCCCCAAAA
UUUUCCCCAAA
Testing & Autograder
3
3
12
12
(no characters)

0
2
7
1
0
0
5
10
12
11
0
There are limited local tests in Genes.main and tests/GenesTest.java; see Lab 1 for
testing instructions. Submit Genes.java to gradescope; see Lab 1 for submission instructions.
GenesOrder
Method Signature
public static AbstractList genesOrder(InputGenerator
gen, int k)
Method Behaviour & Notes
Returns a list of the different genes (substrands) of length k > 0 in the sequence generated by
a given InputGenerator, in the order they are first encountered.
An AbstractList is an abstract java interface that is implemented by all the ods List implementations
(e.g. ArrayStack, ArrayDeque, DLList, SkiplistList, etc). Using AbstractList as the return type
means you have the flexibility to choose which of these List implementations best suits your code
and the desired complexity requirements.
Desired Complexity & Notes
O(k2n)A time, where n is the number of characters generated.
O(kd) space, where k is the integer input parameter, and d is the solution.
Since there are 4 bases, d ≤ 4k and thus log d = O(k).
Even within these bounds, do your best to minimize wasteful time or space operation in
order to pass all the performance tests.
Examples
characters generated by gen
AAAA
k
3
output (d)
AAA
n
4
AUAAA
UGCGAAUAUA
UUUUCCCCAAAA
Testing & Autograder
3
3
12
(no characters)
0
AUA,UAA,AAA
5
UGC,GCG,CGA… 10
UUUUCCCCAAAA 12
0
0
There are limited local tests in GenesOrder.main and tests/GenesOrderTest.java; see
Lab 1 for testing instructions. Submit GenesOrder.java to gradescope; see Lab 1 for
submission instructions.
Mutations
Method Signature
public static int mutations(InputGenerator gen, int k)
Method Behaviour
Returns the number of different “closest mutations” computed over all substrands of length k >
0 in the sequence generated by a given InputGenerator.
Given a strand of length k, its “closest mutation” is the nearest already-existing strand of length
k just following it alphabetically, if it exists. For example
in list [AAA] the closest mutation to AAA is non-existent
in list [AAA,CAA] the closest mutation to AAA is CAA
in list [AAA,CAA,ACA] the closest mutation to AAA is ACA
Desired Complexity & Notes
O(nk2)A time, where n is the number of characters generated.
O(kd) space, where k is the integer input parameter, and d is the number of different
strands of length k (same as in Genes).
Since there are 4 bases, d ≤ 4k and thus log d = O(k).
Even within these bounds, do your best to minimize wasteful time or space operation in
order to pass all the performance tests.
Examples
characters generated by gen
AAA
k
3
output
0
n
3
AAAA
CAAA
CAAACAAA
ACGUACGUACGU
Testing & Autograder
3
3
3
3
(no characters)
0
0
1
2
3
0
4
4
8
12
0
There are limited local tests in Mutations.main and tests/MutationsTest.java; see Lab
1 for testing instructions. Submit Mutations.java to gradescope; see Lab 1 for submission
instructions.
Local Tests
See the Local Tests section of Lab 1; be sure to replace comp2402w24l1 with
comp2402w24l3!
Gradescope Autograder
See the Gradescope Autograder section of Lab 1; be sure to replace Lab 1 with Lab 3 where
necessary.
How to Get Help
See the How to Get Help section of Lab 1. It will be updated with any new resources rather than
duplicating information here.
Common Errors & Fixes
See the Common Errors & Fixes section of Lab 1. It will be updated with any new errors rather
than duplicating information here.
Glossary
There is a glossary at the top of the Problem Solving & Programming Tips document. If you
can’t find a term listed there, please post publicly on piazza so that everyone can benefit from
the answer!
Autograder Runtimes for Sample Solutions
To give you a sense for the autograder runtimes for “perfect scores” you can see some of the
screenshots that follow.
GeneSet: I’ve made some adjustments to the autograder since making the screenshots below,
by increasing the time bound on the correctness and space tests.
● all non-performance tests now have 3 seconds, which should be plenty of time to check
for correctness (this is up from 2 seconds)
● all space performance tests now have 6 seconds, which should be plenty of time to
check for space usage without failing due to time (although they are related: frequently
an overly-space-intensive program will take too much time, as in order to use space you
take time to do so. Keep that in mind if you do fail the space test due to time.)
Resize
Add
Find
Remove
Equals
JumbleC
Genes
GenesOrder
Mutations