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Jeremy Hudson
Marcus,
 
Thank for your post.  Keep with the Risk management theme scrum team fail that do not Plan Risk Response. In a recent study majority of respondents (57 %) think that this step is done during sprint planning. In sprint planning, the team through its knowledge contributes to the choice of optimal option for project risk, and then a risk implementation should be executed. 34% assert that daily scrum opens an interval where we can plan the newly identified and analyzed risk responses during the sprint(Chaouch et al., 2019).
 
Reference:
Chaouch, S., Mejri, A., & Ghannouchi, S. A XXXXXXXXXXA framework for risk management in scrum development process. Procedia Computer Science, 164, XXXXXXXXXX. https:
doi.org/10.1016/j.procs XXXXXXXXXX171Links to an external site.
Ho Choi
Marcus,
Good post.
I agree that the Scrum methodology would have help tremendously if the team followed it wholeheartedly. One thing that caught my attention was in your last paragraph, you mentioned the importance of the Scrum Master. That his/her role is "essential in removing obstacles". I believe this is the crux of the problem for ByteNinja. The Scrum Master or Project Manager in this case is the leader that directs the project. He is the one that oversees everything and makes sure that it will be completed on time and within budget and that the final outcome is met. If within the team, there is someone slacking off, it is the job of the project manager to go after that person and make sure that the slacking off stops and that the project gets back on track. That's why this role is so critical to the overall project. When the Project Manager does not live up to his duty, then for sure the project will not succeed.
I would not put all the burden on the Project Manager, but in the end, he will be liable for the final outcome. 
Martin Rock
Nowadays the rise and fall of software companies is common. Those who learned lessons from their previous failures succeed…The software industry also adopts new approaches with the change in technology and techniques. Agile methodology is one of the methods that help lead to the success of any software ( P. Emam Hossain, XXXXXXXXXXScrum is the framework of agile methodology as it focuses on day-to-day project management. For the ByteNinja team, adopting Scrum could significantly improve the delivery of the Kartastic Bites POS system project by addressing past issues and cu
ent objectives.
Scrum methodology provides a framework for achieving high-quality products by
eaking down the project into manageable pieces or sprints. Each sprint focuses on delivering a set of features or functionalities that meet the quality standards established by the team. At the end of each sprint, the team conducts a review to assess the quality of the deliverables and identify areas for improvement. Given the challenges faced by the team. 
Effective communication is essential as well for the success of the project. Effective communication involves generating, collecting, disseminating, and storing project information. It ensures that all team members and stakeholders are aware of project status, requirements, and changes. Effective communication also helps to identify and resolve issues early on, preventing them from escalating into major problems.
"Lack of communication or poor communication will invariably cause your Scrum team to fall apart…Team members must have clarity about their roles, responsibilities, their team's Sprint capacity, and the scope of the problem that needs to be solved. Having a clear idea about the dates that are important for the success of the product, the purpose of the product, the customer feedback, the action items from the Sprint Retrospective, etc. helps the entire team take shared ownership of the team's results" (Ravlani, 2019).
Adopting Scrum will not only help ByteNinja manage the development of Kartastic Bites' POS system more efficiently but also ensure the final product closely aligns with Sarah's needs through improved communication, transparency, and iterative evaluation. This approach positions ByteNinja to overcome previous challenges and achieve a successful project outcome.
 
Reference
2009. A. B. H.-y. P. Emam Hossain, "Using Scrum in Global Software Development: A Systematic Literature Review", 4rth IEEE International conference on Global Software Engineering, July 2009.
Ravlani, K. (2019, May XXXXXXXXXXways the scrum master can improve scrum team communication. Scrum Certification Training and Agile Coaching. https:
agileforgrowth.com
log/scrumteam-communication

Marcus Mccall
Integrating Scrum principles and practices includes solutions that describe the struggles in the software development teams. Scrum in the team can address challenges by highlighting Business Problem Scenarios and Programming Team Scenarios. As highlighted in the project, Communication is an essential aspect of Scrum and can help mitigate misalignment and ineffective collaboration. Daily Scrum meetings can help team members synchronize activities (Paul & Behjat, 2019). This will help discuss progress and provide a platform for transparent Communication. It ensures all stakeholders know about tasks, progress, and potential obstacles to create a collaborative environment. Task management can help in the development of Sprint Planning of Scrum. Sprint Planning in a team requires the selection of tasks from the product backlog. It includes clearly understanding priorities and achievable goals for upcoming sprints. The Sprint Review allows regular inspection of completed work. It provides opportunities to adapt and reprioritize tasks-based feedback from stakeholders. Quality assurance challenges must be addressed through the implementation of sprint retrospectives. It helps the team to reflect on past sprints to identify areas for improvement and implementation of solutions. This includes the implementation of refining processes and incorporating feedback loops.
The team can enhance quality assurance practices to reduce the occu
ence of software bugs and defects. Roles included in Scrum are Product Owner, Scrum Master, and Development Team. It can help establish responsibilities and accountabilities. It ensures a sense of ownership and empowerment in the team (Bhavsar, Shah & Gopalan, XXXXXXXXXXThe Scrum Master is essential in removing obstacles that can hamper progress. This ensures that the team can focus on delivering value. ByteNinja can transform the software development approach, which will help implement better Communication, task management, and quality assurance practices. It includes regular iterations and feedback loops that allow the team to overcome challenges. It will help the project develop a POS system for Kartastic Bites.
 
References:
Bhavsar, K., Shah, V., & Gopalan, S XXXXXXXXXXScrum: An agile process reengineering in software engineering. International Journal of Innovative Technology and Exploring Engineering, 9(3), XXXXXXXXXX.
Paul, R., & Behjat, L. (2019, June). I am using principles of SCRUM project management in an integrated design project. In The 15th International CDIO Conference.
students-posts-using-scrum-on-a-software-project-attachment-w1mtmoxq-cj52euxc.docx marcus-mccall-my-post-using-scrum-on-a-software-project-wk1-ism510-5c5hk2ue-t0lcjzcc.docx
Answered 3 days After Sep 25, 2024

Solution

Pashikanti Sneha answered on Sep 28 2024
4 Votes
COMP Assignment
Question 1: Find the smallest and second smallest element in an a
ay in
linear time.
Algorithm:
● Step 1: Initialize two variables, smallest and second_smallest, to store the
smallest and second smallest values. Initially, set smallest to infinity (∞) and
second_smallest to infinity.
● Step 2: Traverse through the a
ay once. For each element x in the a
ay:
○ If x is smaller than smallest, update second_smallest = smallest
and smallest = x.
○ Else if x is smaller than second_smallest and not equal to smallest,
update second_smallest = x.
def find_two_smallest(a
):
smallest = float('inf')
second_smallest = float('inf')
for x in a
:
if x < smallest:
second_smallest = smallest
smallest = x
elif x < second_smallest and x != smallest:
second_smallest = x
eturn smallest, second_smallest
Time Complexity:
● Linear time: The algorithm only requires a single traversal through the a
ay (O(n)).
Number of Comparisons:
● In the worst case, for each element x:
○ 1 comparison to check if x < smallest
○ 1 comparison to check if x < second_smallest (if the first comparison
fails).
● This means 2 comparisons per element in the worst case. Hence, for an a
ay of
size n, the worst-case number of comparisons is 2 * (n - 1).
Question 2: Finding the largest and second largest element.
The algorithm to find the largest and second largest element is essentially the same:
● Initialize two variables, largest and second_largest, to negative infinity (-∞).
● Traverse the a
ay and compare each element to find the largest and second largest
in the same manner as the previous algorithm.
def find_two_largest(a
):
largest = float('-inf')
second_largest = float('-inf')
for x in a
:
if x > largest:
second_largest = largest
largest = x
elif x > second_largest and x != largest:
second_largest = x
eturn largest, second_largest
Question 3: Using the algorithm for smallest and second smallest to find
the largest and second largest.
You can modify the input to use the same algorithm for both tasks:
● Invert the a
ay: Multiply every element by -1.
● Apply the smallest and second smallest algorithm: Run the algorithm fo
smallest and second smallest elements on the inverted a
ay.
● Invert the results back: Multiply the resulting smallest and second smallest values
y -1 to obtain the largest and second largest.
def find_two_largest_using_smallest(a
):
inverted_a
= [-x for x in a
]
smallest, second_smallest = find_two_smallest(inverted_a
)
eturn -smallest, -second_smallest
Question 4: Finding the median using the above two algorithms.
To find the median:
1. Find the smallest, second smallest, largest, and second largest elements.
2. The median of a sorted a
ay is the middle element when the number of elements is
odd, or the average of the two middle elements when the number of elements is
even.
3. With this information, you can repeatedly eliminate the smallest and largest
elements, effectively na
owing down to the median.
This strategy can be implemented in linear time by repeatedly applying the smallest and
largest finding algorithms.
Time Complexity:
The time complexity remains O(n) since each pass through the a
ay takes linear time, and
we can reduce the a
ay size gradually.
Question 5: Prove co
ectness using loop invariants.
Loop Invariant:
● Invariant: At the start of each iteration of the loop, the variables smallest and
second_smallest hold the two smallest elements encountered so far in the a
ay.
Proof Outline:
1. Initialization: Before the loop starts, both smallest and second_smallest are
initialized to infinity, ensuring that the invariant holds (since no elements have been
processed yet).
2. Maintenance: On each iteration, the algorithm co
ectly updates smallest and
second_smallest by comparing the cu
ent element with the smallest and second
smallest values. The invariant is maintained since these variables are always
updated to reflect the smallest and second smallest elements encountered so far.
3. Termination: After the loop completes, smallest and second_smallest hold the
two smallest elements of the entire a
ay, as required. Thus, the algorithm is co
ect.
Median Proof:
The same logic applies to finding the median.
Question 2: DT claims that he can perform FindMax in o(log n)
comparisons.
● FindMax is the operation of finding the maximum element in an a
ay and then
deleting it.
(a) What can you say about the number of comparisons of the most
efficient sorting algorithm based on DT’s FindMax?
If DT’s claim were co
ect, we could potentially use his FindMax operation to build a sorting
algorithm. A simple sorting strategy could be:
1. Find the maximum element using FindMax.
2. Delete the maximum element.
3. Repeat the process on the remaining elements until the a
ay is fully sorted.
This is essentially a variation of Selection Sort, where we repeatedly extract the maximum
element instead of the minimum. For an a
ay of size n, we would perform n iterations,
where each iteration involves finding and removing the maximum element.
● First iteration: Find the maximum in the whole a
ay of size n.
● Second iteration: Find the maximum in the remaining n - 1 elements.
● ...
● Last iteration: Find the maximum in the last 2 elements.
Now, if the FindMax operation requires o(log n) comparisons as claimed, the total numbe
of comparisons for sorting the entire a
ay would be the sum of the comparisons made
across all iterations.
f each call to FindMax requires o(log n) comparisons, we can approximate the total
number of comparisons as:
Total comparisons = o(log n) + o(log (n-1)) + ... + o(log 2)
This would yield a total number of comparisons for sorting as o(n log n).
However, from well-established theory, we know that O(n log n) is the best possible time
complexity for comparison-based sorting algorithms (like Merge Sort or Heap Sort).
Therefore, o(n log n) comparisons is not possible for a comparison-based sorting
algorithm.
This means that if DT’s FindMax implementation could actually achieve o(log n)
comparisons, then the sorting algorithm built upon it would contradict this established...
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