Software Requirements Analysis for Cloud Platforms

This assignment focuses on analyzing software requirements within the context of cloud computing. Students will examine both functional and non-functional requirements, explore various Software Development Life Cycle (SDLC) models, and consider the influence of cloud platforms on these processes. The analysis should draw upon provided research papers and demonstrate a comprehensive understanding of the key factors involved in defining and managing software requirements for cloud-based applications.

Running Head: SYSTEM ANALYSIS AND DESIGN
Assignment
[Student Name Here]
[Institution’s Name Here]
[Professor’s Name Here]
[Date Here]

SYSTEM ANALYSIS AND DESIGN 2
Table of Contents
Introduction.............................................................................................................3
Non-functional system requirements.....................................................................3
System qualities......................................................................................................4
System interface and User interface (UI) requirements.........................................4
System constraints..................................................................................................5
Cloud-based solutions.............................................................................................5
Strengths of cloud-based solutions.........................................................................6
Weaknesses............................................................................................................6
Software/system development life cycle (SDLC)..................................................7
Predictive SDLC....................................................................................................7
Pros of predictive approach.................................................................................8
Cons.....................................................................................................................8
Adaptive approach..................................................................................................8
Pros of the approach............................................................................................9
Cons.....................................................................................................................9
The recommendation...............................................................................................9
Conclusion..............................................................................................................10
References..............................................................................................................11

SYSTEM ANALYSIS AND DESIGN 3
Introduction
Headspace project requires an efficient solution based on the current need for information
management where patients record more so their diagnostic stories are stored by an elaborate
information system. From the initial assessments given, the patients and the health practitioners
face serious challenges of information availability and accessibility because the current service
model lacks a means to store the patient's ordeals. Now, a modern information system coupled
with cloud-based resources would offer the necessary solution for meeting the needs of the
medical services (Ghosh, 2012). In essence, the stories (detailed accounts) of the patients would
be stored conveniently in the cloud infrastructure which is an endless storage resource that would
facilitate the treatments given despite the changes in the medical personnel. This report analyses
this system from a design perspective where the resource requirements including the affiliated
cloud facilities are given. Furthermore, the report also compares the different design methods of
implementing software packages and offers a conclusive recommendation.
Non-functional system requirements
While using the proposed system, the users will develop their own personalized judgments based
on the performance exhibited. In this case, they will assess the system’s usability, performance,
security and even reliability. Now, these elements outline the non-functional requirements which
generally are the criteria used to assess the operation of any given system. Moreover, while
functional requirements are determined by the technical specification and define exact behaviour
of the system, non-functional requirements stem from the collaboration of the technical
components (INF, 2004).

SYSTEM ANALYSIS AND DESIGN 4
System qualities
Although several subsidiary objectives are given for the project, the proposed system should
solve the main problem of data management where its availability and accessibility should be
improved. Moreover, the system must be able to maintain records based on the specifications
given by the users i.e. data management (Chung, 2012). Finally, the system must be resilient i.e.
it must have the ability to withstand attacks. In all, it should possess the following qualities:
Enhanced usability – a measure of the practicality quota of the system where users have optimal
satisfaction rates.
Optimal performance – the system should facilitate all the actions of the users regardless of their
complexities.
System reliability – not only the ability to withstand attacks and other hardships but also the
consistency of service delivery regardless of the platforms used.
Security – data safety as provided by good authentication and encryption standards. Moreover,
the policies used should specify the owners of the data (Rahman, Safadi, & Basaula, 2015). In
this case, since cloud facilities are to be used yet they do not fall under the jurisdiction of the
Australian law, encryption more so during transmission will be used. In addition to this, virtual
private networks (VPNs) will facilitate personalized access of the cloud resourced based on
personalized portals. Finally, end to end authentication should be used to ensure access is
managed, depending on the user’s access.
System interface and User interface (UI) requirements
The graphical and visual appeal will determine the system’s usability regardless of the overall
technical abilities or capabilities. Therefore, while the Headspace project may have a wide range
of functionalities, they should be delivered in an aesthetic way to improve the user interaction.

SYSTEM ANALYSIS AND DESIGN 5
Following UI components will determine this visual appeal:
Interface colour – user-centred design recommends colours that appeal to the users based on
their environment. Therefore, the colour of the system should match that of the organization.
Background – integrating images and different colour schematic will enhance the user’s
interaction which improves their performance of the use and that of the system.
Font colour and size – now, these elements do not have a foundational component in the design,
thus should be left for the user to decide i.e. incorporating them as preferences in the system
settings (Hassan, 2015).
System constraints
In this scenario we have three:
1. Deployment platform – many operating system and languages will be used which
possess several challenges
2. System licensing which may limit the overall functionalities.
3. Time and budget limitations.
Cloud-based solutions
Headspace delivers services to the general public which designates its operational environment
as an open platform. Similarly, cloud resources operate within the open platform where elements
of security and even data ownership are unknown. However, cloud-based solution enhance
service delivery systems by incorporating the conveniences and benefits of virtualization into the
information system. Therefore, while basic information system may offer managerial services by
collecting and analyzing information, their roles are enhanced to include real-time facilities of
consulting the resources of the worldwide web (Chappelle, 2008). Furthermore, they increase the

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overall availability of information which is a welcomed feature for Headspace as the patients
interact with several medical practitioners who require access to their data.
In addition to this, consider the storage requirement where detailed patient accounts (stories) are
necessary to give the correct treatments. These stories require a lot of storage as they are never
limited and based on the new recommendations will continuously be added on to increase the
practitioner’s understanding of the patients. Now, while in-house facilities may offer the
necessary storage, they are bulky and expensive thus will not manage the requirements.
Furthermore, these storage facilities are not able to cater for the changes in storage depending on
the volumes of data given. Therefore, the only practical solution is cloud computing as it has all
the necessary resources based on the immediate needs of the users (Primault, 2016).
Strengths of cloud-based solutions
1. Operation conveniences – consider storage as a service as given by cloud computing, this
resource can be accessed from any location so long as the user has an internet connection.
Therefore, the medical fraternity will have the benefit of data availability and accessibility.
2. Cost saving – unlike in-house resources which the user must implement and maintain, cloud
resources are supported by the service provider. This outcome minimizes the overall cost of
operations.
3. System resiliency – cloud service provider will store the customer's data in several locations
which improve the redundancy programs (DR plans) (Alton, 2015).
Weaknesses
1. Control – the user has minimal control on all the resources given including their own data
if it's migrated to the cloud facilities. Moreover, the user cannot tag or track resources the
same way they can while using the on-premise equipment.

SYSTEM ANALYSIS AND DESIGN 7
2. Data security and privacy – cloud-based solutions are accessed using public channels i.e.
the internet. This resource has many security threats that risk the data hosted on online
infrastructures. Furthermore, the users cannot adequately track the traffic between their
own in-house systems at the cloud resource.
Software/system development life cycle (SDLC)
So far, this report has highlighted several requirements of the system including the integration of
cloud computing in order to improve the system’s availability and accessibility. Furthermore, the
requirements of data security, access and ownership have been given which outlines the
complexity of the process of designing the system at hand. SDLC is an industrial response to
these extended requirements where developers use well-known procedures to implement
software solutions. In this case, SDLC will define the technical requirements i.e. functional
elements such as size and capabilities. Furthermore, it will outline the non-functional
requirements which provide the criteria for judging the system’s operations. Finally, having
identified these requirements it will provide the implementation procedure to ensure these
elements are reflected in the final solution (Isaias & Issa, 2010). Therefore, SDLC can be defined
as the process of planning, developing and deploying software systems.
Predictive SDLC
A conventional approach that uses a completely predictable procedure to implement software
solutions. In essence, the development process will start by defining all the system’s preferences
and requirements as outlined by the end user. In addition to these elements, the approach will
define several assumptions that will never change such as the design personnel and
implementation time. Now, following these assumptions and requirements, the approach will
define a consistent procedure of enacting the solution. This procedure must avoid all variations

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and changes to system specifications as they will result in the breakdown of the development
procedure (Okoli & Carillo, 2010). Moreover, the procedure will also follow a sequential
guideline or flow of events based on a number of implementation stages. Again, this flow of the
implementation stages is independent of changes, an outcome that limits the stages overlaps or
concurrent development.
Pros of predictive approach
1. The predictive approach requires accurate documentation of the design process, a
requirement that improves its accountability. Furthermore, it's easier to compare results
with the initial assumptions.
2. It is also cost-effective as the solution implemented must conform with the initial
assessments made including the budget allocations.
3. Finally, it is also very simple to use because the implementation stages are usually
given and work in a logical flow of events (Peru, 2014).
Cons
1. The approach tends to provide many instances of uncertainty and ambiguity as unknown
variables are not adequately handled by its procedures.
2. Furthermore, it's time intensive as all logical steps must be followed sequentially without
any form of overlap, an outcome that consumes a lot of time.
Adaptive approach
While the predictive approach is the traditional method of implementing systems, the adaptive
approach is the industrial response to the limitations of predictive SDLC. In essence, the adaptive
method gives system design and development a modern outlook where the requirements of
agility, flexibility and resilience are demanded. Therefore, unlike the predictive approach, it does

SYSTEM ANALYSIS AND DESIGN 9
not outline specific assumptions but assumes that all requirements will change, outcomes that
enhance its application.
To start with, the adaptive approach does not define a sequential procedure, instead, it provides a
logical structure based on the segmentation of different implementation stages. These stages are
then deployed depending on the needs of the users. Therefore, several implementation stages can
be run concurrently and later combined to form the final solution. Now, informing the final
solution several iteration techniques are used which continuously tests the system based on the
rudimentary functionalities of the system, otherwise known as prototypes (UT, 2017).
Pros of the approach
1. Time efficient – system developer can implement a single solution using an extended
team running different implementation stages concurrently.
2. User-centered approach – the approach develops solutions with the constant consolation
of the user which improves system performance and usability.
3. Agile and flexible – any changes to the development process can be accommodated
without affecting the performance of the final solution (Okoli & Carillo, 2010).
Cons
1. In most cases, the approach tends to require a lot of expertise because of the
diversification made on the implementation process i.e. the stages.
The recommendation
By all definitions, the Headspace project is a modern system with advanced requirements that
include the use of virtualization. These requirements are quite extensive for the assumptions of
predictive methods. Moreover, these requirements will constantly change depending on the
immediate needs of the user. Therefore, the predictive method would not suffice as it would fall

SYSTEM ANALYSIS AND DESIGN 10
short on these requirements. Furthermore, the adaptive approach is agile and flexible, features
that would guarantee the integration of the system with cloud computing (MIS, 2015). The
approach (adaptive) would also optimize resources as it would be able to adapt to different
system changes.
Conclusion
There are many considerations to be made by the Headspace project before implementing the
proposed system. For one, the project requires to identify a suitable cloud resource that will fulfil
their requirements. Furthermore, the project holds a long-term goal having the resources of the
patients and their practitioners. Therefore, the development approach should reflect these
requirements including the non-functional elements. Now, these requirements will constantly
change in the future which outlines the reasons for the SDLC approach chosen. Moreover, the
system should adequately integrate with the cloud-based services including the requirements of
data security and ownership, critical factors that will be determined by the implementation
process. In all, the SDLC approach will dictate the success of the system including its integration
with other functional facilities.

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References
Alton, L. (2015). Cloud computing Pros. IT business edge, Retrieved 28 September, 2017, from:
http://www.smallbusinesscomputing.com/biztools/the-pros-and-cons-of-cloud-
computing.html.
Chappelle, D. (2008). A SHORT INTRODUCTION TO CLOUD PLATFORMS. AN
ENTERPRISE-ORIENTED VIEW, Retrieved 28 September, 2017, from:
http://www.davidchappell.com/CloudPlatforms--Chappell.pdf.
Chung, L. (2012). Non-Functional Requirements. Retrieved 28 September, 2017, from:
https://www.utdallas.edu/~chung/SYSM6309/NFR-18-4-on-1.pdf.
Ghosh, A. (2012). Cloud Computing. M.Tech. Seminar Report, Retrieved 28 September, 2017,
from: https://www.cse.iitb.ac.in/~abhirup09/Docs/cloud_computing_final_report.pdf.
Hassan, A. (2015). Software Architecture. CISC 322, Retrieved 28 September, 2017, from:
http://research.cs.queensu.ca/~ahmed/home/teaching/CISC322/F09/slides/
CISC322_02_Requirements.pdf.
INF, C. (2004). Software Requirements. CS2 Software Engineering note 2, Retrieved 28
September, 2017, from:
http://www.inf.ed.ac.uk/teaching/courses/cs2/LectureNotes/CS2Ah/SoftEng/se02.pdf.
Isaias, P., & Issa, T. (2010). Information System Development Life Cycle Models. Retrieved 28
September, 2017, from:
http://www.springer.com/cda/content/document/cda_downloaddocument/
9781461492535-c2.pdf?SGWID=0-0-45-1479416-p175478101.
MIS. (2015). The System Development Life Cycle. Retrieved 28 September, 2017, from:
https://utexas.instructure.com/courses/1166782/files/38198507/download.
Okoli, C., & Carillo, K. (2010). The best of adaptive and predictive methodologies: Open source
software development, a balance between agility and discipline. Retrieved 28 September,
2017, from: http://chitu.okoli.org/media/pro/research/pubs/OkoliCarillo2010IJAESD.pdf.
Peru, G. (2014). Software Development Life Cycle. GSL Peru , Retrieved 28 September, 2017,
from: http://gsl.mit.edu/media/programs/peru-summer-2014/materials/t04-

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_software_development_life_cycle.pdf.
Primault, C. (2016). Cloud Computing for Small Business Success. Retrieved 28 September,
2017, from: http://getapp.ulitzer.com/.
Rahman, R., Safadi, W., & Basaula, A. (2015). Functional And Non-Functional Requirements.
Retrieved 28 September, 2017, from: http://ami-2015.github.io/MyGuide/d2-final.pdf.
UT. (2017). 2 - SDLC - adaptive and predictive.pdf. MIS, Retrieved 28 September, 2017, from:
https://utexas.instructure.com/courses/1166782/files/38198507.