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Discuss about the Headspace System Analysis and Design.

Headspace a well known Australian youth mental health foundation, that serves a mobile application to those young consumers, who are suffering from mental illness. This is basically an online counseling application (Dinh et al., 2013). In order to access the mobile app the users are required to register themselves as a regular customer. In order to meet the requirement of the consumers it is very much necessary to understand the functional and non functional requirement and the requirements are as follows:

Feasibility: The system developed for Headspace is needed feasible enough. Feasibility is one of the most important things that is strictly required to be considered by the managerial head of the company to measure that whether the outcome of the application will be beneficial for the company or not.

Reliability: The system is needed to be reliable at the same time so that all the components of the system could work properly to meet the required application properly (Fernando, Loke & Rahayu, 2013). In order to perform the task properly it is very much important for the system to be reliable enough.  

Performance: The performance of the system is a one of the major metrics that should be considered before the implementation of the supplication. The app should be accessible regardless of time and location of the consumers. Based on the performance of the system Headspace will be able to grab consumers from throughout Australia (Rahimi et al., 2014). Not only this but also the response time, stability, supportability and resilience are also somewhere dependent on the performance of the system.

Security: Security is another important factor for the system that will help the company to gain competitive advantages and high ranged revenue model from the market. Proper security aspects are needed to be adopted by the management authority of the business organization named as headspace so that none of the external or unwanted attackers could come and access information regarding the employees and patients as well from the data server.

Comparison between Non functional and functional requirements


Non Functional

This is user specific in nature and the functional requirement is an activity system that must be performed properly.

Whereas the functional requirements are completely managed by the technical experts.

The software level functionalities could be defined with the help of functional requirements.

The non functional requirements could be support all the supplications properly.

The functional requirements help to define essential trouble shot activities.

The maintainability and the extensibility can be evolved properly with the help if the non functional requirements

Contrast between Non functional and functional requirements

In order to develop the mobile supplication for Headspace both the functional ad non functional requirements are necessary to be followed. The functional requirement for Headspace

Are needed to be processed properly.

Strength and weakness of the cloud based system

Cloud computing



Cost saving: The Headspace mobile app is based on SaaS cloud model and from the significant features of both the functional and non functional requirements it has been found that cloud based model is cost saving (Yang et al., 2014).  The power cost, operation cost and even the conditional as well as administrational cost also less in case of cloud computing.

Security: Security is ne of the most important thing that is strictly required to be considered by the system developers of Headspace. While storing data in the cloud server if, proper protection mechanism are nt adopted then the information cloud get hijacked very easily b the external attackers.

Reliability: Cloud based models are reliable in nature. It offers SLA that guarantees over 24/7/365 and 99.99% availability. Even from the redundant IT resources, massive benefit can be gained.

Vendor Lock In: It is found that, the cloud service providers promises to serve a flexible, integrated and switching system to the consumers (Ahmed et al., 2015). However while developing such system from the consumers they do face higher level difficulties.

Manageability: Cloud based model provides increased and simple IT management infrastructure to the developers. Also offers SLA guarantee that ensure the timely delivery and maintenance as well (Liu et al., 2015).

Limitation on control: As the cloud infrastructure completely owner, controlled and monitored by the service providers thus, from the consumers aspects the control capacity is very less. Which is a major issue in cloud computing

As the technology is improving day by day thus the mechanism for storing and securing data from the external attackers are also evolving accordingly. In order to keep the data secured from hackers proper security measures are required to be considered. The technology for data security data in any kind of cloud based platforms are as follows:


Encryption: Encryption is one of the most modern as well as advanced from the data security that most of those companies used for protecting information from the hijackers. In case of encryption technology a hidden code is used by the system developers (Joorabchi, Mesbah & Kruchten, 2013). That code is accessible to the sender and the receiver only and none of the third party will be able to access data from the server without proper authorization.  The secret encrypted code is transfer to the receiver and with the help of the proper decryption algorithm that particular code could be decrypted. It means that if the sender and receiver fails to use proper encryption and decryption leys then information might get hijacked by any user.

Authentication: System authentication is another important thing that is strictly required to be considered by the managerial head of the Headspace app developers. Authentication is the process of PIN verification (Shih et al,. 2015) If the users fails to detect the proper PIN then he or will be declared as the unauthenticated user and the data server will never be accessible to them. Thus, it can be said that with the help o the authentication mechanism the information could kept secured from the external attackers easily.

Web application Firewall: The worldwide internet network security has been improved with the help of the web level application firewall. From the SQL injection, comet spam, cross site scripting and even from the core Ecommerce specific attacks also the servers and the information stored in the server could be kept secured accordingly (Barnett, Vasa & Grundy, 2015). If application firewall is used the hackers will not be able to send unwanted request thus, the information will be secured from the external attackers.

The predictive SDLC approach is also known as the sequential SDLC methodology. Different software developments methodologies are there those are predictive bay nature. Waterfall model is the one the most known predictive model that is used for testing, debugging, construction, and integration as well (Mankad, Hu & Gopal, 2016). The Pros and cons of this approach are as follows:



This model is very much simple and easy for the developers understanding.

If in any middle phase the project requirements are found to make some necessary changes then, that is very much difficult in case of predictive model.

Due to the model rigidity it could be even managed much easily. Each phase associated to Predictive model holds specific deliverable and process modeling approach as well.

It is not possible to implement any working software until late during the life cycle. High range of risk and uncertainty are also associated to it.

Every phase of this model can be competed at one chance and no overlapping among the phase could occur in this model.

This model is not at all useful for any object oriented and complex project. For the long term ongoing projects this model is nit at all useful.

If the project requirements are clearly understood by the project manager and the project development team members then, utilization of the waterfall model will be very much beneficial (Hermano & Stewart, 2014).  However, for the larger and complex projects the predictive SDLC model is not at all useful.

This model is not useful for projects where chances of risk occurrence is very high and at the same time, if the project related requirements are not  clear then this model should not be used by the system developers.

The adaptive SDLC model is also act as a combination of several agile models. The pros and cons of this model are as follows:



This model is used step by step it means that the system developers can use this particular model where the content could be changed easily with the changing phase and requirement of the consumers.

If the users suggest adding continuous project functionalities then, it become a little difficult for the developers (Green et al., 2014).

The development of this particular model could begin without high costing documentation, costly device and digital support as well (O'Malley et al., 2014).

If huge number of project team members gets attached to the project then major system level failure might occur.

Immediate changes can be made as per the changing requirement of the consumers.

Due to lack of technical experiences the project cannot be completed successfully,

Scope based and priority based changes could be made and with the help of continuous communication, with a lower cost, the quality of the projects can be improved dynamically.

Application of such model is much expensive than the other models (Nezerwa, et al., 2015).

After considering the background of the Headspace mental counseling app, it has been found that, based on both the functional and non functional requirements of the app the most suitable model application for the company are the adaptive model. As the headspace model is a mobile application thus, after getting feedback from the consumers the system model will definitely required to be changed accordingly. In case of predictive model if changes are required to be done in any middle phase then that will stand as very much difficult. On the other hand, in case adaptive model sequential changes could be done easily. Therefore, for developing the mobile app for Headspace, the suggested model is the adoptive model.  



Ahmed, E., Akhunzada, A., Whaiduzzaman, M., Gani, A., Ab Hamid, S. H., & Buyya, R. (2015). Network-centric performance analysis of runtime application migration in mobile cloud computing. Simulation Modelling Practice and Theory, 50, 42-56.

Barnett, S., Vasa, R., & Grundy, J. (2015, May). Bootstrapping mobile app development. In Proceedings of the 37th International Conference on Software Engineering-Volume 2 (pp. 657-660). IEEE Press.

Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A survey of mobile cloud computing: architecture, applications, and approaches. Wireless communications and mobile computing, 13(18), 1587-1611.

Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future generation computer systems, 29(1), 84-106.

Green, L. S., Hechter, R. P., Tysinger, P. D., & Chassereau, K. D. (2014). Mobile app selection for 5th through 12th grade science: The development of the MASS rubric. Computers & Education, 75, 65-71.

Hermano, M., & Stewart, G. (2014). Design guidelines for a mobile app for wellbeing of emerging adults. In Twentieth Americas Conference on Information Systems Proceedings (pp. 1-14).

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Joorabchi, M. E., Mesbah, A., & Kruchten, P. (2013, October). Real challenges in mobile app development. In Empirical Software Engineering and Measurement, 2013 ACM/IEEE International Symposium on (pp. 15-24). IEEE.

Liu, J., Ahmed, E., Shiraz, M., Gani, A., Buyya, R., & Qureshi, A. (2015). Application partitioning algorithms in mobile cloud computing: Taxonomy, review and future directions. Journal of Network and Computer Applications, 48, 99-117.

Mankad, S., Hu, S., & Gopal, A. (2016). Single Stage Prediction with Online Reviews for Mobile App Development and Management. arXiv preprint arXiv:1607.07515.

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Yang, L., Cao, J., Yuan, Y., Li, T., Han, A., & Chan, A. (2013). A framework for partitioning and execution of data stream applications in mobile cloud computing. ACM SIGMETRICS Performance Evaluation Review, 40(4), 23-32.

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