ESTIMATION MODELS

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COCOMO Estimation Model

The COCOMO estimation model will be relevant in the estimation of the Missed Call Notification software costs and any other project parameters. However, in the usage of the COCOMO product, it will not make up for the variations in hardware constraints, individual quality, and experience. The main rationale for this is that they limit the accuracy of the COCOMO model. It is, however, crucial to account for the usage of contemporary tools and strategies in developing the software as they significantly influence the MCN software costs. Accordingly, the COCOMO estimation will trail the following formulae:

 Effort = a1 x (KLOC) a2 PM

Tdev = b1 x (Effort) b2 Months

In cost estimation, one has to review the MCN platform. The determining factors will be, the hardware, which has a simple logic implementation attached to, the software logic which has medium difficulty and the fact that MCN software is just a middleware between internal information technology network and telecommunication core network, thus has high difficulty. The support, service maintenance, and infrastructure.

The development of the MCN program can also be estimated in three specific categories (Kishore & Naik, 2001, p. 199-222):

  1. Organic: which is achieved by the following formula: 

Effort = 2.4(KLOC) 1.05 PM

  1. Semi-Detached: which is arrived at by the following formula:

Effort = 3.0(KLOC) 1.12 PM

  1. Embedded:

Effort = 3.6(KLOC) 1.20 PM

PM denotes Person Months

It is crucial to present the levels of difficulty regarding the development, technical requirements, and implementation of the MCN. 

MCN PROJECT CHARACTERISTICS 
Development Technical Requirements Implementation
Organic Little Simple Simple
Semi-detached Medium Medium  Medium
Embedded Complex Complex Hardware Complex

in the MCN case, development has medium complexity, technical requirements are complex, and implementation has medium difficulty.

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Process-based Estimation Model

Comparatively, this is the most used project estimation technique. When using this method in the MCN case, one has to base the estimates on the processes used when developing the program. According, the process-based estimation technique decomposes the entire procedure into comparatively small task sets and consequently estimates the effort needed to complete each duty (Keyes, 2004, p. 207-209).  There is a similarity between process-based and problem-based estimation techniques because, when we apply the process-based estimation model, we will start by delineating software functions acquired from the scope of the entire project (Goodman, 2006, p. 63-67). Accordingly, a series of the MCN program process activities will be engaged when tackling every function. After process activities and problem functions are combined, effort estimates, for instance, person-months necessary to complete each software process activity in every program function are evaluated. Individual quality is also a key factor to consider in this process, for instance, its establishment that in the MCN case, senior staff was primarily engaged in early activities thus they become more expensive as compared to junior staff who are engaged in the later design stages such as generating codes.

Triangulation Results 

The main benefit of using the triangulation estimation process reasonableness verified through expert judgment (Colomo-Palacios et al., 2014, pp. 59-60). The triangulation involves comparing the software process and costs with other external. It will therefore also incorporate sensitivity analysis on central variables such as the development, technical and implementation process. Comparisons in this process are crucial as they can also show when the estimates established are starting to inflate. 

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  1. Colomo-Palacios, R., Villalón, J. A. C.-M., Seco, A. d. A. & Gilbert, T. S. F., 2014. Agile estimation techniques and innovative approaches to software process improvement. Hershey, PA: Information Science Reference.
  2. Goodman, F. A., 2006. Process-Based Software Project Management. s.l.:s.n.
  3. Keyes, J., 2004. Software Configuration Management. New York: Auerbach Publications.
  4. Kishore, S. & Naik, R., 2001. Software requirements and estimation. New Delhi: Tata McGraw-Hill Pub. Co..
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