Software Project Estimation Models

A project is considered successful when it is completed on schedule, on budget, and with the necessary quality. These are some specific goals that the project manager and his team are working to achieve. Making accurate estimates is essential to this procedure. The effort, which influences the cost, and the duration, which influences the delivery time, must be estimated by the project manager.

Challenges of Project Estimation:

Part of the estimating challenge stems from the software’s intricacy and obscurity. Among the additional challenges are the following:

• Subjective Nature of Estimation: Individuals frequently overestimate the difficulty of big jobs while underestimating the difficulty of small ones.
• Political Implications: Various factions within an organization have varying goals. For instance, development managers might try to pressure estimators to lower their cost estimates to persuade upper management to accept an increasing number of projects. In contrast, some groups may attempt to raise the projections to put themselves in a more comfortable position (taking into account any potential risks or budget slippage).
• Changing Technology: It is challenging to apply the lessons learned from past projects to new ones when technology is evolving so quickly.
• Inconsistency in the project experience: Experiencing inconsistency in a project refers to encountering variations, irregularities, or discrepancies in different aspects of the project, such as planning, execution, documentation, or team collaboration. Inconsistencies can lead to confusion, inefficiencies, and potential challenges in meeting project goals.

Metrics for Estimating Project Size:

To estimate time, effort, and cost satisfactorily, it is imperative to accurately estimate the scale of the challenge. The following appropriate metrics or units must be defined to determine the project size accurately:

Line of Code (LOC):

The smallest matrix accessible is called LOC, and its size is determined by counting the number of source instructions in the final program. Depending on the specific coding style, the LOC, which provides the problem’s numerical value, can vary significantly.

Function Point Metric(FPM):

Albrecht was the one who first introduced FPM. One of the key benefits of FPM is that it makes it simple to determine the size of a software product based only on the problem description. In contrast, size can only be precisely ascertained after the product has been fully quoted when using the LOC metric. The fundamental tenet of FPM is that the quantity of features or functionalities that a software product supports directly relates to its size. It makes sense that a software package with more features will be larger than one with fewer features.

Techniques for Project Cost Estimation:

One of the fundamental planning activities is estimating different project parameters. Size, effort, and cost are some of the key elements that need to be assessed. In addition to being helpful to clients, these estimates are also helpful for scheduling and resource planning:

Cost Estimation indicates a method by which the cost estimates are determined. The amount of money spent on software development and testing in software engineering is known as the cost estimate

. A project’s or product’s cost can be estimated using parametric equations or mathematical algorithms called cost-estimating models. As demonstrated below, there are numerous methods or models for cost estimating that are also referred to as cost estimation models.

Empirical Techniques:

This method relies on estimating project parameters and carrying them out. Experience in creating a comparable product is useful in this situation. Among these methods are:

• Expert Judgement Technique
• Delphi Cost Method
• Estimation By analogy

Heuristic Technique:

The word “heuristic” comes from a Greek verb that means “to discover.” A model or strategy known as a heuristic is applied to practical procedures that are meant to achieve short-term objectives to solve issues, learn, or make discoveries. These methods are easy to use and adaptable for making fast choices using shortcuts and accurate computations, most often in the case of complex data. However, for the choices made using this method to be ideal, they must be made. This method uses mathematical equations to express the link between several project factors. The Constructive Cost Model provides a common heuristic technique (COCOMO). Moreover, this method is employed to accelerate or intensify analysis and investment choices.

Analytical Techniques:

One kind of method used to gauge work is analytical estimation. Using this method, the work is initially separated into its constituent parts or activities to facilitate analysis. Second, each part or component of the job is subject to these sources if the standard time is obtained from another source. Third, the work is estimated using the work’s experience if no such time is available. With this method, a few fundamental project assumptions are made to get the outcomes. As such, there is some scientific support for the analytical estimation technique. An analytical estimation model serves as the foundation for Halstead’s software science.

Other models for cost estimation are:

• Function Point Analysis (FPA): This method assesses how sophisticated and functional a piece of software is by counting the number and complexity of tasks it can complete. This model can be used to predict the amount of work required for development, testing, and maintenance.
• Putnam Model: This parametric estimation model accounts for the size of the program, the development team’s experience, and other project-specific factors when estimating effort, time, and errors.
• Cost-to-Win Estimation: This methodology, which is frequently used in competitive bidding, aims to project the costs related to creating a certain software project to win a contract. It entails examining competitors and market conditions.
• Models Based on Machine Learning: Neural networks, regression analysis, and decision trees are a few examples of machine learning techniques that can be used to create custom cost estimation models. The models are built using historical project data. These models are adaptable enough to take into account elements unique to each project as well as evolving data.
• Function Points Model (IFPUG): The International Function Point Users Group (IFPUG) provides a standardized method for evaluating the functionality of software using function points. It is used to determine how much work goes into developing and maintaining software.