Costing in a project, especially in software development, refers to the process of estimating, budgeting, and managing expenses related to completing the project objectives. Here are the typical elements that contribute to costs in a software project:

  • Personnel Costs such as salaries, benefits, and other compensations for the team members working on the project.
  • Hardware Costs – related to the purchase or rental of hardware infrastructure like servers, computers, networking equipment, etc.
  • Licensing fees or costs associated with purchasing software tools and platforms needed for development, testing, and deployment.
  • Infrastructure Costs for setting up and maintaining development and testing environments, cloud services, or physical infrastructure.
  • Costs for outsourcing certain development tasks, consulting fees, or using external services such as cloud hosting, APIs, etc.
  • Expenses related to training the team, providing customer support, or user training after deployment.
  • Overheads – General administrative expenses, utilities, office rent, and other indirect costs incurred during the project.

Expert Judgment

Expert judgment relies on the experience and insights of individuals who have worked on similar projects or have extensive knowledge in the domain. It involves consulting with industry experts, project managers, or senior developers to gather their opinions on the project’s cost.

Example: A company plans to develop a new mobile app similar to an existing one in terms of complexity and features.

Estimation Process: The project manager consults with two senior developers and a previous project manager who worked on a similar app.

Result: Based on their collective expertise, they estimate the total project cost to be around $150,000, considering development, testing, and deployment phases.

Analogous Estimating

It uses historical data from similar projects as a reference point for estimating the current project’s costs. It assumes that the current project will have a similar cost profile to past projects of comparable size and complexity.

Example: A company is planning to develop a new e-commerce platform with similar functionalities to an existing one they developed last year.

Method : The project manager reviews the cost data from the previous e-commerce platform project.

Result: Based on the historical data, the previous e-commerce platform project cost $200,000. The current project is estimated to cost around $220,000, factoring in inflation and any additional complexities.

Parametric Estimating

Parametric estimating uses statistical relationships between historical data and project parameters (such as size, lines of code, or complexity) to calculate project costs. It involves using mathematical models or algorithms to derive cost estimates based on specific project metrics.

Example: A company plans to develop a custom web application based on specific functional requirements.

Method : We estimate cost based on the number of user stories or functional points required for the application. The parametric model calculates that similar web applications have cost $100 per functional point. If the application requires 200 functional points, the estimated cost would be $20,000.

Bottom-Up Estimating

It involves estimating the cost of individual tasks or work packages and then aggregating these estimates to derive the total project cost. It provides a detailed breakdown of costs at a granular level.

Example: A company is tasked with developing a new CRM (Customer Relationship Management) software. The project team breaks down the project into smaller tasks such as database design, front-end development, back-end development, testing, and deployment.

Result: Each task is estimated separately: database design $10,000, front-end development $20,000, back-end development $30,000, testing $15,000, deployment $5,000. The total estimated cost is $80,000.

Why is costing difficult for a software project?

Costing for a software project can be challenging due to several inherent factors:

  • Software projects often involve intricate technical requirements, evolving user needs, and complex interactions. Predicting all possible scenarios accurately can be difficult, leading to uncertainty in cost estimation.
  • Requirements in software projects can change frequently, either due to evolving business needs, new technological insights, or user feedback. These changes can significantly impact the project scope, timeline, and consequently, the costs.
  • The expertise and experience levels of team members can vary, affecting productivity, efficiency, and ultimately, project costs. Estimating the impact of team composition on costs can be challenging.
  • Different estimation techniques (e.g., expert judgment, parametric estimating) vary in their accuracy and applicability depending on the project’s characteristics. Selecting the most suitable technique requires understanding its strengths and limitations.
  • Software projects often rely on external factors such as third-party services, regulatory requirements, or integration with other systems. Managing these dependencies and estimating their costs accurately adds complexity to project costing.
  • Identifying and mitigating risks associated with technology, market changes, or project execution is crucial but adds another layer of complexity to cost estimation. Contingency planning for unforeseen events impacts cost projections.
  • Unlike physical products, measuring progress in software development is often qualitative (e.g., functionality delivered, bugs fixed) rather than quantitative (e.g., units produced). This makes tracking and estimating costs more abstract and subjective.
  • Fluctuations in economic conditions, currency exchange rates, and market dynamics can impact project costs, especially for global teams or projects involving international transactions.


In summary, costing for software projects is challenging due to the dynamic nature of software development, the interplay of technical and non-technical factors, and the need for adaptive estimation techniques to account for uncertainty and change throughout the project lifecycle.