Better Time Estimation for Software Projects
Every team member and project is different. Planning can be really hard to get right.
There are a few things to consider when estimating the time for a project: complexity, dependencies, risks, testing, and a buffer.
The Formula
Total Estimated Hours = Base Effort x Complexity x Dependencies x Experience x Risk x Tests x (1 + Buffer Percentage)
- Base Effort: The initial time estimate for the task in its simplest form before accounting for any factors. If all conditions were ideal, this is the raw, unadjusted time needed for the project. Also called the “happy path.”
- Complexity: Adjusts the base effort based on the task’s difficulty or complexity. Higher complexity increases the effort (multiplier > 1), while low complexity decreases it (multiplier < 1).
- Dependencies: Accounts for how much the task relies on external systems, teams, or components. More dependencies increase the likelihood of delays or coordination issues, raising the effort.
- Experience: Adjusts the effort based on the team’s familiarity and skill with the technologies or domain. A less experienced team increases the effort, while a more experienced team decreases it.
- Risk: Reflects potential uncertainties or challenges that could arise during the project. High-risk tasks require more time due to the need for risk mitigation and dealing with possible issues.
- Tests: Accounts for the level of testing required to ensure the project meets quality standards. Extensive testing increases the effort needed, while minimal testing reduces it.
- Buffer Percentage: Adds a contingency to the total estimated effort to account for unforeseen delays or challenges. The buffer is calculated as a percentage of the adjusted effort and added on top for safety.
Example Projects
Booking a Restaurant Reservation
- Base Time Estimate: 120 Hours
- Complexity: High (150%)
- Dependencies: Some (110%)
- Experience: Medium (100%)
- Risk: Low (100%)
- Tests: High (130%)
- Buffer: Medium (15%)
- Adjusted: ~296 Hours
This project involves developing a system that enables customers to book restaurant reservations seamlessly. The complexity is high because it requires real-time synchronization of table availability, handling various booking scenarios (such as group reservations and special requests), and integrating with external platforms like third-party reservation services or payment gateways. There are some dependencies, mainly on external APIs and the restaurant’s internal systems, for up-to-date availability and scheduling.
The team’s experience is medium. They have prior experience with web application development and some familiarity with reservation systems but may be working with new APIs or integrating with unfamiliar third-party services. This moderate level of experience means there might be a learning curve, but the team is generally capable of handling the project’s requirements.
The risk is low since reservation systems are well-established and the technologies are mature, reducing the likelihood of unexpected challenges. However, the testing requirements are high to ensure reliability and a flawless user experience; the system must handle edge cases, prevent double bookings, and maintain data integrity. A medium buffer accommodates minor unforeseen issues, such as additional time needed to learn new APIs or adjust to integration challenges.
2. Creating a Restaurant Data Model
- Base Time Estimate: 40 Hours
- Complexity: Medium (120%)
- Dependencies: Low (110%)
- Experience: High (90%)
- Risk: Medium (120%)
- Tests: High (130%)
- Buffer: Medium (15%)
- Adjusted: ~85 Hours
This project focuses on designing the underlying data model for a restaurant application, defining how entities like menus, dishes, orders, customers, reservations, and staff are structured and related within a database. The complexity is medium because, while data modeling is a standard practice, it demands careful planning to create a scalable and efficient schema.
Dependencies are low, as the task is largely self-contained with minimal reliance on external systems. The team’s experience is high — they are well-versed in data modeling techniques and have substantial knowledge of database design principles. Additionally, they possess strong domain knowledge of the restaurant industry, which helps accurately represent real-world relationships in the data model.
The risk is medium since an improper data model could lead to performance issues or difficulties in adding new features in the future. Testing is high because the data model is foundational; it requires rigorous validation to ensure all relationships, constraints, and data integrity rules function correctly. A medium buffer is allocated to allow for iterative model refinement based on feedback and to address any complexities discovered during development.
3. Deploying Production and Staging Infrastructure for the Restaurant App
- Base Time Estimate: 80 Hours
- Complexity: High (150%)
- Dependencies: High (130%)
- Experience: Low (120%)
- Risk: High (150%)
- Tests: High (130%)
- Buffer: High (20%)
- Adjusted: ~438 Hours
This project involves setting up production and staging environments for the restaurant application, ensuring that they are robust and secure and mirror each other for effective testing and deployment. The complexity is high due to the need to configure various components such as servers, databases, networking, load balancers, continuous integration/continuous deployment (CI/CD) pipelines, and security protocols.
Dependencies are high because the deployment relies on multiple external services, including cloud providers (like AWS, Azure, or Google Cloud), third-party integrations (such as payment gateways or notification services), and possibly coordination with other teams (like security or compliance). The team’s experience is low in this area — they may have limited prior experience with cloud infrastructure, DevOps practices, or the specific tools and platforms required for this project. This lack of expertise introduces a significant learning curve and increases the potential for configuration errors.
The risk is high since misconfigurations can lead to severe consequences like application downtime, security vulnerabilities, or data loss. Therefore, testing is also high; comprehensive testing is essential to validate the infrastructure setup, including performance testing, security assessments, and failover simulations. A high buffer is included to manage potential delays due to the complex nature of infrastructure work, the need to troubleshoot unexpected issues, and the additional time required for the team to ramp up their skills in unfamiliar technologies.
Conclusion
Estimating time for software projects can be challenging due to factors that influence the total effort required. Project managers can create more accurate and reliable time estimates by using a structured formula that accounts for complexity, dependencies, experience, risk, tests, and a buffer percentage. These dimensions provide a comprehensive view of the potential hurdles and adjustments needed to deliver a successful project.
No two projects are the same, and each factor can vary based on the team’s experience and the specific project context. By carefully analyzing and applying multipliers for each dimension, you can better prepare for expected and unexpected challenges, ultimately leading to more realistic timelines and successful project outcomes. Time estimation may never be perfect, but this approach makes it much more manageable.
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The Spreadsheet
Here’s the example spreadsheet for you to copy, modify, and use! https://docs.google.com/spreadsheets/d/1lsG9CqAEkgEG82vEgTLeNtBZBFPYyZvfeyiNeHfX-6w/edit?usp=sharing
