Introduction

Test effort estimation is a crucial aspect of software testing that involves predicting the time and resources required to achieve the testing objectives. Accurate estimation ensures efficient resource allocation, cost control, and timely delivery of software products. However, estimations are based on assumptions and are inherently subject to uncertainty. Breaking large tasks into smaller ones often improves accuracy.

Several estimation techniques can help teams forecast testing efforts more effectively. This blog explores four commonly used methods: Estimation Based on Ratios, Extrapolation, Wideband Delphi, and Three-Point Estimation.

1. Estimation Based on Ratios

This metrics-based technique leverages historical data from previous projects to determine standard effort ratios. Organizations analyze past projects to derive development-to-test effort ratios, which can then be used to estimate the effort for new projects.

Example:

If the previous project had a development-to-test effort ratio of 3:2 and the current project is expected to require 600 person-days of development effort, the estimated test effort would be:

(600 × 2) / 3 = 400 person-days

This technique works best when an organization has a robust historical dataset.

2. Extrapolation

Extrapolation is another metrics-based technique where early measurements in a project help predict future effort requirements. This technique is particularly useful in iterative software development life cycles (SDLCs), such as Agile.

Example:

If a team has completed three sprints and the average test effort per sprint is 50 hours, they can estimate that the remaining five sprints will require:

50 × 5 = 250 hours

This method helps refine estimates as more real-world data becomes available.

3. Wideband Delphi

Wideband Delphi is an expert-based technique that involves multiple rounds of estimation by experienced team members. Each expert provides an estimate individually, and deviations outside a predefined range are discussed collectively. The process repeats until consensus is reached.

Steps in Wideband Delphi:

1. Experts provide independent estimates.
2. If estimates vary significantly, a discussion follows.
3. Experts adjust their estimates based on feedback.
4. Iterations continue until a consensus is achieved.

Planning Poker, commonly used in Agile development, is a variant of Wideband Delphi where estimations are made using numbered cards representing effort size.

4. Three-Point Estimation

This method incorporates three different estimates to account for uncertainty:

• Optimistic estimate (a) — The best-case scenario.
• Most likely estimate (m) — The most realistic prediction.
• Pessimistic estimate (b) — The worst-case scenario.

The final estimate (E) is calculated using the formula:

E = (a + 4m + b) / 6

The standard deviation (SD) is:

SD = (b — a) / 6

Example:

If a testing task is estimated as:

• Optimistic (a) = 6 hours
• Most likely (m) = 9 hours
• Pessimistic (b) = 18 hours

Then the estimated effort is:

E = (6 + 4(9) + 18) / 6 = 10 hours

The uncertainty range is calculated as:

SD = (18–6) / 6 = 2 hours

Thus, the test effort is estimated as 10 ± 2 hours, meaning it will likely take between 8 to 12 hours.

Choosing the Right Estimation Technique

The choice of estimation technique depends on project size, available data, and the development methodology:

• Metrics-based approaches (Ratios, Extrapolation) work well when historical data is available.
• Expert-based techniques (Wideband Delphi, Three-Point Estimation) are useful when past data is limited or the project is unique.
• Hybrid approaches combining both techniques often yield more reliable results.

Conclusion

Test effort estimation is essential for efficient test planning and resource management. While no technique is 100% accurate, leveraging historical data, expert judgment, and mathematical models can improve accuracy. Choosing the right method depends on project characteristics and team expertise. By continuously refining estimates based on actual progress, teams can enhance predictability and optimize testing efforts.