快速学习COSMIC方法之四：早期快速估算功能规模的方法

Summary of Approximate Estimation Methods for Software Sizing

Before diving into the detailed COSMIC method, it is important to understand how to estimate the size of software in the early stages of a project when requirements are not detailed enough for testing. Many companies use approximate estimation methods to determine project budgets and reduce the workload of measurement.

The principle of quick estimation is to analyze the correlation between historical coarse-grained requirements and actual size to find a conversion relationship. The scale of coarse-grained requirements can be based on various factors such as the number of functional processes, number of use cases, number of document pages, or number of developers, as long as there is a strong correlation with functional point size evidenced by historical data.

In the early stages and quick guides of COSMIC, four approximate estimation methods are introduced:

1. Average Size per Functional Process: Companies can use historical data to find a correlation between the number of functional processes and functional point size. By dividing the number of functional points by the number of functional processes, an average value 'a' is obtained, which is then used for future project size estimations.
2. Fixed-Size Classification Approximation: By counting functional points for each functional process and classifying them into size categories (e.g., large, medium, small), companies can estimate the average functional point size for each category. Significant differences between categories are necessary, and criteria can be established to help in classification.
3. Equal-Size Band Method: This more complex method involves sorting functional processes by size and dividing them into bands with equal cumulative functional point totals. Average sizes for each band are then calculated, and these categories can be used for future estimations with the help of objective criteria.
4. Average Use Case Method: Similar to the first method, it also calculates the ratio between functional processes and functional points but includes measuring the number of use cases. This method may offer lower accuracy and requires a standardized template for writing use cases.

It is vital to understand that approximate estimation relies on historical data. A company must first measure some typical projects using the standard COSMIC method to establish a baseline for these approximate estimation methods. These relationships need to be regularly calibrated with actual data to ensure that estimations are as close to reality as possible.

While industry benchmark data might seem appealing due to its convenience and lack of effort in data accumulation, it comes with inherent inaccuracies that may not reflect individual company nuances. Therefore, companies should not rely solely on industry data but should invest time in accumulating their own data to benefit from the "slow first, fast later" approach of rapid estimation methods.

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