Contributor Milestone Persona

Contributors may resonate with open source projects based on upstream or downstream ecological interests, personal development, etc., and engage in community contributions. They may stay in the community for a long time, gradually fade away, or even be reawakened. Based on these scenarios, we define various states of contributors in the community, forming the Milestone Persona Model. This model is inspired by a state machine, where contributors can transition between states, but they can only be in one state at a time. This ensures decoupling of behavior and is more conducive to observing contributors' actions in the community.

Based on contributors' contribution amount, contribution frequency, contribution type, and their states in the community, and considering the time period of contributors' milestones, we divide contributors' milestones into the above 5 states (Figure 5): no state, visitors, regulars, core, and silent. Taking the annual period as an example, here are the definitions of the five states:

• Core: Refers to contributors who complete 50% of all domain contributions in the current year (excluding observer-type contributions such as stars, forks, and watches). These contributions are completed by at least one group of people, referred to as core contributors. All domain contributions are not weighted, only the count is considered.
• Regulars: After excluding the 50% contributions made by core contributors, the next 30% contributions (excluding observer-type contributions such as stars, forks, and watches), completed by at least one group of people. These contributors have been involved in contributions for at least 9 months in the current year and are referred to as regular contributors.
• Visitors: After excluding core and regular contributions, contributors involved in the remaining community contributions (including observer-type contributions, i.e., stars, forks, and watches) are referred to as visitors.
• Silent: Contributors who were active as core, regular, or visitors in the previous year but have made no contributions in the current year.
• No State: People who have never made contributions in the community.

Metrics in the Metrics Model

Core Contributor Count

• Definition: How many active core contributors in the last 90 days.
• Weight: 20%
• Threshold: 20

Core Contribution Count

• Definition: The number of contributions per capita for contributors in the active core in the last 90 days.
• Weight: 30%
• Threshold: 200

Regular Contributor Count

• Definition: How many active regular contributors in the last 90 days.
• Weight: 12%
• Threshold: 100

Regular Contribution Count

• Definition: The number of contributions per capita for contributors in the active regular in the last 90 days.
• Weight: 18%
• Threshold: 25

Visitor Contributor Count

• Definition: How many active visitor contributors in the last 90 days.
• Weight: 8%
• Threshold: 2000

Visitor Contribution Count

• Definition: The number of contributions per capita for contributors in the active visitor in the last 90 days.
• Weight: 12%
• Threshold: 5

Metric Model Algorithm

Weight

We use AHP to calculate weight of each metric.

AHP Input Data

Metric Name	Core Contributor Count	Core Contribution Count	Regular Contributor Count	Regular Contribution Count	Visitor Contributor Count	Visitor Contribution Count
Core Contributor Count	1.000	0.667	1.667	1.111	2.500	1.667
Core Contribution Count	1.500	1.000	2.500	1.667	3.750	2.500
Regular Contributor Count	0.600	0.400	1.000	0.667	1.500	1.000
Regular Contribution Count	0.900	0.600	1.500	1.000	2.250	1.500
Visitor Contributor Count	0.400	0.267	0.667	0.444	1.000	0.667
Visitor Contribution Count	0.600	0.400	1.000	0.667	1.500	1.000

AHP Analysis Result

Metrics Name	Eigenvector	Weight
Core Contributor Count	1.200	20.000%
Core Contribution Count	1.800	30.000%
Regular Contributor Count	0.720	12.000%
Regular Contribution Count	1.080	18.000%
Visitor Contributor Count	0.480	8.000%
Visitor Contribution Count	0.720	12.000%

Consistency Test Results

Largest Eigenvalue	CI Value	RI Value	CR Value	Consistency Test
6.000	0.000	1.260	0.000	PASS

Threshold

The threshold we chose is based on the big-data observations from different types of open source projects.

References

• Reflections on the Evaluation and Measurement of Open Source Ecosystem (1) — Evolution and Trends
• Reflections on the Evaluation and Measurement of Open Source Ecosystem (2) - The Multidimensional Space of Evaluation Systems
• Reflections on the Evaluation and Measurement of Open Source Ecosystem (3) - Dynamics and Statics of Contributors

Contributors

Frontend

• Shengxiang Zhang
• Feng Zhong
• Xingyou Lai

Backend

• Yehui Wang
• Shengxiang Zhang
• Shengbao Li
• Huatian Qin

Metric Model

• Yehui Wang
• Liang Wang
• Shengbao Li