openrank:
OpenRank is a measure of the value orientation of an open-source project. It focuses on collaboration and recognition within the developer community. The value assigned to an individual is based on their level of collaboration, participation in issues and pull requests (PRs), and the recognition they receive through likes on both the main content and comments. The higher the collaboration, the more valuable the issue/PR, and the greater the recognition received, the higher the overall value assigned. Additionally, contributing more through submitting issues and PRs or participating in discussions also contributes to a higher assigned value.
summary:
OpenRank is a metric that evaluates the value orientation of an open-source project, specifically focusing on collaboration, recognition, and contribution. It assigns value to individuals based on their level of collaboration, participation in issues and PRs, likes received on both main content and comments, and overall contribution. By analyzing the OpenRank data, we can assess the collaborative and community-driven nature of the project.
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activity:
Activity is a statistical indicator that measures the level of collaboration and engagement within an open source project. It takes into account five key collaborative events: issue comments, opened issues, opened pull requests, reviewed pull requests, and merged pull requests. The indicator data shows the activity score for each month, indicating the overall level of activity within the project. Based on the provided data, we can observe the following insights: - The activity score for the project remained relatively stable from October 2022 to December 2022, with values around 57-58. - In January 2023, there was a drop in the activity score to 56.2, suggesting a decrease in collaboration events compared to the previous months. - However, in February 2023, the activity score significantly increased to 123.31, indicating a surge in collaborative activities. - From March 2023 to September 2023, the activity score remained at a lower level ranging from 30 to 39, indicating a decrease in overall project activity during this period. It is important to note that the activity score alone does not provide information about the quality or impact of the collaborative events. Further analysis can be done by considering other indicators and metrics to gain a deeper understanding of the project's development and community engagement. Reference: [Link](https://blog.frankzhao.cn/how_to_measure_open_source_1/)
summary:
The activity indicator measures the level of collaboration and engagement within the open source project. The provided data shows fluctuations in the activity score over time, indicating variations in the project's overall level of activity. Notable observations include a significant increase in activity in February 2023 and a lower activity level from March 2023 to September 2023. It is important to consider additional metrics and indicators to gain a comprehensive understanding of the project's development and community engagement.
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bus_factor_detail:
The Bus Factor is a metric that measures the minimum number of contributors who are responsible for 50% of the contributions to a project. It provides an indication of how many contributors the project can lose without stalling. In this particular project, we have data for the Bus Factor for each month starting from October 2022 to September 2023. The data consists of lists of contributors and the number of contributions they have made. For example, in October 2022, the contributors and their contributions were: zhicheng-ning (6), LinuxSuRen (4), xgdyp (8), yoyo-wu98 (5), frank-zsy (7), and lhbvvvvv (4). Based on the available data, it is observed that the number of contributors and their contributions vary from month to month. For instance, in February 2023, there was a significant increase in the number of contributors and contributions, with multiple contributors making only one or two contributions. This suggests a more distributed contribution pattern during that month. It is important to note that the Bus Factor is a dynamic metric that can change over time as contributors come and go. Therefore, regular monitoring and analysis of this metric can help project maintainers understand the project's resilience to contributor turnover and identify any potential risks to the project's sustainability.
summary:
The Bus Factor is a metric that measures the minimum number of contributors who are responsible for 50% of the contributions to a project. In this project, the Bus Factor has been tracked from October 2022 to September 2023, showing variations in the number of contributors and their contributions over time. Regular monitoring of the Bus Factor can help project maintainers identify any potential risks to the project's sustainability.
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activity_details:
The "activity_details" indicator represents the activity level of the open-digger project. It is a statistical metric that takes into account five collaborative event behaviors: issue comment, open issue, open PR, review PR, and PR merged.
summary:
The open-digger project has a consistent level of activity, with contributions from multiple contributors. The project receives regular issue comments, and there is active participation in opening issues, PRs, reviewing PRs, and merging them.
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attention:
The "attention" indicator measures the activity level of a GitHub repository. It is a statistical metric proposed by the X-lab lab, which takes into account both the number of stars and forks as indicators of social collaboration behaviors on GitHub. Analyzing the data provided, we can see that the "attention" value for the repository has been consistently decreasing over time. In October 2022, the value was 27, but it dropped to 15 in November 2022, further decreased to 7 in December 2022, and continued to decrease in the following months. In 2023, the "attention" value remained relatively low, fluctuating between 5 and 20. The lowest point was reached in August 2023, with a value of 3. This indicates a decline in the social collaboration and interest in the repository. The decreasing trend in the "attention" value suggests that the repository is experiencing a decline in popularity and engagement. It could be that there are fewer contributors, less active discussions, or a decline in overall interest from the developer community. This decline might indicate a need for revitalization efforts, such as improving project documentation, engaging with the community, or introducing new features to regain attention and attract more contributors.
summary:
The "attention" indicator for the GitHub repository shows a consistent decrease in activity over time. This decline in the "attention" value suggests a decline in popularity and engagement with the repository. Revitalization efforts, such as improving project documentation, engaging with the community, or introducing new features, might be necessary to regain attention and attract more contributors.
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stars:
The "stars" indicator represents the number of stars per month for the project. Based on the provided data, the number of stars has fluctuated over time. In September 2022, there were 10 stars for the project, which decreased to 9 in October and November 2022. In December 2022, the number of stars dropped to 3. From January 2023 to June 2023, the number of stars varied between 1 and 12. It is interesting to note that there was a spike in the number of stars in March 2023, with 12 stars, which is the highest count among the available data. However, the number of stars decreased in the following months, reaching a low of 2 stars in September 2023. Overall, the trend in the number of stars suggests some fluctuation and variation in the popularity and engagement with the project over time.
summary:
The "stars" indicator represents the number of stars per month for the project. The data shows some fluctuation and variation in the number of stars, indicating changes in the project's popularity and engagement over time.
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technical_fork:
The indicator "technical_fork" measures the number of copies of a project on the same code development platform. It indicates the presence of distributed version control copies of a project.
summary:
Based on the data provided for the "technical_fork" indicator, the number of technical forks has been fluctuating over time. In September 2022, there were 3 technical forks, which increased to 9 in October 2022. However, it decreased to 2 in December 2022 and January 2023, and then increased again in the following months. It is important to monitor the number of technical forks as it can reflect the popularity and collaboration potential of a project. A higher number of technical forks may indicate a larger community contributing to the project, while a decreasing number may suggest a lack of ongoing development or decreased interest in the project. Further investigation is needed to understand the reasons behind these fluctuations and the impact on the project's overall development.
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issues_new:
The "issues_new" indicator represents the number of new issues created in the project over time. According to the CHAOSS community, this indicator is used to track the growth of issues in a project. Analyzing the data provided, we can observe that the number of new issues has fluctuated over the given time period. In October 2022, there were 20 new issues reported. The number increased to 29 in November and then decreased to 17 in December. In 2023, the number varied between 11 and 20, with a slight downward trend towards the end of the period. It is crucial to monitor the trend of new issues as it provides insights into the project's development process and potential areas that require attention. The project should investigate the causes behind spikes or drops in new issues to ensure efficient issue management.
issues_closed:
The "issues_closed" indicator tracks the number of issues that have been resolved or closed in the project. It is another metric proposed by the CHAOSS community to evaluate the project's issue management effectiveness. Analyzing the provided data, we can observe that the number of closed issues has also varied over time. In October 2022, 18 issues were closed, which increased to 30 in November and further increased to 28 in December. In 2023, the number ranged between 9 and 34, indicating some fluctuations in the resolution rate. It is essential for the project to monitor and analyze the closure rate of issues to ensure timely resolutions. By identifying patterns in the data, the project can take necessary actions to improve its issue resolution process.
issue_comments:
The "issue_comments" indicator measures the total number of comments made on issues in the project over time. This metric, proposed by X-lab, provides insights into the level of engagement and collaboration among project contributors. Analyzing the given data, we can see that the number of issue comments has also varied throughout the defined period. In October 2022, there were 97 comments made on issues, which increased to 165 in November and further increased to 205 in December. In 2023, the number ranged from 37 to 191, showing some fluctuations. Monitoring issue comments is important as it reflects the level of community involvement and collaboration within the project. A higher number of comments may indicate active discussions and problem-solving, while a lower number may require efforts to engage the community.
summary:
Based on the analysis of the provided indicators, several insights can be drawn. The number of new issues, closed issues, and issue comments has shown fluctuations over time. By monitoring these indicators, the project can gain insights into the growth, resolution, and engagement levels within the community. It is crucial for the project to analyze these metrics further to identify any patterns or trends that may impact the project's overall performance. By addressing any issues or areas of improvement highlighted by these indicators, the project can enhance its development process, issue management, and community engagement.
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change_requests:
The "Change Requests" metric measures the number of Pull Requests (PRs) in OpenDigger. From the data provided, we can observe the following trends: - The number of PRs in OpenDigger increased from 16 in October 2022 to 34 in January 2023. - After reaching a peak in January 2023, the number of PRs decreased to 9 in June 2023. - There was a slight increase in PRs from June to August 2023, with a total of 12 PRs in August. - However, in September 2023, the number of PRs decreased drastically to 4, indicating a significant drop in the contribution or collaboration activities.
change_requests_accepted:
The "Change Requests Accepted" metric measures the number of merged PRs in OpenDigger. Based on the provided data, the following insights can be derived: - The number of merged PRs in OpenDigger increased from 18 in October 2022 to 35 in January 2023, showing a positive trend. - However, from January 2023 to February 2023, there was a decline in merged PRs, dropping from 35 to 28. - After the decline, the trend continued to decrease with some fluctuations. By September 2023, the number of merged PRs decreased to 7, which is significantly lower than the previous months. - The decline in merged PRs suggests a potential decrease in collaboration or review activities for the project.
change_requests_reviews:
The "Change Requests Reviews" metric measures the number of PR reviews in OpenDigger. Analyzing the data provided, we can draw the following conclusions: - In the early months of the data (July, August, and September 2022), the number of PR reviews was relatively low, ranging from 2 to 16. - From October 2022 to December 2022, there was a substantial increase in PR reviews, with the highest peak of 25 reviews in December. - However, after December 2022, there was a significant decrease in the number of PR reviews. The trend continued to decline with minor fluctuations, reaching the lowest point of 2 reviews in March 2023. - From March 2023, there was a slight increase in PR reviews, but the numbers remained relatively low compared to the previous period. - The decrease in PR reviews suggests a potential decrease in the thoroughness of code review or lower involvement of reviewers in the project.
summary:
The analysis of the provided indicators reveals several insights into the OpenDigger project: - The number of PRs in OpenDigger showed a fluctuating trend, with a peak in January 2023 but a significant drop in September 2023. - The number of merged PRs in OpenDigger also exhibited a decrease, indicating a potential decrease in collaboration or contribution activities. - The number of PR reviews in OpenDigger showed a similar trend to merged PRs, with a decline in the thoroughness of code reviews or lower reviewer involvement. Overall, these indicators suggest a potential decline in activity and collaboration within the OpenDigger project, which may require further investigation and analysis to identify underlying causes and propose improvements.
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participants:
The participants indicator tracks the number of developers who have generated log behaviors in the project. From the data provided, we can observe the following trends:
new_contributors_detail:
The new_contributors_detail indicator measures the number of developers who have contributed code for the first time in the project. Based on the data provided, we can draw the following conclusions:
inactive_contributors:
The inactive_contributors indicator tracks the number of developers who have not contributed code to the project for a certain period of time. The data provided yields the following observations:
summary:
The analysis of the provided indicators reveals the following insights for the open source project:
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active_dates_and_times:
The "Active Dates and Times" indicator measures the activity of developers in the community based on dates and times. It provides a temporal distribution of their activity throughout the months. The indicator data includes an array of values where each element represents the number of active developers on a specific day and time within a month. Upon analyzing the data, the following patterns can be observed: - There is a general trend of low activity during weekends and holidays, with fewer developers being active. - Activity tends to peak in the middle of the month, especially around weekdays. - There are specific days with higher activity, potentially indicating code releases, meetings, or events that attract more developer attention. It is worth noting that the data indicates the number of active developers but does not provide information about the specific activities they are engaged in. Therefore, it is recommended to combine this indicator with other metrics to gain a more comprehensive understanding of community dynamics and engagement patterns.
summary:
The "Active Dates and Times" indicator provides insights into the temporal distribution of developer activity within the community. Analyzing the data reveals patterns of activity, with lower activity levels during weekends and holidays, and increased activity during weekdays, especially in the middle of the month. It is important to consider this indicator in conjunction with other metrics to gain a holistic understanding of community dynamics and engagement patterns.
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issue_response_time:
Issue Response Time is a metric that measures the time it takes for issues in a project to receive a response after being raised. It is an important indicator of how quickly the project team addresses user concerns and resolves issues. From the provided data, we can observe the following trends: - The average issue response time has varied over the months, ranging from 2.12 hours to 110.79 hours. - The median issue response time is not provided directly in the data, but we can calculate it using the values of the 50th percentile (quantile2). - The 50th percentile (quantile2) ranges from 0.5 hours to 226.75 hours, indicating that half of the issues received a response within this time range. - The 75th percentile (quantile3) ranges from 1.25 hours to 317 hours, indicating that 75% of the issues received a response within this time range. - The maximum issue response time (quantile4) ranges from 18 hours to 317 hours, indicating the longest time it took for an issue to receive a response. These insights can help the project team understand the historical trends and performance of issue response time, identify areas for improvement, and set targets for response time goals.
summary:
The analysis of the Issue Response Time indicator data reveals the variability of response time for issues raised in the project. The insights obtained can guide the project team in understanding historical performance, identifying areas for improvement, and setting response time goals.
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issue_resolution_duration:
The issue resolution duration is a metric that measures the time it takes to resolve issues in a project from the moment they are raised to the moment they are closed. It provides insights into the efficiency of issue resolution process. Based on the provided data, the average duration of issue resolution has decreased over time. In October 2022, the average duration was 16.0 days, which decreased to 14.67 days in November 2022, and further decreased to 5.0 days in September 2023. However, it is important to note that there was a spike in April 2023, where the average duration increased to 21.69 days. The data also provides information on different levels of issue resolution duration, represented by the number of different levels (e.g., levels of support, severity, or priority) within each month. The provided data shows that these levels fluctuate over time, suggesting variations in the complexity of issues being resolved. Additionally, the data includes quantiles that provide a distribution of issue resolution duration. The 25th percentile (quantile 1) represents the point below which 25% of the resolved issues fall, while the 75th percentile (quantile 3) represents the point below which 75% of the resolved issues fall. The data shows that the majority of resolved issues (between the 25th and 75th percentiles) have a duration of around 3 to 9 days, with some outliers taking longer. Overall, the provided data suggests that the issue resolution duration in the project has improved, with a decrease in the average duration over time. However, there are still occasional spikes in duration, indicating potential areas of improvement in the issue resolution process.
summary:
The analysis of the provided indicator data on issue resolution duration suggests that the project has experienced a decrease in the average duration of issue resolution over time. However, there are occasional spikes in duration, indicating areas for potential improvement.
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error:
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change_request_response_time:
The "change_request_response_time" indicator measures the average, median, etc. response time of PRs in a project from the time they were raised to the first time they were responded to. This metric is important in understanding the efficiency and responsiveness of the project's development process. Analyzing the provided data, we can observe the following trends: - The average response time fluctuated between 0.82 days and 97.52 days over the period from October 2022 to September 2023. This indicates significant variations in the project's responsiveness to change requests. - The response time quantiles provide further insight into the distribution of response time. The 25th percentile (quantile1) remained relatively low throughout the period, indicating that a significant portion of change requests received a response within a short time. However, the 75th percentile (quantile3) and 90th percentile (quantile4) show that there were instances where some change requests experienced significantly longer response times, ranging from 1.25 days to 270 days. It is important for projects to strive for shorter response times to ensure a timely feedback loop with contributors. This helps in maintaining an active and engaged community and encourages continuous improvement. The project should aim to minimize outliers in response time and keep the majority of change requests within an acceptable timeframe.
summary:
The "change_request_response_time" indicator provides insights into the average and distribution of response times for change requests in the project. From the analysis of the provided data, we can conclude that the project has experienced significant variations in response times, ranging from very short durations to much longer durations. It is crucial for the project to focus on reducing outliers and maintaining a timely feedback loop with contributors to foster an active and engaged community.
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change_request_resolution_duration:
The "change_request_resolution_duration" indicator refers to the duration it takes for change requests (also known as pull requests or PRs) to be resolved in a project. It measures the time from when the PR is opened to when it is closed. The average duration of resolving PRs has shown some variations throughout the recorded period. It started at 1.06 months in October 2022, increased to 1.6 months in November 2022, and further increased to 2.77 months in December 2022. The duration then decreased to 0.82 months in January 2023, and further decreased to 0.5 months in February 2023. However, it increased again to 2.36 months in March 2023, then to 2.62 months in April 2023. In May 2023, the duration significantly increased to 3.54 months. Subsequently, it decreased to 1.1 months in June 2023 and 0.67 months in July 2023. In August 2023, there was a significant increase in duration, reaching 6.85 months. Finally, in September 2023, the duration decreased to 1.0 month. The "levels" data provides additional insights into the resolution duration of PRs. It includes the number of PRs at different levels of the resolution duration. For example, in October 2022, there were 16 PRs that were resolved within 0 days, 1 PR that took 1 day, and no PRs that took 2 or 3 days. These numbers vary for each month. The "quantile" data provides information about the distribution of resolution durations. Each quantile represents a specific percentage of PRs that are resolved within a certain duration. For example, in October 2022, the 0th quantile indicates that no PRs were resolved within 0 days, the 1st quantile indicates that no PRs were resolved within 0 days, the 2nd quantile indicates that 1 PR was resolved within 1 day, the 3rd quantile indicates that 1 PR was resolved within 1 day, and the 4th quantile indicates that 10 PRs were resolved within 10 days. These quantile values vary for each month.
summary:
The change_request_resolution_duration has shown variations in the duration it takes for change requests to be resolved. The average resolution duration fluctuated over the recorded period, with some months experiencing longer durations and others shorter durations. The levels and quantile data provide additional insights into the distribution of resolution durations.
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change_request_age:
The "change_request_age" indicator measures the average age of open pull requests (PRs) in a given time period. It provides insights into how long PRs have been left open without being merged or closed. This metric is useful for evaluating the efficiency of the pull request review and merge process. From the provided data, the average age of open PRs has been increasing over time. In September 2022, the average age was 265.53 days, and it has steadily increased to 392.35 days in August 2023. The "levels" data in the indicator shows the number of PRs at different age ranges. It indicates that there is a consistent number of PRs that have remained open for a long time, with some PRs closing and reopening during the considered time period. The "quantile" data provides insight into the distribution of PR ages. The quantile0 represents the minimum age, while quantile4 represents the maximum age. The values between quantile0 and quantile1 represent the lower quartile, between quantile1 and quantile2 represent the median, between quantile2 and quantile3 represent the upper quartile, and between quantile3 and quantile4 represent the highest quartile. Based on the quantile data, it can be observed that the majority of open PRs have an age between the lower quartile and the upper quartile. However, there is a significant number of PRs that have remained open for a longer period, as indicated by the values between the upper quartile and the highest quartile. This suggests that there may be bottlenecks or inefficiencies in the pull request review and merge process, leading to a considerable number of PRs being left open for an extended period. It is recommended to investigate the reasons for these delays and take necessary actions to improve the efficiency of the review and merge workflow.
summary:
The analysis of the "change_request_age" indicator reveals that the average age of open PRs has been increasing over time. This indicates potential inefficiencies in the pull request review and merge process, resulting in a significant number of PRs being left open for extended periods. It is crucial to identify and address the bottlenecks to improve the overall efficiency of the workflow.
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code_change_lines_add:
The indicator "code_change_lines_add" represents the number of lines of code added in the project over time. The value for this indicator increases or decreases depending on the amount of code added in each month. According to the data, we can observe that the number of lines of code added in October 2022 was 1,656, which significantly increased to 52,042 in November 2022. In December 2022, there was a decrease in the number of lines of code added to 30,774. The trend continues in the following months with varying amounts of code added. It is worth noting that in September 2023, there was a surge in the number of lines of code added reaching 54,294. Based on this data, we can conclude that there are periods of intense development activity with significant code additions.
code_change_lines_remove:
The indicator "code_change_lines_remove" represents the number of lines of code removed in the project over time. This indicator indicates the amount of code that has been deleted in each month. According to the data, we can see that in November 2022, there was a significant amount of code removal, with 28,178 lines removed. The trend continues with varying amounts of code removal in the following months. In September 2023, there was a decrease in the number of lines of code removed to 273. This suggests that there are periods of code refactoring or restructuring happening in the project. It is also worth mentioning that the number of lines of code removed is generally lower compared to the number of lines of code added, indicating that the project is actively adding new features or functionality.
code_change_lines_sum:
The indicator "code_change_lines_sum" represents the net change in the number of lines of code in the project over time. It calculates the difference between the lines of code added and the lines of code removed. Positive values indicate an increase in code size, while negative values indicate a decrease. According to the data, we can observe that the net change in code size was positive in most months, indicating overall growth in the project. However, there are some months, like August 2023, where there was a decrease in code size (-4,282 lines). This suggests that there might have been significant code refactoring or removal of unused code during that month. In September 2023, there was a significant increase in code size with a net addition of 54,021 lines. This indicates a period of intense development activity with substantial additions to the codebase. Overall, this indicator provides insights into the evolution of the project in terms of code size.
summary:
The analysis of the code change indicators provides valuable insights into the development activity of the project. The "code_change_lines_add" indicator shows the number of lines of code added over time, highlighting periods of intense development with significant additions. On the other hand, the "code_change_lines_remove" indicator reflects the removal of code, suggesting code refactoring or restructuring. The "code_change_lines_sum" indicator provides the net change in code size, indicating overall growth with occasional decreases due to code refactoring. These indicators collectively give a comprehensive understanding of the evolution of the project in terms of code changes.
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