Repo X-lab2017/open-digger Indicator Report

openrank:

OpenRank is a metric that measures the value and influence of developers in open source projects. It focuses on four key factors: collaborating with high-influencers, generating collaboration, earning recognition, and contributing more. The value assigned to a developer increases if they collaborate with developers who have high influence, such as participating in their issues or pull requests. This indicates that developers with higher influence bring higher value to the project. Another important factor is generating collaboration on issues and pull requests. The more collaboration an issue or pull request receives, the higher its value. Therefore, collaborating with other developers and encouraging discussions can increase your assigned value. To earn recognition, gaining "likes" on the main content of issues and pull requests is important. The more "likes" an issue or pull request receives, the higher its overall value. Additionally, "likes" on comments can make the comment author more valuable. Lastly, contributing more by submitting issues and pull requests or actively participating in discussions can increase your assigned value.


summary:

OpenRank is a metric that evaluates the value and influence of developers in open source projects. It considers factors such as collaborating with high-influencers, generating collaboration, earning recognition, and contributing more. By actively participating in issues and pull requests, collaborating with influential developers, engaging in discussions, and gaining recognition through "likes," developers can increase their assigned value.


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activity:

The "activity" indicator measures the level of collaboration and engagement in an open-source project. It is calculated based on five key collaborative events: issue comment, open issue, open PR, review PR, and PR merged. These events are essential for efficient communication, problem-solving, and code development within a project. Analyzing the data provided, the activity level of the project has varied over the past months. In January 2023, the activity level was 56.2, indicating a moderate level of collaboration and engagement. However, in February, the activity significantly increased to 123.31, suggesting a surge in project activities. In March, the activity level decreased to 39.06, indicating a decline in collaboration. This decrease could potentially be attributed to external factors such as vacations or the prioritization of other tasks. The activity level continued to fluctuate in the following months, with values of 52.43 in April, 48.92 in May, 30.43 in June, 31.38 in July, and 35.64 in August. These numbers suggest a relatively stable but lower level of collaboration compared to the peak in February. However, in September, the activity level slightly decreased to 33.29, indicating a slight dip in collaboration compared to the preceding months. It is essential to monitor the activity level closely as it reflects the overall engagement and progress of the open-source project. A consistent and high level of activity indicates a healthy and vibrant project community, while a decline in activity may require attention and efforts to re-engage contributors and maintain momentum.


summary:

The activity indicator measures the collaboration and engagement level in the open-source project. By analyzing the provided data, we observed fluctuations in the activity level over the months. The activity level reached its peak in February (123.31), suggesting a surge in project activities. However, it decreased in subsequent months, stabilizing at a relatively lower level. Monitoring the activity level is crucial to track the health and progress of the project community.


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bus_factor_detail:

The Bus Factor is a metric that visualizes how many contributors a project can lose before it stalls. It represents the smallest number of people who contribute to 50% of the project's code. Based on the data, we can observe the following insights: - In January 2023, the contributors with significant contributions were "frank-zsy" with 15 commits, "birdflyi" with 5 commits, "PureNatural" with 6 commits, and "xgdyp" with 5 commits. - In February 2023, there was a significant increase in contributors with various levels of contributions. "frank-zsy" had 14 commits, followed by "xgdyp" with 5 commits. Several contributors had 1 commit each. - In March 2023, "frank-zsy" had 6 commits, followed by "Zzzzzhuzhiwei" and "xgdyp" with 5 commits each. - In April 2023, "frank-zsy" had 7 commits, followed by "birdflyi" with 5 commits. "xgdyp" and "Zzzzzhuzhiwei" had 6 and 4 commits respectively. - In May 2023, the top contributors were "frank-zsy" and "xgdyp" with 7 and 6 commits respectively. "Zzzzzhuzhiwei" and "PureNatural" had 3 and 5 commits respectively. - In June 2023, "Zzzzzhuzhiwei" had the highest number of commits with 6, followed by "xgdyp" with 5 commits. - In July 2023, "frank-zsy" had 7 commits, followed by "xgdyp" with 4 commits. "birdflyi" and "bifenglin" had 3 commits each. - In August 2023, "frank-zsy" had 9 commits, followed by "Zzzzzhuzhiwei" with 5 commits. "birdflyi" had 4 commits. - In September 2023, "xgdyp" had the highest number of commits with 5, followed by "frank-zsy", "Zzzzzhuzhiwei", and "PureNatural" with 4 commits each. Understanding the Bus Factor helps the project understand its resilience and evaluate the potential impact of losing key contributors. It highlights the critical individuals who contribute significantly to the project's success and provides insights for future resource planning and collaboration efforts.


summary:

The Bus Factor metric analyzes the number of contributors required for 50% of the project's contributions. Based on the data, we observe variations in the contributors' commitment levels over time. Monitoring the Bus Factor enables the project to identify key contributors and evaluate the impact of their potential departure. This insight helps in resource planning and fostering collaboration within the project.


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activity_details:

The "activity_details" indicator measures the level of activity and collaboration in the open source project, taking into account five collaborative event behaviors: issue comment, open issue, open pull request (PR), review PR, and PR merged. The indicator data provides information on the number of events performed by each contributor in each month, along with their corresponding weights.


summary:

The project has shown consistent activity throughout the analyzed months, with varying degrees of participation from different contributors. The following insights can be drawn from the "activity_details" indicator data: - The contributor "frank-zsy" has consistently shown the highest level of activity, with a weighted activity score of 14.93 in January 2023 and 13.53 in February 2023. - The "open-digger-bot[bot]" and "xgdyp" contributors also demonstrate a high level of activity, consistently appearing in the top contributors list across multiple months. - There is a diverse group of contributors with varying levels of activity, indicating a healthy and collaborative open source community. - The activity level in the project has remained relatively consistent over time, with slight fluctuations observed in certain months. - The indicator data shows a balanced distribution of activity across the different collaborative event behaviors, suggesting active engagement in different aspects of the project. Overall, the project exhibits a healthy level of activity and collaboration, with a group of active contributors consistently participating in various aspects of the project.


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attention:

The "attention" indicator represents the activity of a GitHub repository, taking into account both the number of stars and forks it has received. The data provided consists of monthly values for the year 2023. The activity level for the repository starts at 5 in January 2023 and increases to a peak of 20 in March 2023. After reaching the peak, the activity gradually declines, reaching a value of 4 in September 2023. Overall, the activity of the repository shows a positive trend in the first few months, indicating increased interest and engagement from the GitHub community. However, the declining trend suggests a decrease in attention and interaction over time. It is important to closely monitor these metrics and consider strategies to maintain or increase attention on the repository.


summary:

The "attention" indicator provides insights into the activity level of a GitHub repository, considering both stars and forks. The data for the year 2023 indicates an initial increase in activity followed by a decline. It is recommended to monitor and strategize to maintain or increase attention on the repository.


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stars:

The number of stars per month for the project is as follows: - January 2023: 1 star - February 2023: 8 stars - March 2023: 12 stars - April 2023: 6 stars - May 2023: 2 stars - June 2023: 3 stars - August 2023: 3 stars - September 2023: 2 stars


summary:

The project's number of stars has varied over time, with a peak of 12 stars in March 2023. However, the number of stars has generally been low, with only 1 star in January 2023. There is also a gap in data for the month of July 2023. Overall, the project has not gained significant popularity based on the number of stars.


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technical_fork:

The "technical_fork" indicator measures the number of copies of a project on the same code development platform. It is a reflection of how many distributed version control copies exist for a project. By analyzing the data, we can observe the following trends: - In January 2023, the number of technical forks was 2. - In February 2023, the number of technical forks increased to 4. - In March 2023, the number of technical forks remained the same at 4. - In April 2023, the number of technical forks increased to 5. - In May 2023, the number of technical forks decreased to 3. - In June 2023, the number of technical forks decreased further to 2. - In July 2023, the number of technical forks remained the same at 2. - In September 2023, the number of technical forks decreased to 1. This indicates that there have been fluctuations in the number of technical forks over the months. It is important to monitor this indicator to understand the popularity and distribution of the project on different code development platforms. A rapid increase in the number of technical forks could indicate a growing interest in the project, while a decrease in forks may suggest a decline in popularity or consolidation of development efforts.


summary:

The "technical_fork" indicator provides insights into the number of distributed version control copies of a project on the same code development platform. By analyzing the data, we observed fluctuations in the number of technical forks over the months, indicating changes in the popularity and distribution of the project. Monitoring this indicator can help understand the project's community engagement and potential collaboration opportunities.


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issues_new:

The "issues_new" indicator represents the number of newly created issues in a project over time. From the data provided, it can be observed that the number of new issues started at 19 in January 2023 and gradually decreased to 5 in September 2023. This indicates a slight decrease in the number of new issues being reported in the project.


issues_closed:

The "issues_closed" indicator represents the number of closed issues in a project over time. Analyzing the data, it can be noted that the number of closed issues varied throughout the period. The highest number of closed issues was recorded in January 2023 with 34 closed issues, and the lowest was in April 2023 with 9 closed issues. Although there were some fluctuations, there is generally a decreasing trend in the number of closed issues.


issue_comments:

The "issue_comments" indicator represents the total number of issue comments in a project over time. Studying the provided data, it can be observed that the number of issue comments varied each month. The highest number of comments was observed in February 2023 with 191 comments, while the lowest number was in June 2023 with 37 comments. Overall, there is no clear trend in the number of issue comments.


summary:

The analysis of the indicators reveals that the number of newly created issues has slightly decreased over time, implying a potential improvement in the project's stability. Meanwhile, the number of closed issues exhibits fluctuations but generally follows a decreasing trend, indicating a continuous effort to resolve existing issues. The number of issue comments shows variations each month, without a clear trend. Monitoring these metrics helps in understanding the project's development and identifying areas that require attention.


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change_requests:

The "Change Requests" metric refers to the number of Pull Requests (PRs) in the OpenDigger repository on GitHub. This metric measures the level of collaboration and contribution within the project. From the provided data, it can be observed that the number of change requests fluctuated over the months in 2023, ranging from 4 to 34.


change_requests_accepted:

The "Change Requests Accepted" metric represents the number of PRs that have been successfully merged into the OpenDigger repository. This metric indicates the effectiveness of the review and acceptance process in the project. Analyzing the data, it can be observed that the number of accepted change requests varied from 7 to 35 during 2023.


change_requests_reviews:

The "Change Requests Reviews" metric measures the number of reviews performed on Pull Requests in the OpenDigger repository. This metric provides insights into the engagement and feedback from the project's contributors. Based on the data, it can be noted that the number of PR reviews ranged from 2 to 8 throughout 2023.


summary:

The analysis of the provided indicators for the OpenDigger repository in 2023 highlights the following: - The number of change requests ranged from 4 to 34, indicating varying levels of contribution and collaboration within the project. - The number of accepted change requests varied from 7 to 35, reflecting the effectiveness of the review and acceptance process in the project. - The number of PR reviews ranged from 2 to 8, indicating the amount of engagement and feedback from contributors on the pull requests.


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participants:

The "participants" indicator represents the number of developers who have generated log behaviors in the project. Based on the data provided, the number of participants has fluctuated over the months in 2023. In January, the project had 14 participants, which increased to 89 in February. However, it dropped to 16 in March and gradually increased to 15 in September.


new_contributors_detail:

The "new_contributors_detail" indicator measures the number of developers who have contributed code for the first time to the project. According to the data, there were new contributors in January, March, May, and September of 2023. In January, two new contributors, "tisonkun" and "bifenglin," joined the project. In March, "sy-records" and "yanchaomei" contributed code for the first time. May saw multiple new contributors, including "tc2000731," "stevending1st," and "tyn1998." Finally, "Peng99999" became a new contributor in September.


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. Looking at the data, the number of inactive contributors has been increasing steadily from January to August of 2023. In January, there were 14 inactive contributors, which gradually increased to 24 by August.


summary:

The analysis of the provided indicators reveals interesting insights about the project. The number of participants in the project fluctuated throughout the year, reaching its peak in February with 89 participants and its lowest point in June with 11 participants. On the other hand, the number of new contributors varied across different months, with new contributors joining in January, March, May, and September. Additionally, the number of inactive contributors has been increasing steadily from January to August. These insights highlight the growth and involvement of developers in the project, as well as the need to engage inactive contributors and encourage new contributions.


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active_dates_and_times:

The "active_dates_and_times" indicator measures the activity levels of developers in the community based on the dates and times they are active. The indicator data consists of the number of activities performed by developers for each month. From the data, we can observe that there is variation in the number of activities across different months. In particular, the months with the highest activity levels are January, February, and April. On the other hand, the months with the lowest activity levels are March, May, and September. Additionally, there are certain peaks and dips in activity within each month. For example, in January, there is a peak of activity around the middle of the month, followed by a dip, and then another peak towards the end of the month. These patterns may indicate specific events or initiatives that drive increased developer activity during those periods. Overall, the "active_dates_and_times" indicator provides insights into the dynamics of developer activity over time, allowing the community to identify trends and patterns that may inform resource allocation and decision-making.


summary:

The analysis of the "active_dates_and_times" indicator reveals variations in developer activity levels across different months. Certain months, such as January, February, and April, exhibit higher activity levels, while others, like March, May, and September, have lower activity levels. The indicator data also shows distinct peaks and dips within each month, suggesting specific events or initiatives that drive increased developer engagement. These insights can help the community understand the dynamics of developer activity and make informed decisions regarding resource allocation.


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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 their first response. It is an important indicator of the project's responsiveness to user concerns and its ability to address and resolve issues in a timely manner. Based on the provided data, the average issue response time for the project has varied over the months of 2023. In January, the average response time was 42.83, which increased to 110.79 in February. However, it decreased to 53.12 in March and further to 14.29 in April. In May, the response time increased slightly to 16.82 and then decreased to 32.38 in June. For the remaining months, the response time gradually reduced to 13.71 in July, 11.42 in August, and 9.67 in September. The levels of issue response time indicate the distribution of response times. In January, there were a total of 12 issues, with 8 issues having response times less than or equal to 8, 2 issues with response times between 8 and 16, 0 issues with response times between 16 and 32, and 2 issues with response times greater than 32. Similar distributions can be observed for the other months, showing variations in the responsiveness of the project. The quantiles provide additional insight into the response time distribution. The 0th quantile represents the minimum response time observed, which consistently remains at 0 throughout the months. The 1st quantile represents the lower quartile, or the 25th percentile of response times. In February, the lower quartile was 0.25, indicating that 25% of the issues received their first response within 0.25 units of time. In March and April, the lower quartiles were 0.75 and 1.0 respectively, indicating a slower response time for those months. The 2nd quantile represents the median response time, or the 50th percentile. The median response time varied between 0.0 and 88.0 across the months, suggesting a wide range of response times. The 3rd quantile represents the upper quartile, or the 75th percentile of response times. In February, the upper quartile was 226.75, indicating that 75% of the issues received their first response within 226.75 units of time. In April, the upper quartile reduced to 6.0, indicating a faster response time. The 4th quantile represents the maximum response time observed, which ranged from 258.0 to 26.0 throughout the months. Overall, the data indicates that the project's issue response time has varied over the months of 2023. There were periods of slower and faster response times, which may depend on various factors such as workload, team capacity, and prioritization. It is important for the project to monitor and analyze these response times to ensure timely resolution of user issues.


summary:

The analysis of the "issue_response_time" indicator data shows variations in the project's average response time for user issues over the months of 2023. The distribution of response times and the quantiles provide further insights into the project's responsiveness. It is crucial for the project to monitor and optimize its issue response time to ensure timely resolution of user concerns.


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issue_resolution_duration:

The issue_resolution_duration indicator measures the duration it takes to resolve issues in a project over a period of time. The average duration from the time an issue is raised to the time it is closed is as follows: - January 2023: 8.81 days - February 2023: 5.92 days - March 2023: 8.5 days - April 2023: 21.69 days - May 2023: 10.85 days - June 2023: 17.12 days - July 2023: 7.11 days - August 2023: 6.31 days - September 2023: 5.0 days


summary:

The issue_resolution_duration metric provides insights into the average and distribution of issue resolution duration in the project. It helps identify the average time taken to resolve issues and the variation in resolution durations across different quantiles.


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issue_age:

The indicator "issue_age" measures the length of time issues have been left open in the considered time period. It gives us an idea of the average age of open issues and the distribution of issue ages. The data provides information on the average issue age, levels of issue age, and quantiles of issue age. The average issue age fluctuated over the months, starting from 398.32 in January and gradually increasing to 530.36 in September. This suggests that, on average, issues remained open for a longer duration as time progressed. The "levels" data provides a breakdown of the number of issues across different age ranges. For example, in January, there were 3 issues with an age of less than 1 month, 2 issues with an age between 1 month and 2 months, 1 issue with an age between 2 months and 6 months, and 67 issues with an age greater than 6 months. The "quantile0" data represents the minimum issue age observed in each month. In September, the minimum issue age was 15, indicating that there were no very young issues. The "quantile1" data represents the 25th percentile of the issue age distribution. In January, 25% of the issues had an age of 226 days or less. This suggests that a significant portion of the issues had been open for a considerable duration. The "quantile2" data represents the median of the issue age distribution. In September, the median issue age was 531 days, indicating that half of the issues had been open for more than 531 days. The "quantile3" data represents the 75th percentile of the issue age distribution. In September, 75% of the issues had an age of 797 days or less. This suggests that a quarter of the issues had been open for a very long duration. The "quantile4" data represents the maximum issue age observed in each month. In September, the maximum issue age reached 1138 days, indicating the presence of some highly aged issues. Overall, the data shows that issues tend to remain open for a significant duration, with a gradual increase in the average issue age over time. This indicates a need for better issue management and more timely resolution of open issues.


summary:

Issue age is an important metric that reflects how long issues have been left open. The data analysis of the "issue_age" indicator suggests that issues tend to remain open for a significant duration, with the average age gradually increasing over time. This indicates the need for better issue management and more timely resolution of open issues in the considered time period.


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change_request_response_time:

The "change_request_response_time" indicator refers to the average, median, etc. of pull requests (PRs) in a project over a period of time from when they were raised to the first time they were responded to. It is a configurable parameter in OpenDigger. After analyzing the indicator data, we can draw the following insights: 1. Average response time: - In January 2023, the average response time for PRs was 97.52 days. - In February 2023, the average response time for PRs was 61.15 days. - In March 2023, the average response time for PRs was 0.67 days. - In April 2023, the average response time for PRs was 2.0 days. - In May 2023, the average response time for PRs was 1.92 days. - In June 2023, the average response time for PRs was 12.11 days. - In July 2023, the average response time for PRs was 13.82 days. - In August 2023, the average response time for PRs was 1.33 days. - In September 2023, the average response time for PRs was 0.75 days. 2. Levels of response time: - In January 2023, there were 18 PRs with a response time of 0 days, 0 PRs with a response time of 1 day, 0 PRs with a response time of 2 days, and 11 PRs with a response time more than 2 days. - Similarly, the levels of response time can be observed for each month. 3. Quantiles of response time: - The quantile values represent the distribution of the response time for PRs. - For example, in January 2023, the 0th quantile was 0.0 days, the 1st quantile was 0.0 days, the 2nd quantile was 1.0 day, the 3rd quantile was 248.0 days, and the 4th quantile was 270.0 days. - The quantile values can be examined for each month to understand the distribution. Based on these insights, it can be concluded that the average response time for PRs in the project varies each month. The levels and quantiles of response time provide a deeper understanding of the distribution of response time. Further analysis can be performed to determine the factors affecting the response time and identify areas for improvement.


summary:

The analysis of the "change_request_response_time" indicator data reveals that the average response time for pull requests (PRs) in the project varies each month. The levels and quantiles of response time provide a deeper understanding of the distribution and can be utilized to identify areas for improvement.


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change_request_resolution_duration:

The Change Request Resolution Duration indicator measures the time taken to resolve Pull Requests (PRs) in a project. It provides various metrics such as the average resolution duration, different quantiles, and levels of PRs. The indicator data consists of the average resolution duration over time, the levels of PRs, and the quantiles. From the average resolution duration data, we can see that the resolution duration varied throughout the year. In January, the average resolution duration was 0.82, which decreased to 0.5 in February, and then increased to 2.36 in March. This trend continued with a gradual increase in April (2.62), May (3.54), and June (1.1). In July, there was a slight decrease in the average resolution duration (0.67), but it significantly increased in August (6.85). Finally, in September, it decreased again to 1.0. The levels data shows the distribution of PRs based on different levels. For each month, the levels are represented as an array with four values: [high, medium, low, none]. From the data, we can observe the variations in the number of PRs per level. For example, in January, there were 32 PRs classified as high, 2 PRs classified as medium, and none classified as low or none. Similarly, the distribution of PRs across levels can be observed for the other months. The quantiles data provides information about the distribution of resolution durations. Each quantile represents a percentage of PRs that were resolved within a certain duration. For example, the quantile2 data indicates that 50% of PRs were resolved within a resolution duration of 1.0 in January, 2.0 in February, and so on. Overall, the Change Request Resolution Duration indicator provides insights into the time taken to resolve PRs in a project. By analyzing the data, we can identify trends, variations, and distribution patterns of resolution durations and PR levels. *Note: The data provided is for the year 2023 and is_raw is False for all data points, indicating the use of processed data.* [For more information about the metric-issue-resolution-duration, you can refer to the Chaoss community documentation](https://chaoss.community/metric-issue-resolution-duration/)


summary:

The Change Request Resolution Duration indicator provides insights into the duration of resolving Pull Requests (PRs) in a project. The analysis of the indicator data reveals trends, variations, and distribution patterns of resolution durations and PR levels over time. It is an important metric for evaluating the efficiency and effectiveness of the PR resolution process in an open source project.


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change_request_age:

The "change_request_age" indicator measures the age of pull requests (PRs) in an open source project. It indicates how long PRs have been left open during the considered time period. If a PR has been closed and re-opened within that period, it will be considered as having remained open since its initial opening date. The average change request age over the period is as follows: - January 2023: 364.56 days - February 2023: 333.37 days - March 2023: 330.0 days - April 2023: 358.52 days - May 2023: 388.05 days - June 2023: 418.05 days - July 2023: 394.04 days - August 2023: 392.35 days The levels of change request age can be analyzed by month and year. Each level represents the number of PRs falling into a specific age range. The age ranges are as follows: - 0-7 days - 8-30 days - 31-90 days - 90+ days The level of change request age for each month in 2023 is as follows: - January 2023: [0 PRs in the 0-7 days range, 1 PR in the 8-30 days range, 0 PRs in the 31-90 days range, 15 PRs in the 90+ days range] - February 2023: [1 PR in the 0-7 days range, 2 PRs in the 8-30 days range, 1 PR in the 31-90 days range, 15 PRs in the 90+ days range] - March 2023: [2 PRs in the 0-7 days range, 0 PRs in the 8-30 days range, 3 PRs in the 31-90 days range, 16 PRs in the 90+ days range] - April 2023: [1 PR in the 0-7 days range, 0 PRs in the 8-30 days range, 1 PR in the 31-90 days range, 19 PRs in the 90+ days range] - May 2023: [1 PR in the 0-7 days range, 0 PRs in the 8-30 days range, 0 PRs in the 31-90 days range, 20 PRs in the 90+ days range] - June 2023: [0 PRs in the 0-7 days range, 0 PRs in the 8-30 days range, 1 PR in the 31-90 days range, 20 PRs in the 90+ days range] - July 2023: [3 PRs in the 0-7 days range, 0 PRs in the 8-30 days range, 0 PRs in the 31-90 days range, 21 PRs in the 90+ days range] - August 2023: [2 PRs in the 0-7 days range, 1 PR in the 8-30 days range, 2 PRs in the 31-90 days range, 21 PRs in the 90+ days range] The distribution of change request age can be analyzed using quintiles. The quintiles provide insights into various age ranges for PRs. The quintiles for change request age in 2023 are as follows: - Quantile 0: The minimum change request age - Quantile 1: 25% of the change request age data - Quantile 2: 50% of the change request age data - Quantile 3: 75% of the change request age data - Quantile 4: The maximum change request age The quintile values for change request age in each month of 2023 are as follows: - January 2023: [16.0 days as Quantile 0, 211.25 days as Quantile 1, 240.5 days as Quantile 2, 437.0 days as Quantile 3, 839.0 days as Quantile 4] - February 2023: [7.0 days as Quantile 0, 176.0 days as Quantile 1, 253.0 days as Quantile 2, 385.0 days as Quantile 3, 867.0 days as Quantile 4] - March 2023: [3.0 days as Quantile 0, 75.0 days as Quantile 1, 279.0 days as Quantile 2, 402.0 days as Quantile 3, 898.0 days as Quantile 4] - April 2023: [3.0 days as Quantile 0, 105.0 days as Quantile 1, 309.0 days as Quantile 2, 432.0 days as Quantile 3, 928.0 days as Quantile 4] - May 2023: [3.0 days as Quantile 0, 136.0 days as Quantile 1, 340.0 days as Quantile 2, 463.0 days as Quantile 3, 959.0 days as Quantile 4] - June 2023: [33.0 days as Quantile 0, 166.0 days as Quantile 1, 370.0 days as Quantile 2, 493.0 days as Quantile 3, 989.0 days as Quantile 4] - July 2023: [1.0 day as Quantile 0, 169.0 days as Quantile 1, 391.5 days as Quantile 2, 513.5 days as Quantile 3, 1020.0 days as Quantile 4] - August 2023: [1.0 day as Quantile 0, 164.75 days as Quantile 1, 407.5 days as Quantile 2, 520.25 days as Quantile 3, 1051.0 days as Quantile 4] Based on the average change request age and quintile information, it can be observed that the age of PRs has been consistently high throughout the considered time period. This indicates that PRs are taking a long time to be closed or merged in the project. Furthermore, the distribution of change request age shows that a significant number of PRs have been open for an extended period. It is recommended to monitor the change request age closely and take necessary actions to reduce the time PRs remain open. This can be achieved through improved code review and project management practices, such as setting clear timelines for PR reviews and providing timely feedback to contributors. Timely resolution of PRs can lead to improved project efficiency and responsiveness to user needs.


summary:

The analysis of the "change_request_age" indicator shows that the age of PRs in the project has been consistently high, indicating a long time taken to close or merge PRs. It is recommended to closely monitor and address this issue to improve project efficiency and responsiveness.


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code_change_lines_add:

The "code_change_lines_add" indicator tracks the number of lines of code added in a given project over time. The data provided shows the monthly values for each year in 2023. From the data, it can be observed that there is some variation in the number of lines of code added each month. In January, 28,294 lines of code were added, which decreased to 11,397 in February and further decreased to 1,270 in March. The number of lines of code added then increased in April to 1,825, but decreased again in May to 11,198. In June, 4,745 lines of code were added, followed by 4,898 in July and 3,792 in August. The highest number of lines of code added in 2023 is seen in September, with a value of 54,294. Overall, there is a fluctuation in the number of lines of code added each month, indicating ongoing development activity in the project.


code_change_lines_remove:

The "code_change_lines_remove" indicator tracks the number of lines of code removed in a given project over time. The data provided shows the monthly values for each year in 2023. Analyzing the data, it can be observed that there is some variation in the number of lines of code removed each month. In January, 9,095 lines of code were removed, which decreased to 6,899 in February and further decreased to 157 in March. The number of lines of code removed then increased in April to 883, but decreased again in May to 1,914. In June, only 151 lines of code were removed, followed by 821 in July and 273 in August. The lowest number of lines of code removed in 2023 is seen in September, with a value of 273. Overall, the number of lines of code removed each month is relatively small compared to the number of lines of code added, indicating a relatively stable codebase with minimal removals.


code_change_lines_sum:

The "code_change_lines_sum" indicator represents the net change in lines of code in a given project over time. It is calculated by subtracting the number of lines of code removed from the number of lines of code added. Analyzing the data, it can be observed that there is both positive and negative values for the net change in lines of code each month. In January, there was a net addition of 19,199 lines of code, which decreased to 4,498 in February and further decreased to 1,113 in March. The net change then increased in April to 942, indicating a decrease in the net addition. In May, the net change increased significantly to 9,284, followed by 4,594 in June and 4,077 in July. In August, there was a net decrease of 4,282 lines of code, indicating more lines removed than added. The highest net change in lines of code in 2023 is seen in September, with a value of 54,021. Overall, the net change in lines of code each month fluctuates, indicating both additions and removals of code in the project. However, the overall trend is towards code addition, with the exception of August where there is a net decrease in lines of code.


summary:

The analysis of the code change lines indicators provides insights into the development activity and codebase changes in the project. The "code_change_lines_add" indicator shows the number of lines of code added each month, indicating ongoing development work. There is some variation in the number of lines added, with a significant increase observed in September. The "code_change_lines_remove" indicator, on the other hand, tracks the number of lines of code removed each month. This indicator shows relatively small removals compared to additions, suggesting a stable codebase with minimal removal activity. Lastly, the "code_change_lines_sum" indicator represents the net change in lines of code, taking into account both additions and removals. The analysis of this indicator reveals fluctuations in the net change, with an overall trend towards code addition. However, there is a net decrease in August, indicating a higher removal of code compared to additions.


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failed_indcators:

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