Project Score
Project Score highlights the score for each project and provides an option to understand the time periods in which the performance is affected.
The project score is calculated as the combination of
- Code Progress data that focuses on the PR, commit, submitter, reviewer actions
- Development Behavior data that focuses on the cycle time, productivity, collaboration details
- Project Health data that focuses on the issues, bugs, commit risk analysis, proficiency details
- Actively trained large data set to generate AI analysis for the data-driven approach
Under the project score chart there are three data cards that show the related data out of the selected time period that shows;
- The number of contributors as team members involved in the process by opening a ticket, writing a code, conducting code review as a part of issue management or Git cycle.
- The number of active issues shows the issues that have been processed during the selected time period that contribute to the score calculation.
- The average time spent is calculated by the work logs provided related to the project in the selected time period.
Work Breakdown
Work Breakdown demonstrates the issue focus of the team members in total. It is a beneficial report to understand what is the generic status of the issues, the amount of work done so far, the quality of work with the issue statuses and bug distributions.
Throughput
Throughput indicates the sum of the number of changed codes. While having this trend through time can reflect the amount of work put into the project, it would be much more accurate to measure the progress by excluding the sum of churn codes from the total amount.
Therefore, the efficiency part is the value presented after reducing the codes indicating the churn. This report also enables our customers to understand the efficiency of the development process during the specified period.
Our suggestion is to be aware of the dramatic drops or continuous poor indications in throughput and efficiency trends.
Average Throughput
This indicator shows the number of code lines affected by the changes made in the selected time period for a project.
Average Efficiency
This indicator mainly takes churn rate into consideration because churn rate reflects that the work has been repeated to achieve the same purpose.
Least Productive Week
This indicator is related to the week period with the most churn ratio and least impact ratio for the changes that have been applied by the team.
Proficiency
Proficiency is the measurement of the commit efficiency for the software languages used in the company.
The main idea of proficiency is to show the managers and leaders;
- What are the focused software languages for the company teams in terms of the lines of code and the number of commits for each language
- How efficient is the coding process for each specific software language in terms of the amount of code staying untouched during the first 3 weeks period after it is written
- Get feedback and recommendations to improve the efficiency and company skill set for the technologies adopted by the company
The presented languages alongside the company average are the following;
ASP.NET | HTML | Ruby |
C/C++ | Java | Rust |
C# | JavaScript | Scala |
CSS | Kotlin | SQL |
Cobol | Objective-C | Swift |
Dart | Perl | Typescript |
Erlang | PHP | VBScript |
Go | Python | XML |
Groovy | R | YAML |
In our report to present the team efficiency of software languages, the following bubble report has been used to represent the efficiency of each language over time.
By hovering over each bubble, lines of code and efficiency information can be seen together. This also enables users to see the weight of a specific language on the completed work.
While offering insights to highlight the most efficient parts and the parts that require more attention, Valven Atlas also provides recommendations to explore the possible ways to improve required areas.
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