Employee performance report from agile worklogs dataset.
This library uses employee worklogs downloaded from Jira in excel format to calculate various KPIs of employee performance. This information can also be viewed in a pdf report generated for each employee.
| DIMENSION | Nº | KPI | DESCRIPTION |
|---|---|---|---|
| productivity | 1 | velocity | average number of hours to complete an assigned task |
| 2 | concentration | average length of time to complete an assigned task | |
| 3 | engagement | percentage of hours logged | |
| 4 | independence | percentage of own work on assigned tasks | |
| adaptability | 5 | learning | percentage of time spent studying, researching or learning |
| 6 | versatility | standard deviation of the dedication to the different existing projects | |
| 7 | heterogeneity | standard deviation of dedication to the different existing issue types | |
| 8 | complexity | assumed bugs resolution rate | |
| teamwork | 9 | colaboration | percentage of time spent collaborating on tasks assigned to other employees |
| 10 | sociability | percentage of employees with whom they collaborate | |
| 11 | participation | percentage of time spent on multi-assigned tasks | |
| 12 | connection | percentage of time spent in meetings | |
| mentorship | 13 | management | percentage of time spent on tasks related to planning and organization |
| 14 | guiance | assumed tasks review rate | |
| 15 | responsibility | average percentage assumed per project |
| FILE | DESCRIPTION |
|---|---|
| data/timeUsers/* | Example of excel files dowloaded from Jira |
| data/*.pkl | Pickle files to store the calculated information from the previous files |
| img/* | Examples of generated employee performance reports |
| src/conventions.py | Module to set the parametrization |
| src/preprocess.py | Module to read and preprocess the worklogs' files |
| src/calculate.py | Module to compute the proposed KPIs |
| src/report.py | Module to generate the employee performance reports |
| src/quick_start.ipynb | Notebook to show the complete workflow: read, preprocess, calculate and report |
| src/individual_kpis.ipynb | Notebook to understand and work with the individual KPIs |
| src/aggregated_kpis.ipynb | Notebook to use and work with the aggregated KPIs |
| requirements.txt | List of needed libraries |
| README.md | Library summary |
# read
from preprocess import read_worklogs_files
worklogs = read_worklogs_files()
# preprocess
from preprocess import preprocess_worklogs
worklogs = preprocess_worklogs(worklogs)
# calculate
from calculate import calculate_metrics_by_year
years = range(2019, 2023)
users_and_metrics = calculate_metrics_by_year(worklogs, years)[0]
# report
from report import generate_reportsworklogs = read_worklogs_files()
year = 2022
history = range(2019, 2022)
generate_reports(users_and_metrics, year, history)