One, get data
The previous article is based on Jira's automatic defect report analysis (5) Defect statistics by project We have counted the number of project defects found, the number of remaining, and the number of repairs on a daily basis.
Here we can directly obtain the data using
Second, draw a line chart
We follow the article on Jira-based defect automated report analysis (7) matplotlib pie chart: defect severity distribution , and add a method to draw a line chart in the Draw class
From the data, we can see that there are three pieces of data for defect discovery, repair, and number of remaining data, which means that three broken lines need to be drawn with date as the horizontal axis and quantity as the vertical axis.
# coding=utf-8
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
plt.rcParams['font.sans-serif'] = ['simhei'] # Used to display Chinese labels normally
plt.rcParams['axes.unicode_minus'] = False # Used to display the negative sign normally
class Draw:
"""
Drawing
"""
def __init__(self, reportpath):
self.reportpath = reportpath
# Only display >1 tags
def my_autopct(self, pct, limit=1):
return ('%.2f%%' % pct) if pct > limit else ''
def drawing_cake(self):
"""
Draw a pie chart
"""
pass
def drawing_histogram(self):
"""
Histogram
"""
pass
def drawing_linechart_more(self, project_name, title, x_lebal, *y_labels, **kw):
"""
Draw a line chart: support multi-line
"""
fig, ax = plt.subplots(1, 1)
# Set the x-axis display density
tick_spacing = 10
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
for i in range(len(y_labels)):
plt.plot(x_lebal, y_labels[i], kw['marker'][i], linewidth=1, label=kw['label'][i])
plt.legend() # Let the legend take effect
x = range (len (x_lebal))
plt.xticks(x, x_lebal, rotation=90, fontsize=6)
plt.margins(0)
plt.subplots_adjust(bottom=0.15)
plt.xlabel("Date(day)") # X axis label
plt.ylabel("Number") # Y axis label
plt.title(project_name + title) # Title
plt.savefig(self.reportpath + "/" + project_name + title + '.png')
plt.show()
plt.cla ()
plt.close("all")
3. Statistics and graphing
1. Import dependent modules
from ana_jira import Ana_jira from draw import Draw from common.mysqluntil import MysqlUntil
2. Define a class to count polyline data
class Line:
def __init__(self, project_name, test_jira, first_day, last_day, types, weeks, report_path):
self.project_name = project_name
self.test_jira = test_jira
self.first_day = first_day
self.last_day = last_day
self.types = types
self.weeks = weeks
self.report_path = report_path
# Line chart-discovery, repair, and remaining quantity trend chart
def line_lave(self):
sql = '''
SELECT
`date`,
`amount_find`,
`amount_lave`,
`amount_repair`
FROM
`project_issues`
WHERE
`project` = '{}'
AND `date` BETWEEN '{}'
AND '{}';
'''.format(self.project_name, self.first_day, self.last_day)
datas = MysqlUntil().mysql_select(sql)
date, amount_find, amount_lave, amount_repair = [], [], [], []
for data in datas:
date.append(data['date'] if data['date'] else '未填写')
amount_find.append(int(data['amount_find']) if data['amount_find'] else 0) #
amount_lave.append(int(data['amount_lave']) if data['amount_lave'] else 0) # 遗留数量
amount_repair.append(int(data['amount_repair']) if data['amount_repair'] else 0)
if date:
marker = ('b-.','r-','g--') # line style, color
label = ('find','lave','repair') # legend
Draw(self.report_path).drawing_linechart_more(self.project_name,
" Issues Trend Analysis (IT WEEK)",
date,
amount_find, amount_lave, amount_repair,
marker=marker, label=label
)
print('Comprehensive defect trend chart: data statistics completed in recent {}weeks'.format(self.weeks))
else:
print('Defect comprehensive trend chart: No data in recent {}weeks'.format(self.weeks))
3. Expand statistical methods
The above is based on the daily statistics of project defect data. In addition, we can also directly execute JQL query data
For example, the number of statistical findings
def days(start_time, weeks, m=0):
days = []
for i in range(m, 7*weeks+m):
enddays = start_time + datetime.timedelta (days = i)
time_Stamp1 = int(time.mktime(enddays.timetuple())) # Convert to timestamp:
t1 = time.localtime(time_Stamp1) # Converted into time ancestor
day = time.strftime("%Y-%m-%d", t1) # Convert to other string format:
days.append(day)
return days
def line(self):
"""
Draw a line graph: find the number
:param project_name:
:return:
"""
start_time = datetime.datetime.strptime(self.first_day + ' 00:00:00', "%Y-%m-%d %H:%M:%S")
# x axis
start = days(start_time, self.weeks)
end = days(start_time, self.weeks, 1)
y_lebal = []
for m, n in zip(start, end):
everyday_find_jql = 'project="{}" AND created >= {} AND created <= {}'.format(self.project_name, m, n)
one_line = Ana_jira (self.test_jira, everyday_find_jql) .req_jira () # 缺 陷 Quantity
y_lebal.append(one_line)
if sum(y_lebal) > 0:
marker = ('b-.')
label = ('find')
Draw(self.report_path).drawing_linechart_more(self.project_name,
" Issues Discovery Trend Analysis (IT WEEK)",
start,
y_lebal,
marker=marker, label=label
)
print('Project {} recent {} week defect discovery trend statistics completed'.format(self.project_name, self.weeks))
else:
print('Project {} has not submitted BUG in recent {} week, unable to analyze the trend of defect discovery'.format(self.project_name, self.weeks))
Four, execution statistics
li = Line(project_name, test_jira, first_day, last_day, types, weeks, project_report_path) # li.line() # (Project) Discover the quantity trend li.line_lave() # (Project) Comprehensive trend analysis: discovery, repair, remaining quantity (whether the trend converges)
