ExcelBDD Python指南

在Python里面支持BDD

Excel BDD Tool Specification By ExcelBDD Method

This tool is to get BDD test data from an excel file, its requirement specification is below

The Essential of this approach is obtaining multiple sets of test data, so when combined with Excel's Sheet, the key parameters are:

  1. ExcelFileName, required, which excel file is used.
  2. SheetName, optional, which Sheet the requirement writer writes in, if not specified, 1st sheet is chosen. An Excel file supports multiple Sheets, so an Excel is sufficient to support a wide range, such as Epic, Release, or a module.
  3. HeaderMatcher, filter the header row by this matcher, if matched, this set will be collected in.
  4. HeaderUnmatcher, filter the header row by this matcher, if matched, this set will be excluded.

Once the header row and parameter name column are determined by 'Parameter Name' grid automatically, the data area is determined, such as the green area in the table above. The gray area of the table above is the story step description, which is the general requirements step.

Install ExcelBDD Python Edition

pip install excelbdd

API

behavior.get_example_list

get_example_list(excelFile, sheetName = None, headerMatcher = None, headerUnmatcher = None)

  1. excelFile: excel file path and name, relative or absolute
  2. sheetName: sheet name, optional, default is the first sheet in excel file
  3. HeaderMatcher: filter the header row by this matcher, if matched, this set will be collected in. optional, default is to select all.
  4. HeaderUnmatcher: filter the header row by this matcher, if matched, this set will be excluded. optional, default is to exclude none.

behavior.get_example_table

get_example_table(excelFile,sheetName = None,headerRow = 1,startColumn = 'A')

  1. excelFile: excel file path and name, relative or absolute
  2. sheetName: sheet name, optional, default is the first sheet in excel file
  3. headerRow: the number of header row, optional, default is 1
  4. startColumn: the char of first data area, optional, default is column A in sheet

Simple example code

The Famouse FizzBuzz kata is described in excelbdd format, as below.

import pytest
from excelbdd.behavior import get_example_list
import FizzBuzz

excelBDDFile = "path of excel file" 
@pytest.mark.parametrize("HeaderName, Number1, Output1, Number2, Output2, Number3, Output3, Number4, Output4",
                        get_example_list(excelBDDFile,"FizzBuzz"))
def test_FizzBuzz(HeaderName, Number1, Output1, Number2, Output2, Number3, Output3, Number4, Output4):
    assert FizzBuzz.handle(Number1) == Output1
    assert FizzBuzz.handle(Number2) == Output2
    assert FizzBuzz.handle(Number3) == Output3
    assert FizzBuzz.handle(Number4) == Output4

Input vs Expect + Test Result Format - SBT - Specification By Testcase

testcase example is below, which uses headerMatcher to filter the data

@pytest.mark.parametrize("HeaderName, ParamName1, ParamName1Expected, ParamName1TestResult, \
                         ParamName2, ParamName2Expected, ParamName2TestResult, ParamName3, \
                         ParamName3Expected, ParamName3TestResult, ParamName4, ParamName4Expected, \
                         ParamName4TestResult",
                        get_example_list(bddFile1, "SBTSheet1","Scenario"))
def test_excelbdd_sbt(HeaderName, ParamName1, ParamName1Expected, ParamName1TestResult, 
                      ParamName2, ParamName2Expected, ParamName2TestResult, ParamName3, 
                      ParamName3Expected, ParamName3TestResult, ParamName4, ParamName4Expected, 
                      ParamName4TestResult):
    print(HeaderName, ParamName1, ParamName1Expected, ParamName1TestResult, 
                      ParamName2, ParamName2Expected, ParamName2TestResult, ParamName3, 
                      ParamName3Expected, ParamName3TestResult, ParamName4, ParamName4Expected, 
                      ParamName4TestResult)
    # add test data are loaded into the above parameters, add test code below

ExcelBDD can detect 3 parameter-header patterns automatically, the last one is below.

Input vs Expected

The demo code is below

@pytest.mark.parametrize("HeaderName, ParamName1, ParamName1Expected,  \
                         ParamName2, ParamName2Expected, ParamName3, \
                         ParamName3Expected, ParamName4, ParamName4Expected"
                        get_example_list(bddFile1, "SBTSheet1","Scenario"))
def test_excelbdd_sbt(HeaderName, ParamName1, ParamName1Expected,  
                      ParamName2, ParamName2Expected, ParamName3, 
                      ParamName3Expected, ParamName4, ParamName4Expected):
    print(HeaderName, ParamName1, ParamName1Expected, 
                      ParamName2, ParamName2Expected,  ParamName3, 
                      ParamName3Expected, ParamName4, ParamName4Expected)
    # add test data are loaded into the above parameters, add test code below

Get Table

The test data are organized in normal table, as below.

the below code show how to fetch the test data into testcase

from excelbdd.behavior import get_example_table

@pytest.mark.parametrize("Header01, Header02, Header03, Header04, Header05, Header06, Header07, Header08",
                         get_example_table(tableFile, "DataTable4"))
def test_get_example_tableB(Header01, Header02, Header03, Header04, Header05, Header06, Header07, Header08):
    print(Header01, Header02, Header03, Header04, Header05, Header06, Header07, Header08)   
    # add test data are loaded into the above parameters, add test code below

ExcelBDD Python指南线上版维护在ExcelBDD Python Guideline

ExcelBDD开源项目位于 ExcelBDD Homepagehttps://dev.azure.com/simplopen/ExcelBDD

相关推荐
我曾经是个程序员几秒前
鸿蒙学习记录
开发语言·前端·javascript
爱上语文1 分钟前
宠物管理系统:Dao层
java·开发语言·宠物
程序员shen16161141 分钟前
抖音短视频saas矩阵源码系统开发所需掌握的技术
java·前端·数据库·python·算法
小老鼠不吃猫43 分钟前
力学笃行(二)Qt 示例程序运行
开发语言·qt
长潇若雪1 小时前
《类和对象:基础原理全解析(上篇)》
开发语言·c++·经验分享·类和对象
人人人人一样一样1 小时前
作业Python
python
四口鲸鱼爱吃盐1 小时前
Pytorch | 利用VMI-FGSM针对CIFAR10上的ResNet分类器进行对抗攻击
人工智能·pytorch·python
四口鲸鱼爱吃盐1 小时前
Pytorch | 利用PI-FGSM针对CIFAR10上的ResNet分类器进行对抗攻击
人工智能·pytorch·python
小陈phd2 小时前
深度学习之超分辨率算法——SRCNN
python·深度学习·tensorflow·卷积