接口自动化测试

2023-06-03 11:00| 来源: 网络整理| 查看: 265

JSON格式的数据在http接口的自动化测试中是一种常见的数据结构，在接口自动化脚本里解析JSON返回结果,验证数据的正确性是非常关键的一步，但JSON结构有简单有复杂，不同的接口返回不同的结果，自动化脚本中解析响应JSON数据占了一大部分的工作量，而且随着接口的变动，维护脚本也特别麻烦，今天在这里介绍一种通用的解析所有JSON 的通用算法，来解决这一问题。

算法解析：

首先来看一段较为复杂的JSON:

图-1

从图-1可以看出，这段JSON第一层包含了resultCode，resultDesc，data，dataList四个key值，其中resultCode，resultDesc，data 都是单个的字符串，而dataList 的值是一个列表，有俩元素，其中每个元素又是一个字典，包括id,name,caption,subjectId,notes,items,其中items中又是一个list,每个元素为一个字典，key值为 id,name,caption等....不难发现这段JSON中第二层和第三层中都包含相同的key: id，name, capiton,这也是选择这段JSON的原因。大多数的JSON中可能都会有相同的key,解析的时候如何精确的获取想要的key对应的值这也是需要考虑的场景之一。

其实JSON的整个结构就是 “树”，可以将上述JSON中的第一个节点[JSON]看成树的根节点， resultCode，resultDesc 等节点看成叶子节点，而dataList 则可以看成父节点，dataList里的元素可以看成是子节点。

来一张更容易理解的图：A 为根节点，黄色的节点称是父节点，绿色节点则是叶子节点，图中的D 类比为图-1中的dataList节点，B,C为图一种的resultCode，resultDesc

图-2 解析JSON主要分为以下几步第一步：将JSON 转化为树，从根节点（A）开始遍历每一路径，即从根节点到叶子节点每一条路径作为一条数据，并且获取每一深度（JSON中的每一层）叶子节点的{key:value}，其中父节点的值不取（通常父节点可能是一个list,或者一个字典）,当前深度的叶子节点的值全取，与下一深度的叶子节点的值加到同一字典中。如图-2，B,C,D,E是深度为2的节点，第一次遍历，第一条数据为{B,C},以此递归，直至遍历所有路径，可到图-2的遍历结果为(其中B节点理解为 {'B':'B'},图-3 为图-2的JSON格式) 图-3 按第一步解析结果如下： 1. [{'B': 'B'}, {'C': 'C'}] 2. [{'B': 'B'}, {'C': 'C'}, {'G': 'G'}, {'F': 'F'}] 3. [{'B': 'B'}, {'C': 'C'}, {'G': 'G'}, {'F': 'F'}, {'K': 'K'}, {'L': 'L'}, {'M': 'M'}] 4. [{'B': 'B'}, {'C': 'C'}, {'G': 'G'}, {'F': 'F'}, {'O': 'O'}, {'P': 'P'}] 5. [{'B': 'B'}, {'C': 'C'}, {'G': 'G'}, {'F': 'F'}, {'O': 'O'}, {'P': 'P'}, {'S': 'S'}, {'R': 'R'}] 6. [{'B': 'B'}, {'C': 'C'}, {'G': 'G'}, {'F': 'F'}, {'O': 'O'}, {'P': 'P'}, {'S': 'S'}, {'R': 'R'}, {'T': 'T'}] 7. [{'B': 'B'}, {'C': 'C'}, {'U': 'U'}]

分析上述结果可以看出第3组结果包含了第1,2组值，6包含了5,4的结果，因此可以将重复的数据进行一次过滤和筛选

第二步：

过滤重复数据，步骤一的结果可以过滤为：

3. [{'B': 'B'}, {'C': 'C'}, {'G': 'G'}, {'F': 'F'}, {'K': 'K'}, {'L': 'L'}, {'M': 'M'}] 6.[{'B': 'B'}, {'C': 'C'}, {'G': 'G'}, {'F': 'F'}, {'O': 'O'}, {'P': 'P'}, {'S': 'S'}, {'R': 'R'}, {'T': 'T'}] 7. [{'B': 'B'}, {'C': 'C'}, {'U': 'U'}]

在这一步骤中，如果不同深度的叶子节点名称重复，则按照深度，依次在key后面加入序号1,2...

即如果有3个B,则解析结果为：

[{'B': 'B'}, {'B1': 'B1'}, {'B2: 'B2'}, {'F': 'F'}, {'K': 'K'}, {'L': 'L'}, {'M': 'M'}] 第三步：

根据想要获取的KEY,在第二步获取到的list中取出对应的value，KEY以列表的方式给出

keys = [key1,key2,key3....]，如要获取[B,C,F,G]

则最终结果为：

[{'B': 'B'}, {'C': 'C'}, {'G': 'G'}, {'F': 'F'}]

这样就轻而易举的解析处理我们想要的结果!

代码 -python算法实现 def responseComplexJson2LD(responseJson,assertKeys): ''' :param responseJson: 类型：JSONObject 接口返回:JSON 格式 :param assertKeys: 类型：list 需要校验的key ['resultCode','resultDesc','id','name','caption','id1','notes','name1','caption1','cubeName','functions'] 说明：如果key里有重复的解析为 key1,key2... :return: list[dict] ''' try: if not isinstance(responseJson,dict): raise Exception('responseJson 类型错误，必须为JSON格式') result_list = [] def isInclude(dict1, dict2): ''' :function: 判断 dict1 是否包含于dict2 :param dict1: :param dict2: :return: True False ''' for key1 in dict1: if key1 in dict2: pass else: return False return True def parse_dict(responseJson, parent): ''' :递归解析json,将json树解析成list[dict,dict....] :param responseJson: :param parent: :return: ''' if isinstance(responseJson, dict): dp = parent[:] not_l_d = [responseKey for responseKey in responseJson if not isinstance(responseJson[responseKey], (dict, list))] for i in not_l_d: dp.append({i: responseJson[i]}) dp_1 = dp[:] result_list.append(dp) for responseKey in responseJson: if isinstance(responseJson[responseKey], (dict, list)): parse_dict(responseJson[responseKey], dp_1) elif isinstance(responseJson, list): for i in responseJson: dd = parent[:] if not isinstance(i, (dict, list)): dd.append(i) result_list.append(dd) else: parse_dict(i, dd) else: dx = parent[:] dx.append(responseJson) result_list.append(dx) parse_dict(responseJson, parent=[]) if not result_list: return [] '''获取给定的assertKeys''' last_result =[] templist ={} if assertKeys: if isinstance(assertKeys,list): for iter in result_list: for key in iter: thiskey = list(key.keys())[0] '''如果有重复的key,解析为key1,key2...''' i = 1 temp = thiskey while True: if thiskey in list(templist.keys()): thiskey = thiskey + str(i) i = i + 1 else: templist.setdefault(thiskey,key.get(temp)) break last_result.append(templist) templist={} else: raise Exception("assertKeys 类型错误，必须为list") '''将assertKeys里的定义的key加入dict,不在assertKeys 定义的除去''' assert_result = [] templist = {} for meta in last_result: keys = list(meta.keys()) if isInclude(assertKeys,keys): for key in assertKeys: templist.setdefault(key,meta.get(key)) assert_result.append(templist) templist={} else: pass '''对返回的last_result中的元素去重''' result = [] for meta in assert_result: if meta not in result: result.append(meta) else: pass return result except Exception as err: Log.error(err) return [] 结果验证

以图-1中的json 为例测试

测试1： keylist = ['resultCode'] responseComplexJson2LD(test,keylist) 返回： {'resultCode': 100} 测试2： keylist = ['resultCode','resultDesc'] responseComplexJson2LD(test,keylist) 返回： {'resultCode': 100, 'resultDesc': '成功'} 测试3： keylist = ['resultCode','resultDesc','id'] responseComplexJson2LD(test,keylist) 返回： {'resultCode': 100, 'resultDesc': '成功', 'id': 1} {'resultCode': 100, 'resultDesc': '成功', 'id': 2} 测试4：--重复id,第二用id1标识 keylist = ['resultCode', 'resultDesc', 'id','id1'] responseComplexJson2LD(test,keylist) 返回： {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 1} {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 2} {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 20} {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 3} {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 4} {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 5} {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 6} {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 21} {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 22} {'resultCode': 100, 'resultDesc': '成功', 'id': 1, 'id1': 23} {'resultCode': 100, 'resultDesc': '成功', 'id': 2, 'id1': 7} {'resultCode': 100, 'resultDesc': '成功', 'id': 2, 'id1': 8} {'resultCode': 100, 'resultDesc': '成功', 'id': 2, 'id1': 9} {'resultCode': 100, 'resultDesc': '成功', 'id': 2, 'id1': 10} {'resultCode': 100, 'resultDesc': '成功', 'id': 2, 'id1': 24} {'resultCode': 100, 'resultDesc': '成功', 'id': 2, 'id1': 25} 测试5：取第二个id keylist = ['resultCode', 'resultDesc','id1'] responseComplexJson2LD(test,keylist) 返回： {'resultCode': 100, 'resultDesc': '成功', 'id1': 1} {'resultCode': 100, 'resultDesc': '成功', 'id1': 2} {'resultCode': 100, 'resultDesc': '成功', 'id1': 20} {'resultCode': 100, 'resultDesc': '成功', 'id1': 3} {'resultCode': 100, 'resultDesc': '成功', 'id1': 4} {'resultCode': 100, 'resultDesc': '成功', 'id1': 5} {'resultCode': 100, 'resultDesc': '成功', 'id1': 6} {'resultCode': 100, 'resultDesc': '成功', 'id1': 21} {'resultCode': 100, 'resultDesc': '成功', 'id1': 22} {'resultCode': 100, 'resultDesc': '成功', 'id1': 23} {'resultCode': 100, 'resultDesc': '成功', 'id1': 7} {'resultCode': 100, 'resultDesc': '成功', 'id1': 8} {'resultCode': 100, 'resultDesc': '成功', 'id1': 9} {'resultCode': 100, 'resultDesc': '成功', 'id1': 10} {'resultCode': 100, 'resultDesc': '成功', 'id1': 24} {'resultCode': 100, 'resultDesc': '成功', 'id1': 25} 测试6：只取caption 字段，俩caption 时第二个写caption1 keylist = ['caption','caption1] responseComplexJson2LD(test,keylist) 返回： {'caption': '交易', 'caption1': '交易金额'} {'caption': '交易', 'caption1': '交易用户数'} {'caption': '交易', 'caption1': '人均交易金额'} {'caption': '交易', 'caption1': '订单数'} {'caption': '交易', 'caption1': '订单金额'} {'caption': '交易', 'caption1': '大订单数'} {'caption': '交易', 'caption1': '大订单金额'} {'caption': '交易', 'caption1': '平均订单金额'} {'caption': '交易', 'caption1': '大订单数占比'} {'caption': '交易', 'caption1': '平均大订单金额'} {'caption': '盈亏', 'caption1': '盈利用户数'} {'caption': '盈亏', 'caption1': '亏损用户数'} {'caption': '盈亏', 'caption1': '净盈利值'} {'caption': '盈亏', 'caption1': '净亏损值'} {'caption': '盈亏', 'caption1': '盈利用户数占比'} {'caption': '盈亏', 'caption1': '亏损用户数占比'} 附上图-1完整的Json报文： data = { "resultCode": 100, "resultDesc": "成功", "data": "", "dataList": [ { "id": 1, "subjectId": "", "name": "trading", "caption": "交易", "notes": "1.人均交易金额=用户累积交易金额/交易用户数2.平均订单金额=用户累计订单金额/订单数3.大订单：单笔手续费大于50元的订单（手续费＝每笔订单金额＊万分之八）4.大订单数占比=大订单数/订单数5.平均大订单金额=累计大订单金额/大订单数", "items": [ { "id": 1, "name": "pay_money", "caption": "交易金额", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "CONTQTY", "itemType": "SINGLE", "calType": "", "valCalType": "MONEY", "functions": "SUM" }, { "id": 2, "name": "pay_user_num", "caption": "交易用户数", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "ACCOUNT_ID", "itemType": "SINGLE", "calType": "", "valCalType": "COMMON", "functions": "COUNT_DISTINCT" }, { "id": 20, "name": "pay_money,pay_user_num", "caption": "人均交易金额", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "CONTQTY,ACCOUNT_ID", "itemType": "COMBINE", "calType": "/", "valCalType": "MONEY", "functions": "SUM,COUNT_DISTINCT" }, { "id": 3, "name": "order_num", "caption": "订单数", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "*", "itemType": "SINGLE", "calType": "", "valCalType": "COMMON", "functions": "COUNT" }, { "id": 4, "name": "order_money", "caption": "订单金额", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "CONTQTY", "itemType": "SINGLE", "calType": "", "valCalType": "MONEY", "functions": "SUM" }, { "id": 5, "name": "big_order_num", "caption": "大订单数", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "*", "itemType": "CONDITION", "calType": "", "valCalType": "COMMON", "functions": "COUNT" }, { "id": 6, "name": "big_order_money", "caption": "大订单金额", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "CONTQTY", "itemType": "CONDITION", "calType": "", "valCalType": "MONEY", "functions": "SUM" }, { "id": 21, "name": "order_money,order_num", "caption": "平均订单金额", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "CONTQTY,*", "itemType": "COMBINE_CONDITION", "calType": "/", "valCalType": "MONEY", "functions": "SUM,COUNT" }, { "id": 22, "name": "big_order_num,order_num", "caption": "大订单数占比", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "*,*", "itemType": "COMBINE_CONDITION", "calType": "/", "valCalType": "PERCENT", "functions": "COUNT,COUNT" }, { "id": 23, "name": "big_order_money,big_order_num", "caption": "平均大订单金额", "cubeName": "TRANSACTION_ANALYSIS_V3", "columns": "CONTQTY,*", "itemType": "COMBINE_CONDITION", "calType": "/", "valCalType": "MONEY", "functions": "SUM,COUNT" } ] }, { "id": 2, "subjectId": "", "name": "profit&loss", "caption": "盈亏", "notes": "1.盈利用户数：当日用户中，净利润;0的用户数2.亏损用户数：当日用户中，净利润;0的用户数3.盈利用户占比=当日盈利用户数/当日总用户数4.亏损用户占比=当日亏损用户数/当日总用户数5.净盈利值：当日用户中，净利润;0的值求和6.净亏损值：当日用户中，净利润;0的值求和7.日净利润＝当日净资产-前一日净资产-当日入金+当日出金8.月净利润=月末净资产-月初净资产-月内总入金+月内总出金其他时间粒度计算方法类似月粒度", "items": [ { "id": 7, "name": "profit_user_count", "caption": "盈利用户数", "cubeName": "NET_PROFIT_ANALYSIS_V3", "columns": "ACCOUNT_ID", "itemType": "CONDITION", "calType": "", "valCalType": "COMMON", "functions": "COUNT_DISTINCT" }, { "id": 8, "name": "loss_user_count", "caption": "亏损用户数", "cubeName": "NET_PROFIT_ANALYSIS_V3", "columns": "ACCOUNT_ID", "itemType": "CONDITION", "calType": "", "valCalType": "COMMON", "functions": "COUNT_DISTINCT" }, { "id": 9, "name": "net_profit_num", "caption": "净盈利值", "cubeName": "NET_PROFIT_ANALYSIS_V3", "columns": "NETPROFIT", "itemType": "CONDITION", "calType": "", "valCalType": "MONEY", "functions": "SUM" }, { "id": 10, "name": "net_loss_num", "caption": "净亏损值", "cubeName": "NET_PROFIT_ANALYSIS_V3", "columns": "NETPROFIT", "itemType": "CONDITION", "calType": "", "valCalType": "MONEY", "functions": "SUM" }, { "id": 24, "name": "profit_user_count,profit_total_user_count", "caption": "盈利用户数占比", "cubeName": "NET_PROFIT_ANALYSIS_V3", "columns": "ACCOUNT_ID,ACCOUNT_ID", "itemType": "COMBINE_CONDITION", "calType": "/", "valCalType": "PERCENT", "functions": "COUNT_DISTINCT,COUNT_DISTINCT" }, { "id": 25, "name": "loss_user_count,profit_total_user_count", "caption": "亏损用户数占比", "cubeName": "NET_PROFIT_ANALYSIS_V3", "columns": "ACCOUNT_ID,ACCOUNT_ID", "itemType": "COMBINE_CONDITION", "calType": "/", "valCalType": "PERCENT", "functions": "COUNT_DISTINCT,COUNT_DISTINCT" } ] } ] }

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