测试“ UCM” -VOB性能

如何解决测试“ UCM” -VOB性能

我们的VOB当前具有5级功能，我的任务是升级到9级并确保一切仍然正常。我已经阅读了有关功能级别的技术说明from IBM。

对于功能级别7，它指出：

改善了UCM性能

我莫名其妙地想将其数字化。有人可以概述一下我如何实现这一目标。在ClearCase方面相对较新，在ClearCase之前我只知道git，所以我会很感谢一些指导

解决方法

您可以在以下位置找到衡量UCM性能的具体标准：

• 白皮书“ Component Organization and UCM Performance”（Richard Curtis，David Qiang Li）
• feature levels（类似于您所见）

从第一篇论文中，您可以得出设置配置：

我们的测试环境由一个VOB服务器主机和通过100MB全双工网络连接的固定数量的客户端组成。

VOB服务器托管了一个PVOB和一个组件VOB。
组件VOB最初是用单个根级组件创建的。
在随后的测试中，组件VOB是使用一个或多个sub-VOB组件创建的。

元素的数量（32,768）保持不变，与组件的数量无关。

以及要测量的命令。

UCM视图中的非UCM命令：

• import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import datasets (train_x,train_y),(test_x,test_y) = datasets.mnist.load_data() inputs = layers.Input((28,28,1)) net = layers.Conv2D(32,(3,3),padding ='SAME')(inputs) net = layers.Activation('relu')(net) net = layers.Conv2D(32,padding ='SAME')(net) net = layers.Activation('relu')(net) net = layers.MaxPooling2D(pool_size=(2,2))(net) net = layers.Dropout(0.25)(net) net = layers.Conv2D(64,padding ='SAME')(net) net = layers.Activation('relu')(net) net = layers.Conv2D(64,2))(net) net = layers.Dropout(0.25)(net) net = layers.Flatten()(net) net = layers.Dense(512)(net) net = layers.Activation('relu')(net) net = layers.Dropout(0.5)(net) net = layers.Dense(10)(net) net = layers.Activation('softmax')(net) model = tf.keras.Model(inputs=inputs,outputs=net,name='Basic_CNN') loss_fun = tf.keras.losses.sparse_categorical_crossentropy metrics = tf.keras.metrics.Accuracy() optm = tf.keras.optimizers.Adam() model.compile(optimizer=tf.keras.optimizers.Adam(),loss='sparse_categorical_crossentropy',metrics=[tf.keras.metrics.Accuracy()]) train_x.shape,train_y.shape test_x.shape,test_y.shape import numpy as np np.expand_dims(train_x,-1).shape tf.expand_dims(train_x,-1).shape train_x = train_x[...,tf.newaxis] test_x = test_x[...,tf.newaxis] train_x.shape np.min(train_x),np.max(train_x) train_x = train_x / 255. test_x = test_x / 255. np.min(train_x),np.max(train_x) num_epochs = 1 batch_size = 32 model.fit(train_x,train_y,batch_size=batch_size,shuffle=True,epochs=num_epochs) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-20-870033ef5c40> in <module> 2 batch_size=batch_size,3 shuffle=True,----> 4 epochs=num_epochs) ~/opt/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self,*args,**kwargs) 106 def _method_wrapper(self,**kwargs): 107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access --> 108 return method(self,**kwargs) 109 110 # Running inside `run_distribute_coordinator` already. ~/opt/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in fit(self,x,y,batch_size,epochs,verbose,callbacks,validation_split,validation_data,shuffle,class_weight,sample_weight,initial_epoch,steps_per_epoch,validation_steps,validation_batch_size,validation_freq,max_queue_size,workers,use_multiprocessing) 1096 batch_size=batch_size): 1097 callbacks.on_train_batch_begin(step) -> 1098 tmp_logs = train_function(iterator) 1099 if data_handler.should_sync: 1100 context.async_wait() ~/opt/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in __call__(self,**kwds) 778 else: 779 compiler = "nonXla" --> 780 result = self._call(*args,**kwds) 781 782 new_tracing_count = self._get_tracing_count() ~/opt/anaconda3/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in _call(self,**kwds) 805 # In this case we have created variables on the first call,so we run the 806 # defunned version which is guaranteed to never create variables. --> 807 return self._stateless_fn(*args,**kwds) # pylint: disable=not-callable 808 elif self._stateful_fn is not None: 809 # Release the lock early so that multiple threads can perform the call TypeError: 'NoneType' object is not callable
• checkout file
• checkin file
• setact activity_name
• setact -none

针对开发人员的UCM命令：

• mkelem file
• diffbl -ver
• deliver -pre -l -str -to
• deliver -str -to -f
• deliver -com -f
• rebase -rec -view

用于项目经理的UCM命令：

• rebase -com
• chstream -rec
• diffbl -act

UCM发布工程师命令：

• mkbl -inc
• rebase -rec
• rebase -com

一旦有了环境以及包含这些命令的脚本，就可以在功能级别升级之前 运行几次上述脚本，然后在运行之后运行相同的脚本，并测量执行时间增量