Phases of Testing

本文详细介绍了软件测试的不同阶段,包括单元测试、集成测试、系统测试、验收测试及现场测试等,解释了每个阶段的目标与实施重点。
 
Unit

Traditionally done by developers when modules/units of code are developed

Testing is done at the end of an assigned unit of work(coding)

Developer completes to confirm- "have i finished this task?"-exitcriteria

Integration

Testing done at the time when the work of one or more task assignments are conbined, thus the term integration

Stubs are included for missing elements of the system

System    

The entire system has been integrated together

Completed functional testing implemented

Development team focuses on correcting defects identified in testing process

Note: The development team must believe here that if system testing passes the product will go to the field.

Acceptance

Customer runs his/her own testing to confirm that the system meets marketingg requirements

Generally acceptance testing plan has been known by system testing tesm.        

Live    Non-intrusive testing of the system while it is in use to measure quality factors such as performance, system resource use, and to spot check correct operation.

NOTE:

Acceptance testing: Usually ends up with money changing hands, The owners of the contract verify that the application behaves as they intended in the contract. Usually the acceptance tests are written before the application is developed.

Live Testing: Similar to Beta testing

下载方式:https://pan.quark.cn/s/b4d8292ba69a 在构建食品品牌的市场整合营销推广方案时,我们必须首先深入探究品牌的由来、顾客的感知以及市场环境。 此案例聚焦于一款名为“某饼干产品”的食品,该产品自1998年进入河南市场以来,经历了销售业绩的波动。 1999至2000年期间,其销售额取得了明显的上升,然而到了2001年则出现了下滑。 在先前的宣传活动中,品牌主要借助大型互动活动如ROAD SHOW来吸引顾客,但收效甚微,这揭示了宣传信息与顾客实际认同感之间的偏差。 通过市场环境剖析,我们了解到消费者对“3+2”苏打夹心饼干的印象是美味、时尚且充满活力,但同时亦存在口感腻、价位偏高、饼身坚硬等负面评价。 实际上,该产品可以塑造为兼具美味、深度与创新性的休闲食品,适宜在多种情境下分享。 这暗示着品牌需更精确地传递产品特性,同时消解消费者的顾虑。 在策略制定上,我们可考虑将新产品与原有的3+2苏打夹心进行协同推广。 这种策略的长处在于能够借助既有产品的声誉和市场占有率,同时通过新产品的加入,刷新品牌形象,吸引更多元化的消费群体。 然而,这也可能引发一些难题,例如如何合理分配新旧产品间的资源,以及如何保障新产品的独特性和吸引力不被既有产品所掩盖。 为了提升推广成效,品牌可以实施以下举措:1. **定位修正**:基于消费者反馈,重新确立产品定位,突出其美味、创新与共享的特性,减少消费者感知的缺陷。 2. **创新宣传**:宣传信息应与消费者的实际体验相契合,运用更具魅力的创意手段,例如叙事式营销,让消费者体会到产品带来的愉悦和情感共鸣。 3. **渠道选择**:在目标消费者常去的场所开展活动,例如商业中心、影院或在线平台,以提高知名度和参与度。 4. **媒体联...
### Bag of Words Algorithm Datasets For training or testing purposes with the Bag of Words (BoW) model, several well-known datasets provide rich resources suitable for various applications such as image classification, object recognition, and scene categorization. These datasets offer diverse collections of images along with annotations that facilitate constructing robust BoW models. #### Commonly Used Image Databases 1. **Caltech-101/256** This dataset contains around 9K labeled examples across 101 categories plus an additional clutter class in Caltech-256[^1]. The variety within these classes allows researchers to evaluate how effectively different configurations of the BoW approach handle varying levels of intra-class diversity. 2. **Pascal VOC Series** Pascal Visual Object Classes Challenge provides multiple editions like VOC2007, VOC2012 which include annotated objects belonging to twenty distinct types per picture. Such detailed labeling supports more sophisticated analyses beyond simple presence detection into aspects like pose estimation or segmentation tasks[^2]. 3. **CIFAR-10/CIFAR-100** Comprising tiny colored photos split between ten primary labels in CIFAR-10 version; alternatively expanded out over hundred finer-grained subclasses through its bigger sibling set – CIFAR-100. Despite lower resolution compared to others mentioned earlier, it remains popular due partly because faster processing times allow quicker experimentation cycles during development phases. 4. **ImageNet ILSVRC** As perhaps the largest single source available today containing millions upon millions of URLs pointing towards JPEG files spread amongst thousands upon thousands unique synsets organized hierarchically according WordNet structure. Specifically designed challenges based off subsets here have driven much advancement forward regarding deep learning techniques applied toward computer vision problems including those involving bag-of-feature representations derived via local descriptors aggregated together forming global feature vectors representative enough yet compact sufficient for efficient comparison operations against other samples encountered later down line when deployed operationally outside lab conditions. To implement a complete workflow efficiently using any chosen corpus above: ```python import numpy as np from sklearn.cluster import MiniBatchKMeans from skimage.feature import hog from sklearn.preprocessing import StandardScaler def extract_features(image_paths): """Extract HOG features from given list of image paths.""" features = [] for path in image_paths: img = load_image(path) fd = hog(img, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualize=False) features.append(fd) return np.array(features) # Example usage image_paths = [...] # List of filepaths leading to individual items inside selected collection features = extract_features(image_paths) scaler = StandardScaler().fit(features) scaled_features = scaler.transform(features) kmeans = MiniBatchKMeans(n_clusters=vocab_size, batch_size=batch_size).fit(scaled_features) histograms = construct_histograms(kmeans.labels_) ```
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