Intra-Class Testing of Abstract Class Features

One of the characteristics of the increasingly widespread use of object-oriented libraries and the resulting intensive use of inheritance is the proliferation of dependencies on abstract classes. Such classes defer the implementation of some features, and are typically used as a specification or design tool. However, since their features are not fully implemented,abstract classes cannot be instantiated, and thus pose challenges for execution-based testing strategies. This paper presents a structured approach that supports the testing of features in abstract classes. Core to the approach is a series of static analysis steps that build a comprehensive view of the inter-class dependencies in the system under test. We then leveraged this information to define a test order for the methods in an abstract class that minimizes the number of stubs required during testing, and clearly identifies the required functionality of these stubs. Our approach is based on a comprehensive taxonomy of object-oriented classes that provides a framework for our analysis. First we describe the algorithms to calculate the inter-class dependencies and the test-order that minimizes stub creation. Then we give an overview of our tool, AbstractTestJ that implements our approach by generating a test order for the methods in an abstract Java class. Finally, we harness this tool to provide an analysis of 12 substantial Java applications that demonstrates both the feasibility of our approach and the importance of this technique

Island Loss for Learning Discriminative Features in Facial Expression Recognition

Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or the center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.Comment: 8 pages, 3 figure

Robust classification with context-sensitive features

This paper addresses the problem of classifying observations when features are context-sensitive, especially when the testing set involves a context that is different from the training set. The paper begins with a precise definition of the problem, then general strategies are presented for enhancing the performance of classification algorithms on this type of problem. These strategies are tested on three domains. The first domain is the diagnosis of gas turbine engines. The problem is to diagnose a faulty engine in one context, such as warm weather, when the fault has previously been seen only in another context, such as cold weather. The second domain is speech recognition. The context is given by the identity of the speaker. The problem is to recognize words spoken by a new speaker, not represented in the training set. The third domain is medical prognosis. The problem is to predict whether a patient with hepatitis will live or die. The context is the age of the patient. For all three domains, exploiting context results in substantially more accurate classification