Test Context

Abstract: A number of prior studies have not found declines in recognition performance when testing occurs in an environmental context that is different from the learning context. These findings raise serious problems for global activation theories of recognition which predict that hit and false alarm rates will decline when the test context does not match the learning context. Environmental context was manipulated as a unique combination of foreground color, background color, and location on a computer screen in three experiments using intact-rearranged recognition testing and two experiments using single-item testing. Changes in context resulted in reduced hit and false alarm rates as predicted by global activation theories in all five experiments. Mental reinstatement of the learning context was also examined

Abstract: Mobile app testing is challenging since each test needs to be executed in a variety of operating contexts including heterogeneous devices, various wireless networks and different locations. Crowdsourcing enables a mobile app test to be distributed as a crowdsourced task to leverage crowd workers to accomplish the test. However, high test quality and expected test context coverage are difficult to achieve in crowdsourced testing. Upon distributing a test task, mobile app providers neither know who to participate nor predict whether all the expected test contexts can be covered in the task. To address this problem, we put forward a novel research problem called Crowdsourced Testing Quality Maximization Under Context Coverage Constraint (Cocoon). Given a mobile app test task, our objective is to recommend a set of workers, from available crowd workers, such that the expected test context coverage and a high test quality can be achieved. We prove that the Cocoon problem is NP-Complete and then introduce two greedy approaches. Based on a real dataset from the largest Chinese crowdsourced testing platform, our evaluation shows the effectiveness and efficiency of the two approaches, which can be potentially used as online services in practice.

Abstract: The present invention comprises a testing system for testing X Servers. The testing system comprises a test harness that communicates with an X Server being tested to obtain the test results therefrom, an archive database for storing test archives to be used by the test harness for testing the X Server, a test result storage database for storing results of an X Server test, and a viewing tool that presents the user with a result file which the user analyzes to determine the X Server defect. Preferably, the test harness is object-oriented code that has a polymorphic and hierarchical structure. The basic units of the test harness are objects, such as display connections, screens, graphics contexts, pixmaps, colormaps and windows. Within the test context, each object encodes a unique hierarchy that indicates its dependencies on other test harness objects. These objects encapsulate Xlib routines and hide much of the detail of Xlib programming from the test writer, thus facilitating the test writer in writing tests. Once a test has been written, the test is run and the results of the test, if they are correct, are stored as an archive file in the test archive storage database for later use. When a test is run on an X Server, the test harness captures the image rendered to the screen by the X Server. The test harness then searches the archive database in a predetermined manner to obtain the appropriate test archive. The test archive is then compared to the test results. If the test failed, then a defect exists in the X Server.

Abstract: Despite its potential to improve lexical selection, most state-of-the-art machine translation systems take only minimal contextual information into account. We capture context with a topic model over distributional profiles built from the context words of each translation unit. Topic distributions are inferred for each translation unit and used to adapt the translation model dynamically to a given test context by measuring their similarity. We show that combining information from both local and global test contexts helps to improve lexical selection and outperforms a baseline system by up to 1.15 BLEU. We test our topic-adapted model on a diverse data set containing documents from three different domains and achieve competitive performance in comparison with two supervised domain-adapted systems.