The fifth version of the Hypertext Markup Language (HTML) standard is widely adopted in the diverse landscape of browser vendors and their continuously upgrading releases. One primary feature of HTML5 is native multimedia playback. The browsers’ native implementations of multimedia support bring lots of benefits such as improved security but require thorough testing, especially on the web page of complex factor combinations according to our manual checking of publicly visible existing tests. This article employs the combinatorial testing technique to trigger failure-inducing factor combinations effectively and to locate them, guided by some extracted properties that cover browsers’ major workflow of processing multimedia. Results are analyzed to give objective suggestions for browser developers to cast light on the places that implementation enhancement could be made, and for web developers to avoid undesirable effects.

White-box testing is an effective technique for generating test cases to provide high coverage for programs. We usually select several execution paths using some strategy, and generate a corresponding test case that follows each path. Each execution path corresponds to an input subspace bounded by constraints. Extreme values in these subspaces are very likely to trigger failures since the constraints bounding input subspaces may be faulty. In this paper, we propose a new way of defining the boundaries of comparison predicates in white-box testing, and apply constrained combinatorial testing to cover these boundaries with reduced number of test cases. Our approach can guarantee to cover all possible boundaries for each selected execution path, thus achieving high fault coverage for boundary faults, while the original structural coverage is still preserved.

Android phones come with a host of hardware components embedded in them, such as Camera, Media Player and Sensor. Most of these components are exclusive resources or resources consuming more memory/energy than general. And they should be explicitly released by developers. Missing release operations of these resources might cause serious problems such as performance degradation or system crash. These kinds of defects are called resource leaks. This paper focuses on resource leak problems in Android apps, and presents our lightweight static analysis tool called Relda, which can automatically analyze an application's resource operations and locate the resource leaks. We propose an automatic method for detecting resource leaks based on a modified Function Call Graph, which handles the features of event-driven mobile programming by analyzing the callbacks defined in Android framework. Our experimental data shows that Relda is effective in detecting resource leaks in real Android apps.

In this paper, we study the testing of ID3v2 tags for MP3 files. We construct two combinatorial testing (CT) models for two test goals. One is for testing the audio players' recognition and display of variously encoded text information, and the other is for testing its robustness against bad header and frame sizes. We have conducted experiment son an on-vehicle leisure and entertainment system and a portable MP3player. We present some experimental results in this paper.

Combinatorial testing is a widely used black-box testing technique, which is used to detect failures caused by parameter interactions (we call them faulty interactions). Traditional combinatorial testing techniques provide fault detection, but most of them provide weak fault diagnosis. In this paper, we propose a new fault characterization method called faulty interaction characterization (FIC) and its binary search alternative FIC_BS to locate one failure-causing interaction in a single failing test case. In addition, we provide a tradeoff strategy of locating multiple faulty interactions in one test case. Our methods are based on adaptive black-box testing, in which test cases are generated based on outcomes of previous tests. For locating a t-way faulty interaction, the number of test cases used is at most k (for FIC) or t(dlog2 k] + 1) + 1 (for FIC_BS), where k is the number of parameters. Simulation experiments show that our method needs smaller number of adaptive test cases than most existing methods for locating randomly-generated faulty interactions. Yet it has stronger or equivalent ability of locating faulty interactions.