Target Directed Event Sequence Generation for Android Applications

Testing is a commonly used approach to ensure the quality of software, of which model-based testing is a hot topic to test GUI programs such as Android applications (apps). Existing approaches mainly either dynamically construct a model that only contains the GUI information, or build a model in the view of code that may fail to describe the changes of GUI widgets during runtime. Besides, most of these models do not support back stack that is a particular mechanism of Android. Therefore, this paper proposes a model LATTE that is constructed dynamically with consideration of the view information in the widgets as well as the back stack, to describe the transition between GUI widgets. We also propose a label set to link the elements of the LATTE model to program snippets. The user can define a subset of the label set as a target for the testing requirements that need to cover some specific parts of the code. To avoid the state explosion problem during model construction, we introduce a definition "state similarity" to balance the model accuracy and analysis cost. Based on this model, a target directed test generation method is presented to generate event sequences to effectively cover the target. The experiments on several real-world apps indicate that the generated test cases based on LATTE can reach a high coverage, and with the model we can generate the event sequences to cover a given target with short event sequences

Effects of Finite Deformed Length in Carbon Nanotubes

The effect of finite deformed length is demonstrated by squashing an armchair (10,10) single-walled carbon nanotube with two finite tips. Only when the deformed length is long enough, an effectual metal-semiconductor-metal heterojunction can be formed in the metallic tube. The effect of finite deformed length is explained by the quantum tunnelling effect. Furthermore, some conceptual designs of nanoscale devices are proposed from the metal-semiconductor-metal heterojunction.Comment: 4 pages, 4 figure

Converting normal insulators into topological insulators via tuning orbital levels

Tuning the spin-orbit coupling strength via foreign element doping and/or modifying bonding strength via strain engineering are the major routes to convert normal insulators to topological insulators. We here propose an alternative strategy to realize topological phase transition by tuning the orbital level. Following this strategy, our first-principles calculations demonstrate that a topological phase transition in some cubic perovskite-type compounds CsGeBr$_3$ and CsSnBr$_3$ could be facilitated by carbon substitutional doping. Such unique topological phase transition predominantly results from the lower orbital energy of the carbon dopant, which can pull down the conduction bands and even induce band inversion. Beyond conventional approaches, our finding of tuning the orbital level may greatly expand the range of topologically nontrivial materials

Reconsideration of the QCD corrections to the ηc\eta_c decays into light hadrons using the principle of maximum conformality

In the paper, we analyze the $\eta_c$ decays into light hadrons at the next-to-leading order QCD corrections by applying the principle of maximum conformality (PMC). The relativistic correction at the ${\cal{O}}(\alpha_s v^2)$-order level has been included in the discussion, which gives about $10\%$ contribution to the ratio $R$. The PMC, which satisfies the renormalization group invariance, is designed to obtain a scale-fixed and scheme-independent prediction at any fixed order. To avoid the confusion of treating $n_f$-terms, we transform the usual $\overline{\rm MS}$ pQCD series into the one under the minimal momentum space subtraction scheme. To compare with the prediction under conventional scale setting, $R_{\rm{Conv,mMOM}-r}= \left(4.12^{+0.30}_{-0.28}\right)\times10^3$, after applying the PMC, we obtain $R_{\rm PMC,mMOM-r}=\left(6.09^{+0.62}_{-0.55}\right) \times10^3$, where the errors are squared averages of the ones caused by $m_c$ and $\Lambda_{\rm mMOM}$. The PMC prediction agrees with the recent PDG value within errors, i.e. $R^{\rm exp}=\left(6.3\pm0.5\right)\times10^3$. Thus we think the mismatching of the prediction under conventional scale-setting with the data is due to improper choice of scale, which however can be solved by using the PMC.Comment: 5 pages, 2 figure