Connecting Software Metrics across Versions to Predict Defects

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models

Absorption and Transport of Inorganic Carbon in Kelps with Emphasis on Saccharina japonica

Due to the low CO2 concentration in seawater, macroalgae including Saccharina japonica have developed mechanisms for using the abundant external pool of HCO3− as an exogenous inorganic carbon (Ci) source. Otherwise, the high photosynthetic efficiency of some macroalgae indicates that they might possess CO2 concentrating mechanisms (CCMs) to elevate CO2 concentration intracellularly around the active site of ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCo). As the photosynthetic modes of macroalgae are diverse (C3, C4 or a combination of C3 and C4 pathway), CCMs in different carbon fixation pathways should vary correspondingly. However, both in C3 and C4 pathways, carbonic anhydrase (CA) plays a key role by supplying either CO2 to RuBisCO or HCO3− to PEPC. Over the past decade, although CA activities have been detected in a number of macroalgae, genes of CA family, expression levels of CA genes under different CO2 concentrations, as well as subcellular location of each CA have been rarely reported. Based on analysis the reported high-throughput sequencing data of S. japonica, 12 CAs of S. japonica (SjCA) genes were obtained. Neighbor-Joining (NJ) phylogenetic tree of SjCAs constructed using Mega6.0 and the subcellular location prediction of each CA by WoLFPSORT are also conducted in this article

Temperature dependence of circular DNA topological states

Circular double stranded DNA has different topological states which are defined by their linking numbers. Equilibrium distribution of linking numbers can be obtained by closing a linear DNA into a circle by ligase. Using Monte Carlo simulation, we predict the temperature dependence of the linking number distribution of small circular DNAs. Our predictions are based on flexible defect excitations resulted from local melting or unstacking of DNA base pairs. We found that the reduced bending rigidity alone can lead to measurable changes of the variance of linking number distribution of short circular DNAs. If the defect is accompanied by local unwinding, the effect becomes much more prominent. The predictions can be easily investigated in experiments, providing a new method to study the micromechanics of sharply bent DNAs and the thermal stability of specific DNA sequences. Furthermore, the predictions are directly applicable to the studies of binding of DNA distorting proteins that can locally reduce DNA rigidity, form DNA kinks, or introduce local unwinding.Comment: 15 pages in preprint format, 4 figure

Research on Chaotic Firefly Algorithm and the Application in Optimal Reactive Power Dispatch

Firefly algorithm (FA) is a newly proposed swarm intelligence optimization algorithm. The original version of FA usually traps into local optima like many other general swarm intelligence optimization algorithm. In order to overcome this drawback, the chaotic firefly algorithm(CFA) is proposed. The methods of chaos initialization, chaos population regeneration and linear decreasing inertia weight have been introduced into the original version of FA so as to increase its global search mobility for robust global optimization. The CFA is calculated in Matlab and is examined on six benchmark functions. In order to evaluate the engineering application of the algorithm, the reactive power optimization problem in IEEE 30 bus system is solved by CFA. The outcomes show that the CFA has better performance compared to the original version of FA and PS

Fuzzy logic based metric in software testing

How to provide cost-effective strategies for Software Testing has been one of the research focuses in Software Engineering for a long time. Many researchers in Software Engineering have addressed the effectiveness and quality metric of Software Testing, and many interesting results have been obtained. However, one issue of paramount importance in software testing &ndash; the intrinsic imprecise and uncertain relationships within testing metrics &ndash; is left unaddressed. To this end, a new quality and effectiveness measurement based on fuzzy logic is proposed. The software quality features and analogy-based reasoning are discussed, which can deal with quality and effectiveness consistency between different test projects. Experimental results are also provided to verify the proposed measurement.<br /

An analysis of the isoparametric bilinear finite volume element method by applying the Simpson rule to quadrilateral meshes

In this work, we construct and study a special isoparametric bilinear finite volume element scheme for solving anisotropic diffusion problems on general convex quadrilateral meshes. The new scheme is obtained by employing the Simpson rule to approximate the line integrals in the classical isoparametric bilinear finite volume element method. By using the cell analysis approach, we suggest a sufficient condition to ensure the coercivity of the new scheme. The sufficient condition has an analytic expression, which only involves the anisotropic diffusion tensor and the geometry of quadrilateral mesh. This yields that for any diffusion tensor and quadrilateral mesh, we can directly judge whether this sufficient condition is satisfied. Specifically, this condition covers the traditional $h^{1+\gamma}$-parallelogram and some trapezoidal meshes with any full anisotropic diffusion tensor. An optimal $H^1$ error estimate of the proposed scheme is also obtained for a quasi-parallelogram mesh. The theoretical results are verified by some numerical experiments

Class-level Multiple Distributions Representation are Necessary for Semantic Segmentation

Existing approaches focus on using class-level features to improve semantic segmentation performance. How to characterize the relationships of intra-class pixels and inter-class pixels is the key to extract the discriminative representative class-level features. In this paper, we introduce for the first time to describe intra-class variations by multiple distributions. Then, multiple distributions representation learning(\textbf{MDRL}) is proposed to augment the pixel representations for semantic segmentation. Meanwhile, we design a class multiple distributions consistency strategy to construct discriminative multiple distribution representations of embedded pixels. Moreover, we put forward a multiple distribution semantic aggregation module to aggregate multiple distributions of the corresponding class to enhance pixel semantic information. Our approach can be seamlessly integrated into popular segmentation frameworks FCN/PSPNet/CCNet and achieve 5.61\%/1.75\%/0.75\% mIoU improvements on ADE20K. Extensive experiments on the Cityscapes, ADE20K datasets have proved that our method can bring significant performance improvement

Calcium channel α2δ1 proteins mediate trigeminal neuropathic pain states associated with aberrant excitatory synaptogenesis.

To investigate a potential mechanism underlying trigeminal nerve injury-induced orofacial hypersensitivity, we used a rat model of chronic constriction injury to the infraorbital nerve (CCI-ION) to study whether CCI-ION caused calcium channel α2δ1 (Cavα2δ1) protein dysregulation in trigeminal ganglia and associated spinal subnucleus caudalis and C1/C2 cervical dorsal spinal cord (Vc/C2). Furthermore, we studied whether this neuroplasticity contributed to spinal neuron sensitization and neuropathic pain states. CCI-ION caused orofacial hypersensitivity that correlated with Cavα2δ1 up-regulation in trigeminal ganglion neurons and Vc/C2. Blocking Cavα2δ1 with gabapentin, a ligand for the Cavα2δ1 proteins, or Cavα2δ1 antisense oligodeoxynucleotides led to a reversal of orofacial hypersensitivity, supporting an important role of Cavα2δ1 in orofacial pain processing. Importantly, increased Cavα2δ1 in Vc/C2 superficial dorsal horn was associated with increased excitatory synaptogenesis and increased frequency, but not the amplitude, of miniature excitatory postsynaptic currents in dorsal horn neurons that could be blocked by gabapentin. Thus, CCI-ION-induced Cavα2δ1 up-regulation may contribute to orofacial neuropathic pain states through abnormal excitatory synapse formation and enhanced presynaptic excitatory neurotransmitter release in Vc/C2