Just-in-time defect prediction for software hunks

Just-in-time defect prediction can remind software developers and managers to verify and fix bugs at the moment they appeared, thus improving the effectiveness and validity of bug fixing. Existing studies mainly focus on just-in-time prediction for software files (JIT-F). JIT-F is a binary classification problem, which classifies (hence predicts) a file change as buggy or clean. This article provides a detailed analysis of just-in-time defect prediction for software hunks (JIT-H), which predicts bugs at a finer level of granularity, and hence further improves the efficiency of bug fixing. Classification is performed using the ensemble technique of bagging—aggregated combinations of random under sampling plus multiple classifiers (J48 and Random Forest). An empirical study with 10 open source projects was conducted to validate the effectiveness of JIT-H. Experimental results show that JIT-H is effective at predicting defects in software hunk changes. Compared with JIT-F, JIT-H is more cost effective. Additionally, analysis on the change features indicates that Text Vector features and hunk change level features are of more importance than features in other groups and levels.     

Evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm for data clustering

Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets. The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.     

微波辅助萃取-超高效液相色谱/静电场轨道阱高分辨质谱法快速测定纺织品中有害有机溶剂残留量

建立了1个快速测定纺织品中有害有机溶剂残留量的超高效液相色谱/静电场轨道阱高分辨质谱(UPLC/Orbitrap HRMS)方法,该方法以乙醇为萃取溶剂,100℃下微波萃取纺织品中残留的有害有机溶剂,萃取液直接进行UPLC/Orbitrap HRMS,提取离子色谱峰面积外标法定量。对样品的提取条件、分析条件进行了优化。该方法的定量限为0.5μg/kg~10.0μg/kg。在3个不同加标浓度水平下,方法的平均加标回收率为81.8~94.5%,相对偏差偏差为3.8%~10.5%(n=9)。该方法简便快速,灵敏度高,定量限低,可有效地解决纺织品中残留的有害有机溶剂的快速测定问题。     

Monolithically integrated CoP nanowire array: An on/off switch for effective on-demand hydrogen generation via hydrolysis of NaBH<Subscript>4</Subscript> and NH<Subscript>3</Subscript>BH<Subscript>3</Subscript>

The issues of hydrogen generation and storage have hindered the widespread use and commercialization of hydrogen fuel cell vehicles.It is thus highly attractive,but the design and development of highly active non-noble-metal catalysts for on-demand hydrogen release from alkaline NaBH4 solution under mild conditions remains a key challenge.Herein,we describe the use of CoP nanowire array integrated on a Ti mesh (CoP NA/Ti) as a three-dimensional (3D) monolithic catalyst for efficient hydrolytic dehydrogenation of NaBH4 in basic solutions.The CoP NA/Ti works as an on/off switch for on-demand hydrogen generation at a rate of 6,500 mL/(min.g) and a low activation energy of 41 kJ/mol.It is highly robust for repeated usage after recycling,without sacrificing catalytic performance.Remarkably,this catalyst also performs efficiently for the hydrolysis of NH3BH3.     

Epitaxial growth and thermal-conductivity limit of single-crystalline Bi2Se3/In2Se3 superlattices on mica

Nano Research - Thermal transport in superlattices is governed by various phonon-scattering processes. For extracting the phonon-scattering contribution of hetero-interfaces in chalcogenide...     

Two-photon-excited near-infrared emissive carbon dots as multifunctional agents for fluorescence imaging and photothermal therapy

C dots (CDs) have shown great potential in bioimaging and phototherapy. However, it is challenging to manipulate their fluorescent properties and therapeutic efficacy to satisfy the requirements for clinic applications. In this study, we prepared S, Se-codoped CDs via a hydrothermal method and demonstrated that the doping resulted in excitation wavelength-independent near-infrared (NIR) emissions of the CDs, with peaks at 731 and 820 nm. Significantly, the CDs exhibited a photothermal conversion efficiency of ~58.2%, which is the highest reported value for C nanostructures and is comparable to that of Au nanostructures. Moreover, the CDs had a large two-photon absorption cross section (~30,045 GM), which allowed NIR emissions and the photothermal conversion of the CDs through the two-photon excitation (TPE) mechanism. In vitro and in vivo tests suggested that CDs can function as new multifunctional phototheranostic agents for the TPE fluorescence imaging and photothermal therapy of cancer cells.

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Bootstrapping Analysis of Lifetime Data with Subsampling

Because of cost and time limitations, reliability experiments frequently contain subsampling, which is a restriction on randomization. A two‐stage approach can analyze right censored Weibull distributed reliability data with subsampling. However, in implementing such a method, we found that it did not address the problems of how to perform confidence intervals of low percentiles and reduce the bias of estimates. In this paper, we present a two‐stage bootstrapping approach and an unbiasing factor approach to solve the aforementioned problems. An example is provided to illustrate the proposed method. In addition, the proposed method is compared with existing methods through simulation. The resulting simulations show that the proposed method performs well in low percentiles. Copyright © 2015 John Wiley & Sons, Ltd.