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Most of the existing classification accuracy indices of attribute patterns lose effectiveness when the response data is absent in diagnostic testing. To handle this issue, this article proposes new indices to predict the correct classification rate of a diagnostic test before administering the test under the deterministic noise input "and" gate (DINA) model. The new indices include an item-level expected classification accuracy (ECA) for attributes and a test-level ECA for attributes and attribute patterns, and both of them are calculated based solely on the known item parameters and Q-matrix. Theoretical analysis showed that the item-level ECA could be regarded as a measure of correct classification rates of attributes contributed by an item. This article also illustrates how to apply the item-level ECA for attributes to estimate the correct classification rate of attributes patterns at the test level. Simulation results showed that two test-level ECA indices, ECA_I_W (an index based on the independence assumption and the weighted sum of the item-level ECAs) and ECA_C_M (an index based on Gaussian Copula function that incorporates the dependence structure of the events of attribute classification and the simple average of the item-level ECAs), could make an accurate prediction for correct classification rates of attribute patterns.

Mocking is an essential unit testing technique for isolating the class under test from its dependencies. Developers often leverage mocking frameworks to develop stub code that specifies the behaviors of mock objects. However, developing and maintaining stub code is labor-intensive and error-prone. In this article, we present StubCoder to automatically generate and repair stub code for regression testing. StubCoder implements a novel evolutionary algorithm that synthesizes test-passing stub code guided by the runtime behavior of test cases. We evaluated our proposed approach on 59 test cases from 13 open source projects. Our evaluation results show that StubCoder can effectively generate stub code for incomplete test cases without stub code and repair obsolete test cases with broken stub code. [ABSTRACT FROM AUTHOR]

Extant studies of environmental investment determinants have not distinguished between different types of political connections. This study tries to close this gap by employing an extensive dataset based on the four-yearly Chinese Private Enterprises Survey conducted between 2006 and 2012. The central question in this paper asks whether different political connections (ascribed vs. achieved) are fostering or hindering environmental protection expenditures in private enterprises. The results show that achieved political connections serve as binds of promoting firm environmental investment while ascribed political connections act as buffers, hampering firm environmental expenditures. The moderating roles of environmental regulation and innovation capability demonstrate heterogeneous effects: environmental regulation stringency strengthens the positive impact of achieved political connections only. In contrast, innovation capability enhances the negative impact of ascribed political connections. [ABSTRACT FROM AUTHOR]

With the rapid economic development, the serious air pollution in Beijing attracts increasing attention in the last decade. Seen as one whole complex and grey system, the causal relationship between the social development and the air pollution in Beijing has been quantitatively analyzed in this paper. By using the grey relational model, the aim of this study is to explore how the socio-economic and human activities affect on the air pollution in the city of Beijing, China. Four air pollutants, as the particulate matter with size 2.5 micrometers or less (PM2.5), particulate matter with size 10 micrometers or less (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NOx), are selected as the indicators of air pollution. Additionally, fifteen socio-economic indicators are selected to account for the regional socio-economic characteristics (economy variables, energy consumption variables, pollution emissions variables, environment and construction activity variables). The results highlight that all variables are associated with the concentrations of the four selected air pollutants, but with notable differences between the air pollutants. Most of the socio-economic indicators, such as industrial output, total energy consumption are highly correlated with PM2.5, while PM10, SO2, and NOx present in general moderate correlations with most of the socio-economic variables. Contrary to other studies and reports this study reveals that vehicles and life energy do not have the strongest effect on air pollution in Beijing. This study provides useful information to reduce air pollution and support decision-making for sustainable development.

Classification consistency and accuracy are viewed as important indicators for evaluating the reliability and validity of classification results in cognitive diagnostic assessment (CDA). Pattern-level classification consistency and accuracy indices were introduced by Cui, Gierl, and Chang. However, the indices at the attribute level have not yet been constructed. This study puts forward a simple approach to estimating the indices at both the attribute and the pattern level through one single test administration. Detailed elaboration is made on how the upper and lower bounds for the attribute-level accuracy can be derived from the variance of error of the attribute mastery probability estimate. In addition, based on Cui's pattern-level indices, an alternative approach to estimating the attribute-level indices is also proposed. Comparative analysis of simulation results indicate that the new indices are very desirable for evaluating test-retest consistency and correct classification rate.