The selection of software metrics for building software quality prediction models is a search-based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault-proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real-world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85% of the software metrics were eliminated.

A graphene-based supercapacitor electrode suitable for ionic liquid electrolytes was designed and prepared based on the electrostatic interactions between negatively charged graphene oxides (GO) and positively charged mesoporous carbon CMK-5 platelets. Thermal annealing of the GO-CMK-5 composite under an inert atmosphere yielded a hierarchical carbon nanostructure with CMK-5 platelets uniformly intercalated between the GO sheets. The electrochemical results demonstrated that the CMK-5 platelets with straight and short mesochannels served as a highway for the fast transport of electrolyte ions, while the separated graphene sheets with more exposed electrochemical surface area favored the formation of electrical double layer capacitance. The RGO-CMK-5 electrode exhibited a specific capacitance of 144.4 F g À1 in 1-ethyl-3-methylimidazolium tetrafluoroborate ionic liquid electrolyte, which can be charged/discharged at an operating voltage of 3.5 V. As a result, an energy density of 60.7 W h kg À1 and a power density as high as 10 kW kg À1 were achieved, which outperforms most of the present graphene-based supercapacitors. Moreover, the superior rate performance together with the excellent cycle performance makes the RGO-CMK-5 composite a promising candidate for next generation supercapacitor electrodes.

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A facile yet effective chemical vapor deposition (CVD) method to prepare carbon nanomesh (CNM) with MgAl‐layered double oxides (LDO) as sacrificial template and ferrocene as carbon precursor is reported. Due to the combined effect of the LDO template and organometallic precursor, the as‐made hexagonal thin‐sheet CNM features a hierarchical pore system consisting of micropores and small mesopores with a size range of 1–6 nm, and a great number of random large mesopores with a pore size of 10–50 nm. The density, geometry, and size of the pores are strongly dependent on the CVD time and the annealing conditions. As supercapacitor electrode, the CNM exhibits an enhanced capacitance, high rate capability, and outstanding cycling performance with a much‐shortened time constant. The excellent capacitive performance is due to the presence of the large mesopores in the 2D CNM, which not only offer additional ion channels to accelerate the diffusion rate across the thin sheets but also help to make efficient use of the oxygen functional groups at the edges of large mesopores to increase the pseudocapacitance contribution.

Creation of nanopores on graphene planar sheets is of great significance in promoting the kinetic diffusion of electrolyte and enhancing the utilization efficiency of graphene planar sheets. Herein, we developed a facile chemical vapor deposition strategy to prepare highly porous graphene with flake-like MgO as template and ferrocene as the carbon precursor. The graphene layers show a highly porous structure with small mesopores of 4-8 nm, large mesopores of 10-20 nm and additional macropores of 100-200 nm. These nanopores on graphene sheets provide numerous channels for fast ion transport perpendicular to the 2D basal plane, while the good powder conductivity ensures an effective electron propagation within the 2D graphene plane. As a result, a specific capacitance of 303 F g(-1), an areal capacitance up to 17.3 μF cm(-2) and a nearly tenfold shorter time constant were achieved when compared with those of nonporous and stacked graphene electrodes. The method demonstrated herein would open up an opportunity to prepare porous graphene for a wide applications in energy storage, biosensors, nanoelectronics and catalysis.