A systematic review of software fault prediction studies

This paper provides a systematic review of previous software fault prediction studies with a specific focus on metrics, methods, and datasets. The review uses 74 software fault prediction papers in 11 journals and several conference proceedings. According to the review results, the usage percentage of public datasets increased significantly and the usage percentage of machine learning algorithms increased slightly since 2005. In addition, method-level metrics are still the most dominant metrics in fault prediction research area and machine learning algorithms are still the most popular methods for fault prediction. Researchers working on software fault prediction area should continue to use public datasets and machine learning algorithms to build better fault predictors. The usage percentage of class-level is beyond acceptable levels and they should be used much more than they are now in order to predict the faults earlier in design phase of software life cycle.     

Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem

Software quality engineering comprises of several quality assurance activities such as testing, formal verification, inspection, fault tolerance, and software fault prediction. Until now, many researchers developed and validated several fault prediction models by using machine learning and statistical techniques. There have been used different kinds of software metrics and diverse feature reduction techniques in order to improve the models’ performance. However, these studies did not investigate the effect of dataset size, metrics set, and feature selection techniques for software fault prediction. This study is focused on the high-performance fault predictors based on machine learning such as Random Forests and the algorithms based on a new computational intelligence approach called Artificial Immune Systems. We used public NASA datasets from the PROMISE repository to make our predictive models repeatable, refutable, and verifiable. The research questions were based on the effects of dataset size, metrics set, and feature selection techniques. In order to answer these questions, there were defined seven test groups. Additionally, nine classifiers were examined for each of the five public NASA datasets. According to this study, Random Forests provides the best prediction performance for large datasets and Naive Bayes is the best prediction algorithm for small datasets in terms of the Area Under Receiver Operating Characteristics Curve (AUC) evaluation parameter. The parallel implementation of Artificial Immune Recognition Systems (AIRS2Parallel) algorithm is the best Artificial Immune Systems paradigm-based algorithm when the method-level metrics are used.     

Decentralized routing controller design using overlapping decompositions

The use of overlapping decompositions in designing routing controllers for large-scale networks is considered and a decentralized dynamic routing controller design strategy is proposed. The strategy is based on the overlapping decompositions method and can be used for large-scale networks which have more than two overlapping subnetworks and/or have more than one node in the overlapping part. The controller obtained by the proposed strategy is decentralized in the sense that all on-line computations can be done locally at the individual nodes without any information transfer from the other nodes. The controller satisfies all the routing control constraints and it avoids any looping. The controller also clears the queues in finite time in the absence of external arrivals and it keeps the queue lengths bounded in the presence of external arrival rates which do not exceed a certain maximum rate. In order to illustrate the proposed controller's performance, the controller is applied to an example network and some simulation results are presented in a number of cases. Furthermore, a centrally designed controller is also applied to the same network for the same cases, in order to obtain a basis for comparison.     

Investigation of oxygen diffusion into contact lenses using electron spin resonance spectroscopy

Oxygen permeability is the most important parameter of contact lenses, as lack of oxygen causes corneal edema and threatens the vision of the patient. This study was unique in that it used an electron spin resonance (ESR) technique to determine the oxygen diffusion coefficient (D) of contact lenses. Although there are many methods and techniques for investigating oxygen diffusion into contact lenses, ESR was used for the first time in this study. The ESR technique is based on the scavenging of radicals produced in lenses by oxygen. As a contact lens is not a paramagnetic substance, it cannot give an ESR spectrum. But it does produce an ESR spectrum after γ irradiation. When a vacuum‐irradiated contact lens is exposed to air, the radicals trapped in the lens are transformed into peroxide radicals by the addition of molecular oxygen to the free radicals, and the ESR spectrum begins to change with time. This effect can be used as a tool to measure oxygen uptake in irradiated contact lenses. The oxygen diffusion coefficient of a contact lens was determined from changes in ESR signal intensity varying with time. The diffusion coefficients of oxygen for a contact lens were determined for rapid decay [(1.5 + 0.4) × 10^?8 cm²/s] and slow decay [(1.3 + 0.3) × 10^?9 cm²/s] in this study. These values are in agreement with the D values given in the literature for polymeric materials used for contact lenses. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 2937–2941, 2006     

A CMOS GSM IF-sampling circuit with reduced in-channel aliasing

A complex intermediate frequency (IF) sampling technique with intrinsic rejection of even-order aliasing channels is demonstrated. The circuit subsamples in-phase and quadrature IF signals and uses a discrete-time analog delay and an adder to notch out the undesired aliasing frequencies. A chip designed in 0.25-/spl mu/m CMOS technology demonstrates 27-dB antialiasing rejection for a 377-MHz IF GSM signal with 52-MHz sampling rate and 70-dB dynamic range.     

Epoxy–lignin polyblends. III. Thermal properties and infrared analysis

Differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA) and IR analysis were performed on a nonviscous epoxy polymer system (EP) with Kraft lignin (L) up to 20%. Mixtures of EP with similar amounts of silica (S) as in EP–L polyblends were used as a reference system for the analyses performed. EP–L polyblends cured at room temperature exhibit a single T_g, a fact characteristic for the monophasic systems. One-step tensile storage modulus vs. temperature curves, and related one-peak tensile loss modulus vs. temperature curves were found for all the EP–L polyblends. At about 30°C the tensile storage modulus of EP does not change in the presence of lignin in amounts up to 20% by weight. All these showed that L is miscible with EP and it does not affect the crosslinking at room temperature. IR spectra led to similar conclusions. The gradual decrease of the peak values of tan δ with the amount of L in polyblends is due to an increase in the tensile storage modulus and a decrease of the tensile loss modulus at temperatures close to T_g. This fact is explained by a stronger bond between EP and L, which could be formed at higher temperatures. The DSC and DMA data are in agreement with the mechanical properties of EP–L polyblends, which were reported previously.     

FTIR determination of ligand-induced secondary and tertiary structural changes in bovine plasminogen

Human plasminogen undergoes a large tertiary structural change in the presence of lysine derivatives (e.g. epsilon-amino caproic acid, EACA). This change facilitates human plasminogen activation by human plasminogen activators, resulting in elevated blood plasmin levels. It is hypothesized that this structure-function relationship is similar for bovine plasminogen. The objectives of this study were to investigate the effect of the ligand EACA on the secondary structure of plasminogen (bovine, human, and rabbit) and the tertiary structure of bovine plasminogen using Fourier-transform infrared spectroscopy (FTIR). Spectra of plasminogen, EACA, and a mixture of plasminogen and EACA in water and deuterium were collected using FTIR. Fourier-self deconvoluted spectra in the amide I region (1700-1600 cm(-1)) were used to detect changes in secondary structure of plasminogen after EACA addition. Change in bovine plasminogen tertiary structure was determined by comparing ratios of amide II (1600-1500 cm(-1)) to amide I bond intensities over time for samples in deuterium. No differences in secondary structure were observed for any plasminogen in the presence of EACA; however, addition of EACA significantly changed tertiary structure of bovine plasminogen. This tertiary structural change indicates a transition from a folded to an unfolded state, which could be more easily converted to plasmin. These results are consistent with reported human plasminogen studies using neutron scattering (tertiary structure) and circular dichroism (secondary structure) methods.     

Treatment of dairy wastewater using anaerobic and solar photocatalytic methods

The present study was aimed to treat the dairy wastewater by using anaerobic and solar photocatalytic oxidation methods. The anaerobic treatment was carried out in a laboratory scale hybrid upflow anaerobic sludge blanket reactor (HUASB) with a working volume of 5.9 L. It was operated at organic loading rate (OLR) varying from 8 to 20 kg COD/m³ day for a period of 110 days. The maximum loading rate of the anaerobic reactor was found to be 19.2 kg COD/m³ day and the corresponding chemical oxygen demand (COD) removal at this OLR was 84%. The anaerobically treated wastewater at an OLR of 19.2 kg COD/m³ day was subjected to secondary solar photocatalytic oxidation treatment. The optimum pH and catalyst loading for the solar photochemical oxidation was found to be 5 and 300 mg/L, respectively. The secondary solar photocatalytic oxidation using TiO₂ removed 62% of the COD from primary anaerobic treatment. Integration of anaerobic and solar photocatalytic treatment resulted in 95% removal of COD from the dairy wastewater. The findings suggest that anaerobic treatment followed by solar photo catalytic oxidation would be a promising alternative for the treatment of dairy wastewater.     

Synthesis and luminescent properties of Tb3+ activated AWO4 based (A = Ca and Sr) efficient green emitting phosphors

Optically efficient terbium activated alkaline earth metal tungstate nano phosphors (AWO₄ [A = Ca, Sr]) with different doping concentrations have been prepared by mechanochemically assisted solid state metathesis reaction at room temperature for the first time. The prepared phosphors were characterized by the X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscope (SEM), Fourier transform Raman (FT-Raman) spectroscopy, photoluminescence and diffuse reflectance spectroscopy measurements. The XRD and Raman spectra results showed that the prepared powders present a scheelite-type tetragonal structure. FTIR spectra exhibited a high absorption band situated at around 850 cm^?1, which was ascribed to the W–O antisymmetric stretching vibrations into the [WO₄]^2? tetrahedron groups and the SEM images reveal that the particle sizes were in the range of 20–60 nm. The excitation and the emission spectra were measured to characterize the luminescent properties of the phosphors. The excitation spectrum exhibits a charge transfer broad band along with some sharp peaks from the typical 4f–4f transitions of Tb³⁺. Under excitation of UV light, these AWO₄:xTb³⁺ (A = Ca, Sr) phosphors showed a strong emission band centered at 545 nm (green) which corresponds to ⁵ D ₄ → ⁷ F ₅ transition of Tb³⁺. Analysis of the emission spectra with different Tb³⁺ concentrations revealed that the optimum dopant concentration for CaWO₄:xTb³⁺ and SrWO₄:xTb³⁺ phosphors are about 8 and 6 mol% of Tb³⁺. The green emission intensity of the solid state meta-thesis prepared CaWO₄:0.08Tb³⁺ and SrWO₄:0.06Tb³⁺ phosphors are 1.5 and 1.2 times greater than that of the commercial LaPO₄:Ce, Tb green phosphor. All properties show that AWO₄:Tb³⁺ (A = Ca, Sr) is a very appropriate green-emitting phosphor for fluorescent lamp applications.