Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

State of Health Estimation of LiFePO_(4) Batteries for Battery Management Systems

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

Nd-doped NiFe_(2)O_(4) and its composite with CNTs to tune photocatalytic activity

Rare earth-doped spinel nano ferrites are attaining importance as heterogeneous nanocatalysts for the degradation of organic effluents.Rare earth metal doping increases the electrical and optical properties,as well as the surface-to-volume ratio of the bare sample.In this work,NiFe_(2)O_(4)(NF) and Nd-NiFe_(2)O_(4)(NF-1) were successfully synthesized via the co-precipitation route.Carbon nanotubes(CNT)-based nanocomposite(NF-2) was prepared using the ultra-sonication method.The prepared materials were analyzed via various physiochemical approaches.The degradation efficiency of these materials was analyzed for the degradation of Rhodamine B,methylene blue,and benzoic acid,NF-2 shows the highest efficiency among all the prepared catalysts.NF-2 shows 83.87%,90.80%,and 66.96% degradation of Rhodamine B,methylene blue,and benzoic acid,respectively.The reason for the superior activity of NF-2 is the existence of rare earth Nd ions and CNTs.The surface area of NF increases due to the presence of carbon nanotubes and enhanced surface area provides more active sites for the degradation reaction.