Recent advancements in technology have emerged the requirements of hardware and software applications. Along with this technical growth, software industries also have faced drastic growth in the demand of software for several applications. For any software industry, developing good quality software and maintaining its eminence for user end is considered as most important task for software industrial growth. In order to achieve this, software engineering plays an important role for software industries. Software applications are developed with the help of computer programming where codes are written for desired task. Generally, these codes contain some faulty instances which may lead to the buggy software development cause due to software defects. In the field of software engineering, software defect prediction is considered as most important task which can be used for maintaining the quality of software. Defect prediction results provide the list of defect-prone source code artefacts so that quality assurance team scan effectively allocate limited resources for validating software products by putting more effort on the defect-prone source code. As the size of software projects becomes larger, defect prediction techniques will play an important role to support developers as well as to speed up time to market with more reliable software products. One of the most exhaustive and pricey part of embedded software development is consider as the process of finding and fixing the defects. Due to complex infrastructure, magnitude, cost and time limitations, monitoring and fulfilling the quality is a big challenge, especially in automotive embedded systems. However, meeting the superior product quality and reliability is mandatory. Hence, higher importance is given to V&V (Verification & Validation). Software testing is an integral part of software V&V, which is focused on promising accurate functionality and long-term reliability of software systems. Simultaneously, software testing requires much effort, cost, infrastructure and expertise as the development. The costs and efforts elevate in safety critical software systems. Therefore, it is essential to have a good testing strategy for any industry with high software development costs. In this work, we are planning to develop an efficient approach for software defect prediction by using soft computing based machine learning techniques which helps to predict optimize the features and efficiently learn the features.

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Survey on software defect prediction techniques

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

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Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

The structure of RSONN can be self-organized based on the contributions of each hidden node, which uses not only the past states but also the current states.The appropriately adjusted learning rates of RSONN is derived based on the Lyapunov stability theorem. Moreover, the convergence of the proposed RSONN is discussed.An experimental hardware, including the proposed soft computing method is set up. The experimental results have confirmed that the soft computing method exhibits satisfactory predicting performance for SVI. In this paper, a soft computing method, based on a recurrent self-organizing neural network (RSONN) is proposed for predicting the sludge volume index (SVI) in the wastewater treatment process (WWTP). For this soft computing method, a growing and pruning method is developed to tune the structure of RSONN by the sensitivity analysis (SA) of hidden nodes. The redundant hidden nodes will be removed and the new hidden nodes will be inserted when the SA values of hidden nodes meet the criteria. Then, the structure of RSONN is able to be self-organized to maintain the prediction accuracy. Moreover, the convergence of RSONN is discussed in both the self-organizing phase and the phase following the modification of the structure for the soft computing method. Finally, the proposed soft computing method has been tested and compared to other algorithms by applying it to the problem of predicting SVI in WWTP. Experimental results demonstrate its effectiveness of achieving considerably better predicting performance for SVI values.

A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network

Data-driven intelligent monitoring system for key variables in wastewater treatment process

With the development of Web service technology, more and more enterprises choose to publish their own services on the Internet. However, with the increasing demands of users, it is difficult for a single service to meet the complex user requirements. To address this challenge, multiple services can be integrated by leveraging the service-oriented architecture (SOA) to generate a value-added service, referred to a service composition, where the component services are loosely coupled. However, due to the dynamic running environment, the performance of each component service (including reliability) may fluctuate. This will introduce cascading effects, which could cause the entire service system to fail. Since component services run in a dynamic environment, the parameters used to conduct reliability prediction are difficult to obtain. Therefore, online reliability prediction that ensures the runtime quality poses a grand challenge. This paper analyzes the historical response time series and throughput time series of component services, and predicts the reliability in the near future. To guarantee the stable and continuous operation of a service system, we proposed an online reliability time series prediction method by combining a Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The proposed approach, referred to as CL-ROP, is able to predict the reliability of a service system in the near future. We conducted a series of experiments over real service data and compared with other competitive approaches to demonstrate the effectiveness of the proposed approach.

Online reliability time series prediction via convolutional neural network and long short term memory for service-oriented systems

Vehicular Ad-hoc Network (VANET) is a collection of communication vehicles, moving in different directions. The vehicles form a communication group to disseminate desired information. Various routing protocols are implemented in a VANET, each having benefits and shortcoming in the domain of implementation. In this paper performance of three routing protocols, namely Ad hoc On-Demand Distance Vector Routing (AODV), Destination Sequenced Distance Vector (DSDV) and Dynamic Source Routing (DSR) is compared for different parameters. The protocols are simulated on Network Simulator-2(ns- 2).

Performance Comparison of VANET Routing Protocols

Traditional parametric software reliability growth models (SRGMs) are based on some assumptions or distributions and none such single model can produce accurate prediction results in all circumstances. Non-parametric models like the artificial neural network (ANN) based models can predict software reliability based on only fault history data without any assumptions. In this paper, initially we propose a robust feedforward neural network (FFNN) based dynamic weighted combination model (PFFNNDWCM) for software reliability prediction. Four well-known traditional SRGMs are combined based on the dynamically evaluated weights determined by the learning algorithm of the proposed FFNN. Based on this proposed FFNN architecture, we also propose a robust recurrent neural network (RNN) based dynamic weighted combination model (PRNNDWCM) to predict the software reliability more justifiably. A real-coded genetic algorithm (GA) is proposed to train the ANNs. Predictability of the proposed models are compared with the existing ANN based software reliability models through three real software failure data sets. We also compare the performances of the proposed models with the models that can be developed by combining three or two of the four SRGMs. Comparative studies demonstrate that the PFFNNDWCM and PRNNDWCM present fairly accurate fitting and predictive capability than the other existing ANN based models. Numerical and graphical explanations show that PRNNDWCM is promising for software reliability prediction since its fitting and prediction error is much less relative to the PFFNNDWCM.

Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction

This paper propose a learning algorithm of supervised back‐propagation neural networks for dynamic weighted combination of software reliability model. The proposed model is an assimilation of 3 well‐known non‐homogeneous poisson process (NHPP)–based software reliability growth models with imperfect debugging. The novel approach of proposed supervised back propagation–based neural network 2‐stage architecture has a great impact on the network by combining the imperfect debugging models based on the nature of fault introduction rate during testing and debugging. Function approximation metrics are used for comparing the proposed model with individual models. Three data sets are trained using supervised back‐propagation neural networks to compare the performance and validity evaluation of proposed and existing NHPP models and dynamic weighted combinational model. Reliability analysis among important NHPP models incorporating imperfect debugging is illustrated through numerical and graphical explanation of several metrics using supervised back‐propagation neural networks.

Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging

This article develops a particle swarm optimization algorithm based on a feed-forward neural network architecture to fit software reliability growth models. We employ adaptive inertia weight within...

A neuro-particle swarm optimization logistic model fitting algorithm for software reliability analysis:

Heart disease is a type of chronic disease that can lead to death if not diagnosed in time. A clinical decision support system developed using machine learning technology can be used in the diagnosis of disease. Proper utilization of feature selection increases classification accuracy by reducing the computational cost. The purpose of this research work is to purpose a new Hybrid Pearson Correlation with Backward Elimination (HPCBE) feature selection method. Proposed HPCBE method is further used to develop a hybrid system for heart disease diagnosis (HSHDD).HPCBE is proposed by combining pearson correlation (PC) and backward elimination (BE) methods. Reduced feature subset selected by HPCBE method is used along with decision tree (DT), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) classifiers to develop HSHDD. The feature reduction ratio of 53.84% is achieved by the proposed HPCBE Feature Selection method. HSHDD achieved a maximum accuracy of 86.49% in heart disease classification with AdaBoost classifier.

Pooja Rani

Papers

Performance Comparison of VANET Routing Protocols

Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction

Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging

A neuro-particle swarm optimization logistic model fitting algorithm for software reliability analysis:

A Hybrid System for Heart Disease Diagnosis Based on HPCBE Method