Anurag Singh Baghel

Implementing test case selection and reduction techniques using meta-heuristics

Regression Testing is an inevitable and very costly maintenance activity that is implemented to make sure the validity of modified software in a time and resource constrained environment. Execution of entire test suite is not possible so it is necessary to apply techniques like Test Case Selection and Test Case Prioritization to select and prioritize a minimum set of test cases, fulfilling some chosen criteria, that is, covering all possible faults in minimum time and other. In this paper a test case reduction hybrid Particle Swarm Optimization (PSO) algorithm has been proposed. This PSO algorithm uses GA mutation operator while processing. PSO is a swarm intelligence algorithm based on particles behavior. GA is an evolutionary algorithm (EA). The proposed algorithm is an optimistic approach which provides optimum best results in minimum time.

A genetic algorithm approach to release planning in agile environment

Agile software development methodology, got importance in recent years. The agile philosophy promotes incremental and iterative design and implementation. Each iterations, delivers one or more product features. Release planning is a main activity in any of Agile approach. Main factors that need to be considered are the technical precedence inherent in the requirements; the features business value perceived by project stake holders, team capacity and required effort to complete the requirement. There are multiple tools available in industry to manage project but they are lacking to provide planning while considering all these factors. Genetic algorithms (GA) have arisen from concepts, introduced from the natural process of biological evolution. GA uses selection, crossover and mutation to evolve a solution to the given problem. In this paper an attempt has been made to formalize the release planning. Then an approach is proposed to do Release planning using genetic algorithms.

Software reliability prediction modeling: A comparison of parametric and non-parametric modeling

Reliable softwares are the need of modern digital era. Failure nonlinearity makes software reliability a complicated task. Over past decades, many researchers have contributed many parametric / non parametric software reliability growth models and discussed their assumptions, applicability and predictability. It concluded that traditional parametric software reliability models have many shortcomings related to their unrealistic assumptions, environment-dependent applicability, and questionable predictability. In contrast to parametric software reliability growth models, the non-parametric software reliability growth models which use machine learning techniques or time series modeling have been proposed by researchers. This paper evaluates and compares the accuracy of 2 parametric and 2 non parametric software reliability growth models on 3 real-life data sets for software failures.

Parameter Estimation of Software Reliability Model Using Firefly Optimization

This paper, presents an effective parameter estimation technique for software reliability growth models using firefly algorithm. Software failure rate with respect to time has always been a foremost concern in the software industry. Every second organization aims to achieve defect free software products, which makes software reliability prediction a burning research area. Software reliability prediction techniques generally use numerical estimation method for parameter estimation, which is certainly not the best. Local optimization, biasness and model’s parameter initialization are some foremost limitation, which eventually suffers the finding of optimal model parameters. Firefly optimization overcomes these limitations and provides optimal solution for parameter estimation of software reliability growth models. Goel Okumoto model and Vtub based fault detection rate model is selected to validate the results. Seven real world datasets were used to compare the proposed technique against Cuckoo search technique and CASRE tool. The results indicate the superiority of proposed approach over existing numerical estimation techniques.

Performance evaluation of DSDV, AODV and LSGR protocol in ad-hoc networks

Ad Hoc network is a infrastructureless network which are mainly used for various types of wireless communication. This network is broadly categorized in to two types mobile ad hoc networks (MANET) vehicular ad hoc network (VANET) utilizes vehicles as mobile nodes in a MANET to constitute a mobile network. Many routing protocols for MANET have designed. The main protocols involved Ad-hoc On-demand Distance Vector (AODV), Destination-Sequenced Distance-Vector (DSDV) routing protocol and Link State Geographical Routing Protocol (LSGR). The performance of each protocol has been evaluated in terms of throughput, the normalized routing load, packet delivery ratio, delay etc. A network simulator-2.35 (NS-2) has been utilized for performance assessment of LSGR, AODV and DSDV. By considering the performance of each protocol it has been observed that LSGR protocol perform better than the DSDV and AODV protocol in ad hoc networks.

Spatial data mining: Recent trends and techniques

Data mining is process of discovering potential useful and interesting pattern hidden in large data set. Spatial data mining is emerged as new era in research community. Mining in spatial data is different in many aspect compare to mining in classical data. Spatial data mining application includes but not limited to social networking, disaster management and weather prediction. In this paper, a survey of currently available spatial data mining trend and techniques are presented.

Big Data Classification Using Scale-Free Binary Particle Swarm Optimization

Due to the data explosion, Big Data is everywhere all around of us. The curse of dimensionality in Big Data has produced a great challenge for data classification problems. Feature selection is a crucial process to select the most important features to increase the classification accuracy and to reduce the time complexity. Traditional feature selection approaches suffer from various limitations, so Particle Swarm Optimization (PSO)-based feature selection approaches are proposed to overcome these limitations, but classical PSO shows premature convergence when the number of features increases or the datasets having more categories/classes. In this paper, topology-controlled Scale-Free Particle Swarm Optimization (SF-PSO) is proposed for feature selection in high-dimensional datasets. Multi-Class Support Vector Machine (MC-SVM) is used as a machine learning classifier and obtained results show the superiority of our proposed approach in big data classification.

Parameter estimation of software reliability growth models using Krill Herd Algorithm

The proposed approach put forward an effective estimation of the parameters for software reliability growth models with the help of Krill Herd Algorithm. The Software Reliability Growth model is considered to be incomplete, if the parameters of model are not known and are not validated over failure datasets. Many techniques are present for estimating parameters of model based on numerical estimation, but the scope of quality enhancement still remain open. The proposed Krill Herd Algorithm based approach outclasses the problems of numerical estimation. The proposed work is validated over four real-time datasets.

Efficient parameter estimation of software reliability growth models using harmony search

The primary challenge of software reliability growth model is to find the unknown model parameters that are used to validate on software failure dataset. Though, numerical estimation technique plays a vital role in parameter estimation of software reliability growth models, they are not optimal as they suffer from constraints sucha as sample size, biasing, and initialisation of parameters. In this study, a parameter estimation of software reliability growth model that utilises a variant of harmony search is proposed. Extensive experiments are conducted on seven different software datasets of varying complexity. A robust experimental setup is developed employing an orthogonal array and Taguchi method. Two-fold performance comparisons are performed. First, the authors tested their proposed approach against Cuckoo search and numerical method (least square estimation) considering mean square error and Theils statistics as a quality measure. Second, the authors applied statistical tests are performed that demonstrate the superiority of their approach over the others. The underlying motivation to conduct this study is to motivate researchers to utilise their approach for a better estimation of model parameters.

BEP/SEP of binary and M-ary signals over k-µ shadowed fading channel

In this paper considering single channel communication over k-μ shadowed fading with arbitrary fading index, closed-form formulas for error probabilities are derived. BER/SER will be evaluated for different modulation/detection types using moment generating function-based approach. The average error probability expressions include Hypergeometric functions of several variables named as Appell hypergeometric function for two variables and Lauricella function for more than two variables. Furthermore, with the help of identities and reduction formulas we reduced our proposed results in the previous published results [3] as special cases of the main result.