Narayanan Manikandan

Bio: Narayanan Manikandan is an academic researcher from VIT University. The author has contributed to research in topics: The Internet & Parallel algorithm. The author has an hindex of 3, co-authored 5 publications receiving 17 citations.

Defect association and complexity prediction by mining association and clustering rules

Abstract: Number of defects remaining in a system provides an insight into the quality of the system. Software defect prediction focuses on classifying the modules of a system into fault prone and non-fault prone modules. This paper focuses on predicting the fault prone modules as well as identifying the types of defects that occur in the fault prone modules. Software defect prediction is combined with association rule mining to determine the associations that occur among the detected defects and the effort required for isolating and correcting these defects. Clustering rules are used to classify the defects into groups indicating their complexity: SIMPLE, MODERATE and COMPLEX. Moreover the defects are used to predict the effect on the project schedules and the nature of risk concerning the completion of such projects.

Parallel AES algorithm for performance improvement in data analytics security for IoT

Abstract: In emerging computing environment like internet of things (IoT) or smart device networking, many constraint-based devices are connected with internet. The device automatically interacts with each other through the connected network and gives us new experience. In order to effectively utilise the features of IoT, it is absolutely essential to ensure the security of connected end nodes. If one of the node security is compromised, the entire process will suffer seriously. However, implementing sufficient cryptographic functions on the device is very difficult due to the limitation of resources. This paper proposes a method of injecting the high performance security algorithm in data analytics done with IOT-based devices. Parallel algorithms will improve the efficiency of security mechanism in data analysis with parallel computing devices. AES algorithm is a symmetric encryption algorithm works efficiently for hardware and software. Through parallel processing of AES algorithm, data analytics in IoT-based systems performance can be improved. This method is tested with varieties of Intel-based multi-core processing architecture and considerable performance improvement is achieved.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

Abstract: Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

Metaheuristic Optimization Based Feature Selection for Software Defect Prediction

Abstract: Software defect prediction has been an important research topic in the software engineering field, especially to solve the inefficiency and ineffectiveness of existing industrial approach of software testing and reviews. The software defect prediction performance decreases significantly because the data set contains noisy attributes and class imbalance. Feature selection is generally used in machine learning when the learning task involves high- dimensional and noisy attribute datasets. Most of the feature selection algorithms, use local search throughout the entire process, consequently near-optimal to optimal solutions are quiet difficult to be achieved. Metaheuristic optimization can find a solution in the full search space and use a global search ability, significantly increasing the ability of finding high-quality solutions within a reasonable period of time. In this research, we propose the combination of metaheuristic optimization methods and bagging technique for improving the performance of the software defect prediction. Metaherustic optimization methods (genetic algorithm and particle swarm optimization) are applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. Results have indicated that the proposed methods makes an impressive improvement in prediction performance for most classifiers. Based on the comparison result, we conclude that there is no significant difference between particle swarm optimization and genetic algorithm when used as feature selection for most classifiers in software defect prediction.

Improving Recall of software defect prediction models using association mining

Abstract: Use of software product metrics in defect prediction studies highlights the utility of these metrics. Public availability of software defect data based on the product metrics has resulted in the development of defect prediction models. These models experience a limitation in learning Defect-prone (D) modules because the available datasets are imbalanced. Most of the datasets are dominated by Not Defect-prone (ND) modules as compared to D modules. This affects the ability of classification models to learn the D modules more accurately. This paper presents an association mining based approach that allows the defect prediction models to learn D modules in imbalanced datasets. The proposed algorithm preprocesses data by setting specific metric values as missing and improves the prediction of D modules. The proposed algorithm has been evaluated using 5 public datasets. A Naive Bayes (NB) classifier has been developed before and after the proposed preprocessing. It has been shown that Recall of the classifier after the proposed preprocessing has improved. Stability of the approach has been tested by experimenting the algorithm with different number of bins. The results show that the algorithm has resulted in up to 40% performance gain.

Applying Swarm Ensemble Clustering Technique for Fault Prediction Using Software Metrics

Abstract: Number of defects remaining in a system provides an insight into the quality of the system. Defect detection systems predict defects by using software metrics and data mining techniques. Clustering analysis is adopted to build the software defect prediction models. Cluster ensembles have emerged as a prominent method for improving robustness, stability and accuracy of clustering solutions. The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. In this paper, the clustering ensemble using Particle Swarm Optimization algorithm (PSO) solution is proposed to improve the prediction quality. An empirical study shows that the PSO can be a good choice to build defect prediction software models.