Software defect prediction techniques using metrics based on neural network classifier

Jayanthi, R.; Florence, M. Lilly

doi:10.1007/s10586-018-1730-1

Cited by 96 publications

(57 citation statements)

References 25 publications

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“…During the optimization process, SUP and CONF of classification rules are utilized as indicated in Eqs. 11,12,13. It helps SHO to be used as a classifier by looking for the suitable classification rules between initially spotted hyenas (random rules).…”

Section: Flowchart and Phases Of The Sho As A Classifiermentioning

confidence: 99%

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Cross-projects software defect prediction using spotted hyena optimizer algorithm

2020

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Cross-projects software defect prediction improves the quality of new software projects or projects with a shortage of historical data. Therefore, various data mining techniques are recommended in this field. The classification accuracy issue is considered one of the most significant problems due to the shortage and heterogeneous in historical data. To address this challenge, this research utilizes a spotted hyena optimizer algorithm as a classifier to predict defects through cross-projects. Confidence and Support are utilized as a multi-objective fitness function to look for the best classification rules. These classification rules are used to predict defects for new projects or other projects with insufficient data. The datasets of NASA such as JM1, KC1, and KC2 are used. By applying spotted hyena optimizer algorithm as a classifier on one dataset and predicting defects in the other two datasets, accuracy is reported 84.6, 92.0, 82.4, 90.7, 86.6 and 81.8 for JM1, KC1, and KC2 respectively. These accuracy values are better than the most significant data mining techniques in the field such as Support Vector Machine, Naïve Bayes, Boosting, C4.5, and Bagging. Also, the proposed research discusses other performance measures such as precision, recall, and f-measure. The conclusion proves that there are many features of McCabe and Halstead that have a strong impact to generate highly accurate predictors for defects such as McCabe's line count of code, McCabe's cyclomatic complexity, McCabe's essential complexity, McCabe's design complexity iv, Halstead's effort, Halstead's time estimator, Halstead's line count, Halstead's count of line of comments and total operators.

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Section: Flowchart and Phases Of The Sho As A Classifiermentioning

confidence: 99%

“…Many techniques were used in SDP such as Support Vector Machine (SVM) [6], Naïve Bayes (NB) [7], Boosting [8], C4.5 [9] and Bagging [10]. SDP models still suffer from a very important and challenging issue, which is detecting accuracy [11,12].…”

Section: Introductionmentioning

confidence: 99%

Cross-projects software defect prediction using spotted hyena optimizer algorithm

2020

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“…It has been observed that accuracy is affecting if data is having high dimension. R. jayanthi et al applied PCA(principle component analysis) for dimension reduction and finally used k-NN, SVM, Naïve Bayes and LDA for SDP [18]. C. Manjula et al used modified GA for dimension reduction with the help of deep neural network for SDP.…”

Section: Literature Surveymentioning

confidence: 99%

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Kumar*,

Prasad

2020

IJITEE

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Software enhances the working capability of any business. Developing such a software entrusts the developing organization to build defect free software. In this context we have used PC1 dataset(NASA dataset) which has sufficient parameters for analysis. Intelligent techniques using different methodologies have been applied exhaustively on the PC1 data to find out the best intelligent technique for software defect. As the PC1 data is highly imbalanced data, there was biasness in the prediction of the intelligent techniques. Hence, to overcome this issue, in this paper we tried to propose best balancing method along with the intelligent technique to predict the software defect accurately.

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“…With the solution of the improved Bellman optimality equation by the value iteration method, they claim their optimization model was resolved, and the optimum overload threshold is adaptively selected. The authors in [44] [46], concentrated on software reliability modelling and software defect prediction using neural network classifier approaches. While the authors in [45] proposed a cost-effective and fault resilient reusability prediction mocek by using genetic algorithm, the authors in [47] proposed a deep neural network based hybrid approach for software defect prediction using software metrics.…”

Section: Related Workmentioning

confidence: 99%

Failure prediction using machine learning in a virtualised HPC system and application

et al. 2019

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Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular checkpointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the Support Vector Machine (SVM), Random Forest(RF), k-Nearest Neighbors (KNN), Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting fail

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Software defect prediction techniques using metrics based on neural network classifier

Cited by 96 publications

References 25 publications

Cross-projects software defect prediction using spotted hyena optimizer algorithm

Cross-projects software defect prediction using spotted hyena optimizer algorithm

Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Failure prediction using machine learning in a virtualised HPC system and application

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