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Software fault prediction based on the dynamic selection of learning technique: findings from the eclipse project study

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Abstract

An effective software fault prediction (SFP) model could help developers in the quick and prompt detection of faults and thus help enhance the overall reliability and quality of the software project. Variations in the prediction performance of learning techniques for different software systems make it difficult to select a suitable learning technique for fault prediction modeling. The evaluation of previously presented SFP approaches has shown that single machine learning-based models failed to provide the best accuracy in any context, highlighting the need to use multiple techniques to build the SFP model. To solve this problem, we present and discuss a software fault prediction approach based on selecting the most appropriate learning techniques from a set of competitive and accurate learning techniques for building a fault prediction model. In work, we apply the discussed SFP approach for the five Eclipse project datasets and nine Object-oriented (OO) project datasets and report the findings of the experimental study. We have used different performance measures, i.e., AUC, accuracy, sensitivity, and specificity, to assess the discussed approach’s performance. Further, we have performed a cost-benefit analysis to evaluate the economic viability of the approach. Results showed that the presented approach predicted the software’s faults effectively for the used accuracy, AUC, sensitivity, and specificity measures with the highest achieved values of 0.816, 0.835, 0.98, and 0.903 for AUC, accuracy, sensitivity, and specificity, respectively. The cost-benefit analysis of the approach showed that it could help reduce the overall software testing cost.

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Author information

Authors and Affiliations

  1. Department of Information Technology, ABV-Indian Institute of Information Technology and Management, Gwalior, India

    Santosh S. Rathore

  2. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Sandeep Kumar

Authors
  1. Santosh S. Rathore
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  2. Sandeep Kumar
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Corresponding author

Correspondence to Sandeep Kumar.

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Appendix

Appendix

In this study, we have used python programming libraries, Sklearn and imbalance to implement different used base learners and the presented approach. Following parameter values have been set for these learning techniques and presented approach.

Techniques

Parameters values

Naïve Bayes

Priors= None, var_smoothing= 1e-9, epsilon= absolute additive value to variances, sigma= variance of each feature per class, theta= mean of each feature per class.

Logistic Regression

penalty= l2, dual= False, tol= 0.0001, C = 1.0, fit_intercept= True, intercept_scaling= 1, class_weight= None, random_state= None, solver= ‘lbfgs’, max_iter= 500, multi_class= ‘auto’, verbose= 0, warm_start= False, n_jobs= None, l1_ratio= None

K-nearest Neighbor

n_neighbors= 5, weights= ‘uniform’, algorithm= ‘auto’, leaf_size= 30, power parameter= 2, metric=‘Euclidean’, metric_params= None, n_jobs= None

Decision Tree

criterion=‘gini’, splitter= ‘best’, max_depth= None, min_samples_split= 2, min_samples_leaf= 1, min_weight_fraction_leaf= 0.0, max_features= None, random_state= None, max_leaf_nodes= None, min_impurity_decrease= 0.0, min_impurity_split= None, class_weight= None, ccp_alpha= 0.0

Support Vector Machine

C = 1.0, kernel= ‘rbf’, degree= 3, gamma= ‘scale’, coef0 = 0.0, shrinking= True, probability= False, tol= 0.001, cache_size= 200, class_weight= None, verbose= False, max_iter= -1, decision_function_shape= ‘ovr’, break_ties= False, random_state= None

Techniques

Parameters values

Multilayer Perceptron

hidden_layer_sizes= 100, activation= ‘relu’, solver= ‘adam’, alpha= 0.0001, batch_size= ‘auto’, learning_rate= ‘constant’, learning_rate_init= 0.001, power_t = 0.5, max_iter= 200, shuffle= True, random_state= None, tol= 0.0001, verbose= False, warm_start= False, momentum= 0.9, nesterovs_momentum= True, early_stopping= False, validation_fraction= 0.1, beta_1 = 0.9, beta_2 = 0.999, epsilon= 1e-08, n_iter_no_change= 10, max_fun= 15000

SMOTE

sampling_strategy= ‘auto’, random_state= None, k_neighbors= 5, n_jobs= None

X-mean clustering (Weka)

binValue= 1.0, cutOffFactor= 0.5, debugLevel= 0, distanceF= Euclidian Distance-R first-last, maxIterations= 100, maxKMeans= 1000, maxKMeansForChildren= 1000, maxNumClusters= 10, minNumClusters= 2, seed= 10, and useKDTree= false

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Rathore, S.S., Kumar, S. Software fault prediction based on the dynamic selection of learning technique: findings from the eclipse project study. Appl Intell 51, 8945–8960 (2021). https://doi.org/10.1007/s10489-021-02346-x

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