An AIS based feature selection method for software fault prediction

Soleimani, Ali; Asdaghi, Faeze

doi:10.1109/iraniancis.2014.6802598

Cited by 6 publications

(2 citation statements)

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“…The authors made use of filters ranker an empirical study to eliminate irrelevant features and they show that only a few software metrics are enough to build an effective defect predictor. In another feature selection study for software defect prediction, authors use an artificial immune system search for building a wrapper model to evaluate a software fault predictor [20]. Jacob et al propose a hybrid selection method combining information gain ratio and correlation based feature selection applied on NASA datasets [21].…”

Section: Related Workmentioning

confidence: 99%

Majority vote feature selection algorithm in software fault prediction

Borandağ¹,

Özçift²,

Kılınç³

et al. 2019

COMSIS J

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Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. The core idea of the method is to identify the most influential software metrics with the collaboration of various feature rankers. To test the efficiency of the proposed method, we used CM1, JM1, KC1, PC1, Eclipse Equinox, Eclipse JDT datasets and J48, NB, K-NN (IBk) ML algorithms. The experiments show that the proposed method is able to find out the most significant software metrics that enhances defect prediction performance.

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Section: Related Workmentioning

confidence: 99%

Majority vote feature selection algorithm in software fault prediction

Borandağ¹,

Özçift²,

Kılınç³

et al. 2019

COMSIS J

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show abstract

“…Software defect prediction technology is to design software metrics related to software defects by analyzing software code, software development process, etc., and then establish the relationship between software metrics and software defects by using historical defect data. Many technologies based on machine learning have been used to predict software defects, including artificial neural network [7], bayesian network [8], SVM [9], dictionary learning [10], association rule [11], naive bayes [12], tree-based methods [13], evolutionary algorithm [14], etc. However, these algorithms ignore the high dimension and class distribution imbalance of the defect data set, which have a great impact on classification performance [15].…”

Section: Related Workmentioning

confidence: 99%

Ensemble MultiBoost Based on RIPPER Classifier for Prediction of Imbalanced Software Defect Data

Zhang

Wang

et al. 2019

IEEE Access

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Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR_SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm.

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