An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes

Kumar, Lov; Misra, Sanjay; Rath, Santanu Kumar

doi:10.1016/j.csi.2017.02.003

Cited by 53 publications

(30 citation statements)

References 44 publications

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“…In the literature, various algorithms have been used by different studies when predicting bugs on a software project. For instance, neural network was used to develop a bug prediction model [4]. SVM and KNN algorithms are compared to find similarities of different files to predict defectiveness [8].…”

Section: Related Workmentioning

confidence: 99%

“…Since datasets in the repository are publicly available, studies can be easily repeated and verified. This repository was also used by several studies [4][5][6][7][8][9][10][11]35,36]. Datasets contain source code metrics that can be used to evaluate the quality of the software or utilized to predict bugs.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Although there are many classification studies [4][5][6][7][8][9][10][11] performed in software bug prediction, to the best of our knowledge, there has been no prior detailed investigation for ordinal classification in this problem. To fill this gap in the literature, the study presented in this article focuses on the application of ordinal classification algorithms on real-world software bug data.…”

Section: Introductionmentioning

confidence: 99%

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Yazılım Hata Tahmini için Sıralı Sınıflandırma Yaklaşımı

Öztürk

Birant

2019

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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Software bug prediction is the process of utilizing classification and/or regression algorithms to predict the presence of possible errors (or defects) in a source code. However, current classification studies in the literature assume that the target attribute values in the datasets are binary (i.e. buggy or non-buggy) or unordered, so they lose inherent order between the class values such as zero, less and more bug levels. To overcome this drawback, this study proposes a novel approach which suggests ordinal classification methods as a solution for software bug prediction problem. This article compares ordinal and nominal versions of various classification algorithms (random forest, support vector machine, Naive Bayes and k-nearest neighbor) in terms of classification performance on real-world 38 software engineering datasets. The results indicate that ordinal classification approach achieves better classification accuracy on average than the traditional (nominal) solutions.

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Section: Related Workmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Yazılım Hata Tahmini için Sıralı Sınıflandırma Yaklaşımı

Öztürk

Birant

2019

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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“…The types of classification methods used are Decision Tree [6] [7] [8], Neural Network [9] [10], Naïve Bayes [11] [12], and K-Nearest Neighbor [13]. Software metric is data that can be used to detect software that has software defects [14] based on coupling, inheritance, cohesion, complexity, dan size [15]. Datasets of NASA Metrics Data Program (NASA MDP) is one of the metric software that researchers use to predict software defects [16] [6].…”

Section: Research Backgroundmentioning

confidence: 99%

Software Defect Prediction Using AWEIG+ADACOST Bayesian Algorithm for Handling High Dimensional Data and Class Imbalance Problem

Suntoro

Christanto

Indriyawati

2018

IJITEB

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The most important part in software engineering is a software defect prediction. Software defect prediction is defined as a software prediction process from errors, failures, and system errors. Machine learning methods are used by researchers to predict software defects including estimation, association, classification, clustering, and datasets analysis. Datasets of NASA Metrics Data Program (NASA MDP) is one of the metric software that researchers use to predict software defects. NASA MDP datasets contain unbalanced classes and high dimensional data, so they will affect the classification evaluation results to be low. In this research, data with unbalanced classes will be solved by the AdaCost method and high dimensional data will be handled with the Average Weight Information Gain (AWEIG) method, while the classification method that will be used is the Naïve Bayes algorithm. The proposed method is named AWEIG + AdaCost Bayesian. In this experiment, the AWEIG + AdaCost Bayesian algorithm is compared to the Naïve Bayesian algorithm. The results showed the mean of Area Under the Curve (AUC) algorithm AWEIG + AdaCost Bayesian yields better than just a Naïve Bayes algorithm with respectively mean of AUC values are 0.752 and 0.696.

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“…Fault prediction technology is a critical step of condition-based maintenance. In recent years, fault prediction has become a hot topic in the field of system monitoring [11]. Artificial neural network (ANN) [12][13][14][15][16][17], autoregressive model (AR) [18,19], support vector machine (SVM) [20], vector autoregressive model (VAR), and so forth [21] are commonly used fault prediction methods.…”

Section: Introductionmentioning

confidence: 99%

DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

Zhou

Wang

Gao

2017

Journal of Control Science and Engineering

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Residual useful life (RUL) prediction is significant for condition-based maintenance. Traditional data-driven RUL prediction method can only predict fault trend of the system rather than RUL of a specific system component. Thus it cannot tell the operator which component should be maintained. The innovation of this paper is as follows: (1) Wavelet filtering based method is developed for early detection of slowly varying fault. (2) Designated component analysis is introduced as a feature extraction tool to define the fault precursor of a specific component. (3) Exponential life prediction model is established by nonlinear fitting of the historical RUL and the fault size characterized by the statistics used. Once online detection statistics is obtained, real-time RUL of the critical component can be predicted online. Simulation shows the effectiveness of this algorithm.

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An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes

Cited by 53 publications

References 44 publications

Yazılım Hata Tahmini için Sıralı Sınıflandırma Yaklaşımı

Yazılım Hata Tahmini için Sıralı Sınıflandırma Yaklaşımı

Software Defect Prediction Using AWEIG+ADACOST Bayesian Algorithm for Handling High Dimensional Data and Class Imbalance Problem

DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

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