scholarly journals Inheritance metrics feats in unsupervised learning to classify unlabeled datasets and clusters in fault prediction

2021 ◽  
Vol 7 ◽  
pp. e722
Author(s):  
Syed Rashid Aziz ◽  
Tamim Ahmed Khan ◽  
Aamer Nadeem
Keyword(s):  
Object Oriented ◽  
Fault Prediction ◽  
Training Data ◽  
Data Sets ◽  
Correct Classification ◽  
High Quality ◽  
Software Fault Prediction ◽  
Public Data ◽  
Software Fault

Fault prediction is a necessity to deliver high-quality software. The absence of training data and mechanism to labeling a cluster faulty or fault-free is a topic of concern in software fault prediction (SFP). Inheritance is an important feature of object-oriented development, and its metrics measure the complexity, depth, and breadth of software. In this paper, we aim to experimentally validate how much inheritance metrics are helpful to classify unlabeled data sets besides conceiving a novel mechanism to label a cluster as faulty or fault-free. We have collected ten public data sets that have inheritance and C&K metrics. Then, these base datasets are further split into two datasets labeled as C&K with inheritance and the C&K dataset for evaluation. K-means clustering is applied, Euclidean formula to compute distances and then label clusters through the average mechanism. Finally, TPR, Recall, Precision, F1 measures, and ROC are computed to measure performance which showed an adequate impact of inheritance metrics in SFP specifically classifying unlabeled datasets and correct classification of instances. The experiment also reveals that the average mechanism is suitable to label clusters in SFP. The quality assurance practitioners can benefit from the utilization of metrics associated with inheritance for labeling datasets and clusters.

scholarly journals Exclusive use and evaluation of inheritance metrics viability in software fault prediction—an experimental study

2021 ◽  
Vol 7 ◽  
pp. e563
Author(s):  
Syed Rashid Aziz ◽  
Tamim Ahmed Khan ◽  
Aamer Nadeem
Keyword(s):  
Software Metrics ◽  
Model Building ◽  
Object Oriented ◽  
Fault Prediction ◽  
Cross Entropy ◽  
Support Vector ◽  
Data Sets ◽  
Software Fault Prediction ◽  
Software Fault ◽  
Class Labels

Software Fault Prediction (SFP) assists in the identification of faulty classes, and software metrics provide us with a mechanism for this purpose. Besides others, metrics addressing inheritance in Object-Oriented (OO) are important as these measure depth, hierarchy, width, and overriding complexity of the software. In this paper, we evaluated the exclusive use, and viability of inheritance metrics in SFP through experiments. We perform a survey of inheritance metrics whose data sets are publicly available, and collected about 40 data sets having inheritance metrics. We cleaned, and filtered them, and captured nine inheritance metrics. After preprocessing, we divided selected data sets into all possible combinations of inheritance metrics, and then we merged similar metrics. We then formed 67 data sets containing only inheritance metrics that have nominal binary class labels. We performed a model building, and validation for Support Vector Machine(SVM). Results of Cross-Entropy, Accuracy, F-Measure, and AUC advocate viability of inheritance metrics in software fault prediction. Furthermore, ic, noc, and dit metrics are helpful in reduction of error entropy rate over the rest of the 67 feature sets.

Object-oriented software fault prediction using neural networks

2007 ◽  
Vol 49 (5) ◽  
pp. 483-492 ◽  
Author(s):  
S. Kanmani ◽  
V. Rhymend Uthariaraj ◽  
V. Sankaranarayanan ◽  
P. Thambidurai
Keyword(s):  
Neural Networks ◽  
Object Oriented ◽  
Fault Prediction ◽  
Software Fault Prediction ◽  
Software Fault

scholarly journals Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Yeresime Suresh ◽  
Lov Kumar ◽  
Santanu Ku. Rath
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Linear Regression ◽  
Object Oriented ◽  
Fault Prediction ◽  
Learning Methods ◽  
Software Fault Prediction ◽  
Machine Learning Methods ◽  
Software Fault

Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.

Genetic Programming for Cross-Release Fault Count Predictions in Large and Complex Software Projects

2010 ◽  
pp. 94-126 ◽  
Author(s):  
Wasif Afzal ◽  
Richard Torkar ◽  
Robert Feldt ◽  
Tony Gorschek
Keyword(s):  
Genetic Programming ◽  
Goodness Of Fit ◽  
Fault Prediction ◽  
Data Sets ◽  
Software Projects ◽  
Validity Of Results ◽  
Software Fault Prediction ◽  
Efficient Resource ◽  
Software Fault ◽  
Industrial Software

Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modeling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.

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