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

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.

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

PeerJ Computer Science ◽

10.7717/peerj-cs.722 ◽

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.

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Software Fault Prediction in Object Oriented Software Systems Using Ensemble Classifiers

Celal Bayar Üniversitesi Fen Bilimleri Dergisi ◽

10.18466/cbayarfbe.424521 ◽

2018 ◽

pp. 297-302

Author(s):

Emin Borandağ ◽

Fatih Yücalar ◽

Kamil Akarsu

Keyword(s):

Object Oriented ◽

Fault Prediction ◽

Software Systems ◽

Ensemble Classifiers ◽

Software Fault Prediction ◽

Software Fault

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Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models

Machine Learning ◽

10.4018/978-1-60960-818-7.ch302 ◽

2012 ◽

pp. 371-387 ◽

Cited By ~ 2

Author(s):

Cagatay Catal ◽

Soumya Banerjee

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Software Metrics ◽

Prediction Models ◽

Artificial Immune Systems ◽

Fault Prediction ◽

Artificial Immune ◽

Software Fault Prediction ◽

Immune Systems ◽

Software Fault

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

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