Iterative software fault prediction with a hybrid approach

“…Erturk and Ebru [23] solve SFP problem based on an iterative hybrid approach between Fuzzy Inference System (FIS) and Artificial Neural Network (ANN). The FIS is employed at the beginning of the project where no historical data available, while ANN is employed once some historical data available.…”

Section: A Software Fault Predictionmentioning

confidence: 99%

Enhanced Binary Moth Flame Optimization as a Feature Selection Algorithm to Predict Software Fault Prediction

et al. 2020

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Software fault prediction (SFP) is a complex problem that meets developers in the software development life cycle. Collecting data from real software projects, either while the development life cycle or after lunch the product, is not a simple task, and the collected data may suffer from imbalance data distribution problem. In this research, we proposed an Enhanced Binary Moth Flame Optimization (EBMFO) with Adaptive synthetic sampling (ADASYN) to predict software faults. BMFO is employed as a wrapper feature selection, while ADASYN enhances the input dataset and address the imbalanced dataset. Converting MFO algorithm from a continues version to the binary version using transfer functions (TFs) from two different groups (S-shape and V-shape) is investigated in this work and proposed an EBFMFO version. Fifteen real projects data obtained from PROMISE repository are employed in this work. Three different classifiers are used: the k-nearest neighbors (k-NN), Decision Trees (DT), and Linear discriminant analysis (LDA). The reported results demonstrate that the proposed EBMFO enhances the overall performance of classifiers and outperforms the results in the literature and show the importance of TF for feature selection algorithms.

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“…The SFP also reduce the costs, time and effort should be spent on software products [3]. Reference [38] reported that the most expensive software development activities are the cost finding and correcting faults.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

2020

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The discovery of software faults at early stages plays an important role in improving software quality; reduce the costs, time, and effort that should be spent on software development. Machine learning (ML) have been widely used in the software faults prediction (SFP), ML algorithms provide varying results in terms of predicting software fault. Deep learning achieves remarkable performance in various areas such as computer vision, natural language processing, speech recognition, and other fields. In this study, two deep learning algorithms are studied, Multi-layer perceptron's (MLPs) and Convolutional Neural Network (CNN) to address the factors that might have an influence on the performance of both algorithms. The experiment results show how modifying parameters is directly affecting the resulting improvement, these parameters are manipulated until the optimal number for each of them is reached. Moreover, the experiments show that the effect of modifying parameters had an important role in prediction performance, which reached a high rate in comparison with the traditional ML algorithm. To validate our assumptions, the experiments are conducted on four common NASA datasets. The result shows how the addressed factors might increase or decrease the fault detection rate measurement. The improvement rate was as follows up to 43.5% for PC1, 8% for KC1, 18% for KC2 and 76.5% for CM1.

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Iterative software fault prediction with a hybrid approach

Cited by 48 publications

References 33 publications

The Effect of Code Smells on the Relationship Between Design Patterns and Defects

The Effect of Code Smells on the Relationship Between Design Patterns and Defects

Enhanced Binary Moth Flame Optimization as a Feature Selection Algorithm to Predict Software Fault Prediction

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

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