A Comparative Study of Unsupervised Learning Algorithms for Software Fault Prediction

Jothi, R.

doi:10.1109/iccons.2018.8663154

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…Therefore, it was concluded that the data pre-processing is good enough. MLP performed better, contrary to the conclusions reached in [16][17][18][19][20][21][22][23][24][25][26][27][28][29]. The reason is the type of data used (numeric data) and the non-linearity of the problem.…”

Section: B Experimental Resultscontrasting

confidence: 74%

“…In addition to supervised ML techniques, unsupervised ML algorithms have been also used. The author in [23] conducted a comparative study on clustering algorithms, specifically Kmeans and their variants (K-means++, QDK, and Fuzzy Cmeans (FCM)). NASA datasets with 29 static code attributes were used, with the QDK algorithm exhibiting the best performance.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the acquired results were compared with those of well-known ML models to showcase the effectiveness of DL. As far as is known, only the studies [22][23][24][25][26][27][28][29][30][31][32][33][34] have considered DL algorithms to predict the number of faults at the class level.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Number of Software Faults using Deep Learning

Alkaberi,

Assiri

2024

Eng. Technol. Appl. Sci. Res.

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The software testing phase requires considerable time, effort, and cost, particularly when there are many faults. Thus, developers focus on the evolution of Software Fault Prediction (SFP) to predict faulty units in advance, therefore, improving software quality significantly. Forecasting the number of faults in software units can efficiently direct software testing efforts. Previous studies have employed several machine learning models to determine whether a software unit is faulty. In this study, a new, simple deep neural network approach that can adapt to the type of input data was designed, utilizing Convolutional Neural Networks (CNNs) and Multi-Layer Perceptron (MLP), to predict the number of software faults. Twelve open-source software project datasets from the PROMISE repository were used for testing and validation. As data imbalance can negatively impact prediction accuracy, the new version of synthetic minority over-sampling technique (SMOTEND) was used to resolve data imbalance. In experimental results, a lower error rate was obtained for MLP, compared to CNN, reaching 0.195, indicating the accuracy of this prediction model. The proposed approach proved to be effective when compared with two of the best machine learning models in the field of prediction. The code will be available on GitHub.

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Section: B Experimental Resultscontrasting

confidence: 74%

Section: Related Workmentioning

confidence: 99%

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Predicting the Number of Software Faults using Deep Learning

Alkaberi,

Assiri

2024

Eng. Technol. Appl. Sci. Res.

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“…Five PROMISE datasets are compared. Authors report that K-means performance is reliable across all k-variations [28]. A study reporting a mix of both quantitative and qualitative analysis to investigate defect prediction is presented in [52].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Software fault prediction using deep learning techniques

Batool

Khan

2023

Software Qual J

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Software fault prediction (SFP) techniques are used to identify faults at the early stages of the software development life cycle (SDLC). We find machine learning techniques as commonly used techniques for SFP as compared to deep learning methods which can produce more accurate results. Deep learning offers exceptional results in a variety of domains such as computer vision, natural language processing, speech recognition, etc. In this study, we use three deep learning methods, namely, Long Short Term Memory (LSTM), Bidirectional LSTM (BILSTM), and Radial Basis Function Network (RBFN) to predict software faults and compare our results with existing models to show how our results are more accurate. In our study, we use Chidamber and Kemerer (CK) metrics-based datasets to conduct experiments and test our proposed algorithm. We conclude that LSTM and BILSTM perform better whereas RBFN is faster in producing the required results. We use k-fold cross validation to do the model evaluation. Our proposed models provide a more accurate and efficient SFP mechanism to software developers.

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“…That is, the evaluation is done in the inner circle. Outside the loop, the initial population (initial costs) is merged with the new population (new costs) and the best ones are selected and the process continues [20].…”

Section: Clone Algorithm Of Emperor Penguinsmentioning

confidence: 99%

Investigating and comparing the performance of meta-heuristic algorithms in feature selection and software fault prediction

Arshaghi

2022

Preprint

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Meta‑Heuristic algorithms are optimization techniques that provide the optimal solution through processes of repeated exploration and exploitation of the entire search space. Feature selection is also an important and prominent process in the field of machine learning that reduces data dimensions. This paper examines and compares nature-inspired meta-heuristic algorithms for feature selection to increase the accuracy of software fault prediction. Researchers cannot easily select meta-heuristic algorithms as a suitable method for their research due to their great variety and multiplicity. In this paper, by describing the feature selection techniques and its methods, the application of meta-heuristic algorithms in different fields, such as swarm intelligence and binary methods of these algorithms has been investigated. Also, by introducing 18 meta-heuristic algorithms in 6 different categories and evaluating each of them, a suitable analysis has been provided to researchers so that they can easily and with the highest efficiency choose the appropriate algorithm and method of their work. In the papers presented so far, meta-heuristic algorithms have been studied from only one aspect, while in this article, while studying different types of research, they have tried to study and evaluate them from different aspects. The effectiveness of the combination of three meta-heuristic algorithms, developed butterfly flame, bee colony and developed wall, was tested on 20 data sets. the proposed method in 17 datasets was able to improve the result of 7 datasets.

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A Comparative Study of Unsupervised Learning Algorithms for Software Fault Prediction

Cited by 12 publications

References 17 publications

Predicting the Number of Software Faults using Deep Learning

Predicting the Number of Software Faults using Deep Learning

Software fault prediction using deep learning techniques

Investigating and comparing the performance of meta-heuristic algorithms in feature selection and software fault prediction

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