Software Defects Prediction using Machine Learning Algorithms

Assim, Marwa; Obeidat, Qasem; Hammad, Mustafa

doi:10.1109/icdabi51230.2020.9325677

Cited by 16 publications

(4 citation statements)

References 19 publications

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“…Results confirmed the practicality of combining multiple ML algorithms for software fault prediction. The SMOreg classifier did better than the ANN classifier in terms of performance [21].…”

Section: Related Workmentioning

confidence: 88%

A New Improved Prediction of Software Defects Using Machine Learning-based Boosting Techniques with NASA Dataset

Goyal,

Sinha

2023

IJRITCC

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Predicting when and where bugs will appear in software may assist improve quality and save on software testing expenses. Predicting bugs in individual modules of software by utilizing machine learning methods. There are, however, two major problems with the software defect prediction dataset: Social stratification (there are many fewer faulty modules than non-defective ones), and noisy characteristics (a result of irrelevant features) that make accurate predictions difficult. The performance of the machine learning model will suffer greatly if these two issues arise. Overfitting will occur, and biassed classification findings will be the end consequence. In this research, we suggest using machine learning approaches to enhance the usefulness of the CatBoost and Gradient Boost classifiers while predicting software flaws. Both the Random Over Sampler and Mutual info classification methods address the class imbalance and feature selection issues inherent in software fault prediction. Eleven datasets from NASA's data repository, "Promise," were utilised in this study. Using 10-fold cross-validation, we classified these 11 datasets and found that our suggested technique outperformed the baseline by a significant margin. The proposed methods have been evaluated based on their abilities to anticipate software defects using the most important indices available: Accuracy, Precision, Recall, F1 score, ROC values, RMSE, MSE, and MAE parameters. For all 11 datasets evaluated, the suggested methods outperform baseline classifiers by a significant margin. We tested our model to other methods of flaw identification and found that it outperformed them all. The computational detection rate of the suggested model is higher than that of conventional models, as shown by the experiments..

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Section: Related Workmentioning

confidence: 88%

A New Improved Prediction of Software Defects Using Machine Learning-based Boosting Techniques with NASA Dataset

Goyal,

Sinha

2023

IJRITCC

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“…Thus, the ML models seek to infer the best mathematical equation to fit the training data acting as regressor models. Motivated by their good performance in related studies in the literature (Romero, 2019; Hooda et al , 2021), the following models were selected: ZeroR (Sangeorzan, 2020) is a rule-based model that operates with the mean of the data. Linear Regression (LR) (Pal and Bharati, 2019) uses the Akaike criterion for model selection and is capable of handling weighted instances. SMOreg (Assim et al , 2020) implements a specific SVM for regression problems. Decision Stump (DS) (Uskov et al , 2021) belongs to the group of tree algorithms and is one of the most basic. Random Forest (RF) (Schonlau and Zou, 2020) belongs to the group of tree algorithms, but follows a more complex approach than DS. Particularly, it is composed of a set of n trees that finally ends up assembled into one to make a prediction. …”

Section: Methodsmentioning

confidence: 99%

Toward data-driven research: preliminary study to predict surface roughness in material extrusion using previously published data with machine learning

García-Martínez,

Carou,

de Arriba-Pérez

et al. 2023

RPJ

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Purpose Material extrusion is one of the most commonly used approaches within the additive manufacturing processes available. Despite its popularity and related technical advancements, process reliability and quality assurance remain only partially solved. In particular, the surface roughness caused by this process is a key concern. To solve this constraint, experimental plans have been exploited to optimize surface roughness in recent years. However, the latter empirical trial and error process is extremely time- and resource consuming. Thus, this study aims to avoid using large experimental programs to optimize surface roughness in material extrusion. Design/methodology/approach This research provides an in-depth analysis of the effect of several printing parameters: layer height, printing temperature, printing speed and wall thickness. The proposed data-driven predictive modeling approach takes advantage of Machine Learning (ML) models to automatically predict surface roughness based on the data gathered from the literature and the experimental data generated for testing. Findings Using ten-fold cross-validation of data gathered from the literature, the proposed ML solution attains a 0.93 correlation with a mean absolute percentage error of 13%. When testing with our own data, the correlation diminishes to 0.79 and the mean absolute percentage error reduces to 8%. Thus, the solution for predicting surface roughness in extrusion-based printing offers competitive results regarding the variability of the analyzed factors. Research limitations/implications There are limitations in obtaining large volumes of reliable data, and the variability of the material extrusion process is relatively high. Originality/value Although ML is not a novel methodology in additive manufacturing, the use of published data from multiple sources has barely been exploited to train predictive models. As available manufacturing data continue to increase on a daily basis, the ability to learn from these large volumes of data is critical in future manufacturing and science. Specifically, the power of ML helps model surface roughness with limited experimental tests.

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“…In [23], the authors developed ML models for defect prediction in the domain of software reliability and performance. The models were built using ANN, random forest (RF), random tree (RT), decision table (DT), linear regression (LR), Gaussian processes (GP), SMOreg, and M5P.…”

Section: B Software Risk Prediction Modelsmentioning

confidence: 99%

Risk Prediction Applied to Global Software Development using Machine Learning Methods

Hassan¹,

Abdel-Fattah²,

Ghoneim³

2022

IJACSA

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Software companies aim to develop high-quality software projects with the best global resources at the best cost. To achieve this global software development (GSD), an approach should be used which adopts work on projects across multiple distributed locations, and this is also known as distributed development. When companies attempt to implement GSD, they face numerous challenges owing to the nature of GSD and its differences from traditional methods. The objectives of this study were to identify the top software development factors that affect the overall success or failure of a software project using exploratory data analysis to find relationships between these factors, and to develop and compare risk prediction models that use machine learning classification techniques such as logistic regression, decision tree, random forest, support vector machine, K-nearest neighbors, and Naive Bayes. The findings of this study are as follows: in GSD, the top 18 factors influencing the software project are listed; and experiments show that the logistic regression and random forest models provide the best results, with an accuracy of 89% and 85%, respectively, and an area under the curve of 73% and 71%, respectively.

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Software Defects Prediction using Machine Learning Algorithms

Cited by 16 publications

References 19 publications

A New Improved Prediction of Software Defects Using Machine Learning-based Boosting Techniques with NASA Dataset

A New Improved Prediction of Software Defects Using Machine Learning-based Boosting Techniques with NASA Dataset

Toward data-driven research: preliminary study to predict surface roughness in material extrusion using previously published data with machine learning

Risk Prediction Applied to Global Software Development using Machine Learning Methods

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