Software reusability metrics prediction by using evolutionary algorithms: The interactive mobile learning application RozGaar

Padhy, Neelamadhab; Satapathy, Suresh Chandra; Mohanty, Jnyana Ranjan; Panigrahi, Rasmita

doi:10.3233/kes-180390

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…The process of developing software inevitably results in the introduction of bugs, which can only be discovered with the assistance of testing. A bug repository is an essential tool for monitoring and preventing programming errors since it stores information about problems and ensures their upkeep [5,6].…”

Section: Introductionmentioning

confidence: 99%

Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality

et al. 2023

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The amount of time and money required to finish a software project and distribute the final product increases when there are bugs in the programme. Software procedures like defect monitoring and repair may be both costly and time-consuming to complete. Because it is difficult to locate and correct every defect in a product, it is essential that the negative effect of those defects be minimised in order to provide a result that is of better overall quality. The process of identifying troublesome sections of software code is known as software defect prediction. This paper presents an optimized machine learningenabled model for software fault prediction to improve software quality. PC1 data set is fed as input data in this model. Important features are selected by ant colony optimization (ACO) technique. Selected features are fed as input to support vector machine. Training and testing of SVM is performed by PC1 data set. Performance of ACO SVM Ant Colony Optimization Support Vector Machine is compared with SVM, Naive Bayes classifier and K-Nearest Neighbour classifier. The performance of ACO-based SVM is better for software fault classification and prediction.

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Section: Introductionmentioning

confidence: 99%

Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality

et al. 2023

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“…Alternatively, machine learning models such as Logistic Regression (LR) and Random Forest (RF) were built [ 45 , 46 ] for the same purpose. The different methods were noted as applying evolutionary techniques [ 47 ], statistical feature selection [ 48 ], Principal Component Analysis [ 49 ], and T-test analysis-based feature selection [ 50 ].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review

Jorayeva

Akbulut

Çatal

et al. 2022

Sensors

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Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field.

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“…Recent studies in the field of Mobile Learning are increasingly exploring machine learning algorithms to predict failure in learning, focusing on both engagement and technology acceptance (Tan et al, 2014), (Padhy et al, 2018), and (Adnan et al, 2019). Diverse sources of data, such as MOODLE platforms and student data are used to implement these predictive models (Adnan, Habib, Ashraf, Shah, et al, 2020), (Moslehi & Haeri, 2020).…”

Section: Rq4: What Are the Machine Learning Algorithms That Show More...mentioning

confidence: 99%

Behavioral Involvement, Technology Acceptance, and Failure in Mobile Learning: A Systematic Review

Daoudi,

Alloug,

Oumaira

et al. 2024

JHETP

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As we move further into the digital age, machine learning algorithms have become increasingly popular in E-learning for their ability to predict learner failure and assess behavioral engagement, particularly in mobile learning environments. This paper reports on the systematic review conducted by the most relevant research in the literature that uses machine learning algorithms to predict failure, verify acceptance of mobile technology, and analyze behavioral engagement in mobile learning platforms. The search was performed using research papers extracted from four commonly used databases and published between 2010 and 2023; the last database access was on 15/05/2023. Guided by the PRISMA checklist, the review followed a structured approach to select, analyze, and report relevant studies. Studies were selected based on strict inclusion and exclusion criteria, focusing on peer-reviewed articles that empirically test the application of machine learning in mobile learning contexts. Of the initial 332 screened articles, 20 were eligible for inclusion. The results highlight the transformative role that machine learning is playing in revolutionizing online mobile learning experiences.

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Software reusability metrics prediction by using evolutionary algorithms: The interactive mobile learning application RozGaar

Cited by 9 publications

References 28 publications

Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality

Retracted: Scientific programming using optimized machine learning techniques for software fault prediction to improve software quality

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review

Behavioral Involvement, Technology Acceptance, and Failure in Mobile Learning: A Systematic Review

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