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SOFTWARE DEFECT PREDICTION USING DEEP BELIEF NETWORK WITH L1-REGULARIZATION BASED OPTIMIZATION
2018
International Journal of Advanced Research in Computer Science
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[11] also presented neural network based approach for software defect prediction where two neural network models are used along with object-oriented metrics. ...
of general framework for software defect prediction. ...
doi:10.26483/ijarcs.v9i1.5476
fatcat:auhno5sjv5bw7efugntqmn2wqu
A Comprehensive Analysis of Ensemble-based Fault Prediction Models Using Product, Process, and Object-Oriented Metrics in Software Engineering
2024
Zenodo
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This study contributes a comprehensive taxonomy to the discourse, offering a holistic perspective on the multifaceted landscape of object-oriented metrics in fault prediction within the broader context ...
Simultaneously, anticipating fault proneness in software components is a pivotal focus in software testing. Software coupling and complexity metrics are critical for evaluating software quality. ...
Ongoing research in object-oriented software testing aims to explore the impact of OO metrics on software maintenance for a more accurate examination of the problem. ...
doi:10.5281/zenodo.10464708
fatcat:mgqv3sg7jrdu5ek6tgcagvo5em
Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults
2021
Scientific Programming
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The objective of this paper is to show the importance of hyperparameter tuning in developing efficient deep neural network models for predicting fault proneness of software modules and to compare the results ...
Deep neural network models built by the appropriate design decisions are crucial to obtain the desired classifier performance. ...
Figure 1 :Figure 2 : 12 A generalized software fault prediction process based on machine learning. Deep neural network. ...
doi:10.1155/2021/6662932
fatcat:c7unakk5qze5flqwhr4procobu
Predictive Models in Software Engineering: Challenges and Opportunities
[article]
2020
arXiv
pre-print
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Based on our findings, we also propose a set of current challenges that still need to be addressed in future work and provide a proposed research road map for these opportunities. ...
Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. ...
Deep learning is part of a broader family of machine learning methods based on artificial neural networks [121] . ...
arXiv:2008.03656v1
fatcat:fe7ylphujfbobeo3g5yevniiei
Effective Prediction of Software Defects using Random-tree Entropy based Feature Selection Framework
2022
International Journal of Advanced Computer Science and Applications
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Thus, in this study, a novel approach is proposed for predicting the number of software defects based on relevant variables using ML. ...
As a result, forecasting the frequency of software module failures is critical to a developer's efficiency. Many methods for defect detection and correcting problems exist. ...
For feature exploration and categorization, neural forest (NF) [18] combines deep neural network with decision forest. ...
doi:10.14569/ijacsa.2022.0130541
fatcat:5bjrn2m57rdx7am4dpffdbm7u4
Statistical Analysis for Revealing Defects in Software Projects: Systematic Literature Review
2021
International Journal of Advanced Computer Science and Applications
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Second, neural networks and regression analysis are among the most important smart and statistical methods used for this purpose. ...
Defect detection in software is the procedure to identify parts of software that may comprise defects. ...
S23 presented a framework to predict software defect type using concept-based classification. ...
doi:10.14569/ijacsa.2021.0121128
fatcat:4q3f7v2uwbgozmrzbwjnp4snja
On the Value of Oversampling for Deep Learning in Software Defect Prediction
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2008.03835v1 fatcat:qslswkxmrjaxtg27qkhvuldvnaOther Versions
These results present a cogent case for the use of oversampling prior to applying deep learning on software defect prediction datasets. ...
For the specific case of deep learning for defect prediction, we show that that truism is false. ...
ACKNOWLEDGEMENTS This work was partially funded by a research grant from the National Science Foundation (CCF #1703487). ...
arXiv:2008.03835v3
fatcat:prw35p6trbgefe34qg422k4z5a
Table of Contents
2021
2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
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, Ljupčo Todorovski and Sanda Martinčić-Ipšić, Extractive
Text Summarization Based on Selectivity Ranking
paper 91
Khadidja Chettah and Amer Draa, A Discrete Differential Evolution
Algorithm for Extractive ...
Valderrama Sakuyama and Chokri Souani, Full Python Interface Control: Auto Generation and Adaptation of Deep Neural Networks for Edge Computing and IoT Applications FPGA-Based Acceleration Hristina Topalova ...
doi:10.1109/inista52262.2021.9548429
fatcat:mrjsewx2orggblilwn7qozxn6q
Prediction of software defects by knowledge graph and genetic algorithm
2022
International Journal of Advanced Trends in Computer Science and Engineering
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This research aims to present a software defect prediction method based on knowledge graphs and automated machine learning. ...
Software defect detection is one of the biggest software development challenges and accounts for the largest budget in the software development process. ...
This paper proposes a software defect prediction model based on automated machine learning using knowledge diagrams. ...
doi:10.30534/ijatcse/2022/011142022
fatcat:eumww24gg5e6hgjg6hj7yes5v4
The Influence of Deep Learning Algorithms Factors in Software Fault Prediction
2020
IEEE Access
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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 ...
The improvement rate was as follows up to 43.5% for PC1, 8% for KC1, 18% for KC2 and 76.5% for CM1. INDEX TERMS Deep learning algorithms, software fault prediction, classification, hyper parameters. ...
They propose a new software fault-prone prediction model called SFPM based on the concept of the filter model and sequential search strategy. ...
doi:10.1109/access.2020.2985290
fatcat:guldb3626fedhm4tf3fmrbki6e
Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features
2024
Computers Materials & Continua
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Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. ...
Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. ...
Acknowledgement: The authors wish to express their appreciation to the reviewers for their helpful suggestions, which greatly improved the presentation of this paper. ...
doi:10.32604/cmc.2024.047172
fatcat:2dojdh7murgf3menvp2bc7xc4e
An Empirical Study on Predictability of Software Code Smell Using Deep Learning Models
2021
International Conference on Advanced Information Networking and Applications
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2021 pre-print arXiv:2108.04659v1 fatcat:xe4vy2sk4vbmpo4ejminrqa2sq
Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices. ...
A total of 576 distinct Deep Learning models were trained using the features and datasets mentioned above. ...
Based on the internal organization and anatomy of the software, a robust model can be created, which can make this excruciating process a lot simpler. ...
doi:10.1007/978-3-030-75075-6_10
dblp:conf/aina/GuptaKKNK21
fatcat:f4ptoofmfve3hnc6swmtqm5kuy
An in-Depth Analysis of the Software Features' Impact on the Performance of Deep Learning-Based Software Defect Predictors
2022
IEEE Access
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A broad evaluation performed on the Calcite software system highlights a statistically significant improvement obtained by applying deep learning-based classifiers for detecting software defects when using ...
The conceptual features are automatically engineered using Doc2Vec, an artificial neural network based prediction model. ...
ACKNOWLEDGMENT The authors would like to thank the editor and the anonymous reviewers for their useful suggestions and comments that helped to improve the article and the presentation. ...
doi:10.1109/access.2022.3181995
fatcat:hplspug54rbqflnqyfl4fbw3tu
Software Defect Prediction Based on Hybrid Swarm Intelligence and Deep Learning
2021
Computational Intelligence and Neuroscience
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In order to improve software quality and testing efficiency, this paper implements the prediction of software defects based on deep learning. ...
Through the analysis of the experimental results of six data sets, compared with the traditional hyperparameter optimization method and a single swarm intelligence algorithm, the model using the hybrid ...
Moreover, when comparing the hyperparameter
Conclusions Based on the deep neural network for defect prediction, the article proposes a hybrid Wolf Pack algorithm to optimize the hyperparameters of the ...
doi:10.1155/2021/4997459
pmid:34992647
pmcid:PMC8727112
fatcat:qlv6r22r7jgkng2dutwfsxhn54
Deep Learning Approach for Software Maintainability Metrics Prediction
2019
IEEE Access
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In this paper, we perform deep learning for software maintainability metrics' prediction on a large number of datasets. ...
In the past, several measures have been taken into account for predicting metrics that influence software maintainability. However, deep learning is yet to be explored for the same. ...
In this paper, we propose a new Deep Learning based method for Software Maintainability Metrics Prediction and apply this method on a large number of datasets. ...
doi:10.1109/access.2019.2913349
fatcat:hmk7y5tpyngffkqs4zoe75pmmq
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