文件列表:

.idea\misc.xml (294, 2019-05-12)
.idea\modules.xml (383, 2019-05-12)
.idea\Software-Reliability-Prediction-Using-Ensemble-Learning-master.iml (464, 2019-05-12)
.idea\workspace.xml (11333, 2019-05-12)
Source Code\Adaboost_Classification.py (3072, 2018-11-01)
Source Code\Bagging_Classification.py (8493, 2018-11-01)
Source Code\Bagging_Prediction.py (4286, 2018-11-01)
Source Code\Dataset\srp-class-csv.csv (153550, 2018-11-01)
Source Code\Dataset\srp-pred-csv.csv (5480, 2018-11-01)
Source Code\Stacking_Classification.py (6212, 2018-11-01)
Source Code\Stacking_Prediction.py (4247, 2018-11-01)
.idea\inspectionProfiles (0, 2019-05-12)
Source Code\Dataset (0, 2020-03-23)
.idea (0, 2020-03-23)
Source Code (0, 2020-03-23)

# Software-Reliability-Prediction-Using-Ensemble-Learning Software reliability is an indispensable part of software quality and is one among the most inevitable aspect for evaluating quality of software product. Software Industry endures various challenges in developing highly reliable software. We use the ensemble methods and machine learning techniques for software reliability predictions and evaluate them based on selected performance criteria. Ensemble learning, a machine learning paradigm where multiple base learners are trained to solve the problem. Here a set of hypotheses are constructed and combined to give improved results. Software reliability modelling based on test data is done to estimate whether the current reliability level meets the requirements for the product software reliability modelling also provides possibility to predict the reliability of the modules in a software. In this proposed work we have used ensemble methods on various machine learning approaches and study the ensemble performance using bagging, boosting and stacking. Using ensembling, a module in a software is classified whether to have faults or not based on certain attributes of the software module and predictions of the next failure in the software given the Mean Time Between Failure.

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