Software Defect Prediction Tool based on Neural Network

Singh, Mukhtar; Salaria, Dalwinder Singh

doi:10.5120/12200-8368

Cited by 19 publications

(11 citation statements)

References 17 publications

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“…In our proposed methodology, we used Ant 1.7 dataset 14 and this dataset consists of defected data which are coming from the PROMISE (Predict or Models in Software Engineering) repository of empir ical software engineering data 14 . In this promise data repository, defected data is freely available and this type of dataset is called public dataset.…”

Section: Proposed Methodologymentioning

confidence: 99%

Design of Software Fault Prediction Model Using BR Technique

Mahajan¹,

Gupta²,

Bedi³

2015

Procedia Computer Science

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During the previous years, the demand for producing the quality of software has been quickly increased. In this paper, Bayesian Regularization (BR) technique has been used for finding the software faults before the testing process. This technique helps us to reduce the cost of software testing which reduces the cost of the software project. The basic purpose of BR technique is to minimizes a combination of squared errors and weights, and then determine the correct combination so as to produce an efficient network.BR Technique algorithm based neural network tool is used for finding the results on the given public dataset. The accuracy of BR algorithm based neural network has been compared with Levenberg-Marquardt(LM) algorithm and Back Propagation (BPA) algorithm for finding the software defects. Our results signify that the software fault prediction model using BR technique provide better accuracy than Levenberg-Marquardt (LM) algorithm and Back Propagation (BPA) algorithm.

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Section: Proposed Methodologymentioning

confidence: 99%

Design of Software Fault Prediction Model Using BR Technique

Mahajan¹,

Gupta²,

Bedi³

2015

Procedia Computer Science

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“…Results showed that SVM performed better. In [9], the researchers proposed a GUI tool in MATLAB which used CK (Chidamber and Kemerer) object-oriented metrics for software defect prediction. For experiment, NASA datasets from PROMISE repository were used and performance of Levenberg-Marquardt (LM) algorithm is compared with Polynomial Function-based Neural Network on software defect prediction.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Iqbal¹,

Aftab²,

Ali³

et al. 2019

IJACSA

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Defect prediction at early stages of software development life cycle is a crucial activity of quality assurance process and has been broadly studied in the last two decades. The early prediction of defective modules in developing software can help the development team to utilize the available resources efficiently and effectively to deliver high quality software product in limited time. Until now, many researchers have developed defect prediction models by using machine learning and statistical techniques. Machine learning approach is an effective way to identify the defective modules, which works by extracting the hidden patterns among software attributes. In this study, several machine learning classification techniques are used to predict the software defects in twelve widely used NASA datasets. The classification techniques include: Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF). Performance of used classification techniques is evaluated by using various measures such as: Precision, Recall, F-Measure, Accuracy, MCC, and ROC Area. The detailed results in this research can be used as a baseline for other researches so that any claim regarding the improvement in prediction through any new technique, model or framework can be compared and verified.

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“…LOC, LCOM and WMC are the best indicators for the system reliability. Singh and Salarai [29] in their study has collected data from the PROMISE repository of empirical software engineering data. This dataset uses the CK (Chidamber and Kemerer) OO (object-oriented) metrics.…”

Section: Related Workmentioning

confidence: 99%

Optimal Machine learning Model for Software Defect Prediction

Lamba¹,

JaganNathUniversity²,

Kavita³

et al. 2019

IJISA

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Machine Learning is a division of Artificial Intelligence which builds a system that learns from the data. Machine learning has the capability of taking the raw data from the repository which can do the computation and can predict the software bug. It is always desirable to detect the software bug at the earliest so that time and cost can be reduced. Feature selection technique wrapper and filter method is used to find the most optimal software metrics. The main aim of the paper is to find the best model for the software bug prediction. In this paper machine learning techniques linear Regression, Random Forest, Neural Network, Support Vector Machine, Decision Tree, Decision Stump are used and comparative analysis has been done using performance parameters such as correlation, R-squared, mean square error, accuracy for software modules named as ant, ivy, tomcat, berek, camel, lucene, poi, synapse and velocity. Support vector machine outperform as compare to other machine learning model.

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Software Defect Prediction Tool based on Neural Network

Cited by 19 publications

References 17 publications

Design of Software Fault Prediction Model Using BR Technique

Design of Software Fault Prediction Model Using BR Technique

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Optimal Machine learning Model for Software Defect Prediction

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