A Hybrid Approach for Software Fault Prediction Using Artificial Neural Network and Simplified Swarm Optimization

Pahal, Ankit; Chillar, R S

doi:10.17148/ijarcce.2017.63140

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Predicting software defects can help developers in fixing bugs and reduce error propagation with less efforts using classification and regression models. [1] Hybrid approach is the combination of artificial neural network (ANN) and Simplified Swarm Optimization (SSO) for fault prediction. ANN helps in categorizing fault-prone and nonfault-prone segments in software.…”

Section: Related Workmentioning

confidence: 99%

Learning based Software Defect Prediction

Tankala

2023

IJCIR

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Software Engineering is a Comprehensive domain since software system have become larger and complex than ever. Such software characteristics make it very complex to develop fault free software. Therefore, identifying defects automatically and fixing them is challenging task. Improper modelling, lack of requirement specifications, wrong coding, poor configuration management may cause defects which leads to failure of software system. Such defects must be detected and fixed at early stage of software development to minimize cost. Machine learning algorithms widely used in the software defect prediction also achieves good results in predicting software defects using deep learning techniques. In this paper, we are providing comparative study of various algorithms like convolutional neural networks, multi-layer Perceptrons in identifying defects in software. Moreover, the experiments conducted on NASA datasets.

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Section: Related Workmentioning

confidence: 99%

Learning based Software Defect Prediction

Tankala

2023

IJCIR

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“…That was obvious in many fields like classification, pattern recognition, and speech recognition. Reference [4] NN is a dominating supervised learning technique [8]. Reference [4] designed a neural network model to measure the performance using the mean squared error function that provides the result in terms of accuracy.…”

Section: B Neural Network Approachmentioning

confidence: 99%

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

2020

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The discovery of software faults at early stages plays an important role in improving software quality; reduce the costs, time, and effort that should be spent on software development. Machine learning (ML) have been widely used in the software faults prediction (SFP), ML algorithms provide varying results in terms of predicting software fault. Deep learning achieves remarkable performance in various areas such as computer vision, natural language processing, speech recognition, and other fields. 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 both algorithms. The experiment results show how modifying parameters is directly affecting the resulting improvement, these parameters are manipulated until the optimal number for each of them is reached. Moreover, the experiments show that the effect of modifying parameters had an important role in prediction performance, which reached a high rate in comparison with the traditional ML algorithm. To validate our assumptions, the experiments are conducted on four common NASA datasets. The result shows how the addressed factors might increase or decrease the fault detection rate measurement. The improvement rate was as follows up to 43.5% for PC1, 8% for KC1, 18% for KC2 and 76.5% for CM1.

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“…The results in a number of fields where this has been tried are spectacular [7]. Multilayered computational models can use deep learning to acquire multi-level abstractions of data [8]. Essential traits are automatically extracted from raw data, and the output becomes robust in the face of changes to the input [9].…”

Section: Introductionmentioning

confidence: 99%

Software Fault Prediction Using Machine Learning Algorithms

Himabindu,

Pranitha Kumari

2022

IJCERT

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Software quality, development time, and cost can all be improved by finding and fixing bugs as soon as possible.Machine learning (ML) has been widely used for software failure prediction (SFP), but there is a wide range in how well different ML algorithms predict SFP failures. The impressive results that deep learning can produce are useful in many different fields of study, including computer vision, natural language processing, speech recognition, and many others. This investigation into Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks seeks to address the factors that may affect the performance of both methods (CNNs). The earlier software errors are found and fixed, the less time, money, and energy are wasted and the higher the likelihood of success and customer satisfaction. While machine learning (ML) and deep learning (DL) have been widely applied to SFP, the results that different algorithms produce can be somewhat inconsistent. This research uses ANN-MLP-based boosting models like XGBoost and CatBoost to enhance accuracy on NASA datasets (Artificial Neural Network-Multi Layer Perceptron). We will use a voting ensemble consisting of ANN-MLP and booster models such as XGBoost and CatBoost to increase precision.

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A Hybrid Approach for Software Fault Prediction Using Artificial Neural Network and Simplified Swarm Optimization

Cited by 8 publications

References 12 publications

Learning based Software Defect Prediction

Learning based Software Defect Prediction

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

Software Fault Prediction Using Machine Learning Algorithms

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