A Classification Framework to Detect DoS Attacks

Testing is one of the crucial activities of software development life cycle which ensures the delivery of high quality product. As software testing consumes significant amount of resources so, if, instead of all software modules, only those are thoroughly tested which are likely to be defective then a high quality software can be delivered at lower cost. Software defect prediction, which has now become an essential part of software testing, can achieve this goal. This research presents a framework for software defect prediction by using feature selection and ensemble learning techniques. The framework consists of four stages: 1) Dataset Selection, 2) Pre Processing, 3) Classification, and 4) Reflection of Results. The framework is implemented on six publically available Cleaned NASA MDP datasets and performance is reflected by using various measures including: F-measure, Accuracy, MCC and ROC. First the performance of all search methods within the framework on each dataset is compared with each other and the method with highest score in each performance measure is identified. Secondly, the results of proposed framework with all search methods are compared with the results of 10 well-known supervised classification techniques. The results reflect that the proposed framework outperformed all of other classification techniques.

Section: Resultsmentioning

confidence: 99%

“…Many researchers have used machine learning techniques to solve the binary classification problems such as Sentiment Analysis [1,2,3,4,5,6], Rainfall Prediction [7,8], Network Intrusion Detection [9,10], and Software Defect Prediction [11,12,13,14,15,16]. Some of the studies related to software defect prediction are discussed here.…”

Section: Related Workmentioning

confidence: 99%

Design of (FPID) controller for Automatic Voltage Regulator using Differential Evolution Algorithm

Alawad¹,

Rahman²

2019

“…Many researchers have used machine learning techniques to resolve the classification problems in various areas including: sentiment analysis [11,12,13,14,15,16], network intrusion detection [17] "in press" [18], [19], rainfall prediction [20,21], and software defect prediction [10], [29] etc.. Some selected studies regarding the software defect predictions are discussed here briefly.…”

Section: Related Workmentioning

confidence: 99%

A Feature Selection based Ensemble Classification Framework for Software Defect Prediction

Iqbal¹,

Aftab²,

Ullah³

et al. 2019

Self Cite

Software defect prediction is one of the emerging research areas of software engineering. The prediction of defects at early stage of development process can produce high quality software at lower cost. This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results. The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection. The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments. The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as: "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)". Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.

“…The process of predicting the defect prone software modules is a binary classification problem. Since last two decades, many researchers have been using the machine learning techniques to solve the problems of binary classification such as: Sentiment Analysis [7,8,9,10,11,12], Rainfall Prediction [13,14], Network Intrusion Detection [15,16], and Software Defect Prediction [1,2,3,4,5,6]. Machine learning techniques are broadly categorized in three classes: 1) Supervised, 2) Unsupervised, and 3) Hybrid [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

A Classification Framework for Software Defect Prediction Using Multi-filter Feature Selection Technique and MLP

Iqbal¹,

Aftab²

2020

Self Cite

Production of high quality software at lower cost can be possible by detecting defect prone software modules before the testing process. With this approach, less time and resources are required to produce a high quality software as only those modules are thoroughly tested which are predicted as defective. This paper presents a classification framework which uses Multi-Filter feature selection technique and Multi-Layer Perceptron (MLP) to predict defect prone software modules. The proposed framework works in two dimensions: 1) with oversampling technique, 2) without oversampling technique. Oversampling is introduced in the framework to analyze the effect of class imbalance issue on the performance of classification techniques. The framework is implemented by using twelve cleaned NASA MDP datasets and performance is evaluated by using: F-measure, Accuracy, MCC and ROC. According to results the proposed framework with class balancing technique performed well in all of the used datasets.