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.

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.

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.

Network security is an essential element in the day-to-day IT operations of nearly every organization in business. Securing a computer network means considering the threats and vulnerabilities and arrange the countermeasures. Network security threats are increasing rapidly and making wireless network and internet services unreliable and insecure. Intrusion Detection System plays a protective role in shielding a network from potential intrusions. In this research paper, Feed Forward Neural Network and Pattern Recognition Neural Network are designed and tested for the detection of various attacks by using modified KDD Cup99 dataset. In our proposed models, Bayesian Regularization and Scaled Conjugate Gradient, training functions are used to train the Artificial Neural Networks. Various performance measures such as Accuracy, MCC, Rsquared, MSE, DR, FAR and AROC are used to evaluate the performance of proposed Neural Network Models. The results have shown that both the models have outperformed each other in different performance measures on different attack detections.

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