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.

The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of ML applications in any domain can only be ensured if intelligent capabilities cover all of its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. First, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Second, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/cross-domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned previously be addressed with improved ML architecture and agile operations and propose an ML pipeline based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain, and multi-layer environment and provide decision making on a global or holistic rather than an individual function level.

With continuously rising trends in applications of information and communication technologies in diverse sectors of life, the networks are challenged to meet the stringent performance requirements. Increasing the bandwidth is one of the most common solutions to ensure that suitable resources are available to meet performance objectives such as sustained high data rates, minimal delays, and restricted delay variations. Guaranteed throughput, minimal latency, and the lowest probability of loss of the packets can ensure the quality of services over the networks. However, the traffic volumes that networks need to handle are not fixed and it changes with time, origin, and other factors. The traffic distributions generally follow some peak intervals and most of the time traffic remains on moderate levels. The network capacity determined by peak interval demands often requires higher capacities in comparison to the capacities required during the moderate intervals. Such an approach increases the cost of the network infrastructure and results in underutilized networks in moderate intervals. Suitable methods that can increase the network utilization in peak and moderate intervals can help the operators to contain the cost of network intrastate. This article proposes a novel technique to improve the network utilization and quality of services over networks by exploiting the packet scheduling-based erlang distribution of different serving areas. The experimental results show that significant improvement can be achieved in congested networks during the peak intervals with the proposed approach both in terms of utilization and quality of service in comparison to the traditional approaches of packet scheduling in the networks. Extensive experiments have been conducted to study the effects of the erlang-based packet scheduling in terms of packet-loss, end-to-end latency, delay variance and network utilization.

Location information of sensor nodes in a wireless sensor network is important. The sensor nodes are usually required to ascertain their positions so that the data collected by these nodes can be labeled with this information. On the other hand, certain attacks on wireless sensor networks lead to the incorrect estimation of sensor node positions. In such situations, when the location information is not correct, the data may be labeled with wrong location information that may subvert the desired operation of the wireless sensor network. In this work, we formulate and propose a distance vector hop-based algorithm to provide secure and robust localization in the presence of malicious sensor nodes that result in incorrect position estimation and jeopardize the wireless sensor network operation. The algorithm uses cryptography to ensure secure and robust operation in the presence of adversaries in the sensor network. As a result of the countermeasures, the attacks are neutralized and the sensor nodes are able to estimate their positions as desired. Our secure localization algorithm provides a defense against various types of security attacks, such as selective forwarding, wormhole, Sybil, tampering, and traffic replay, compared with other algorithms which provide security against only one or two types. Simulation experiments are performed to evaluate the performance of the proposed method, and the results indicate that our secure localization algorithm achieves the design objectives successfully. Performance of the proposed method is also compared with the performance of basic distance vector hop algorithm and two secure algorithms based on distance vector hop localization. The results reveal that our proposed secure localization algorithm outperforms the compared algorithms in the presence of multiple attacks by malicious nodes.

Minimum end to end delay, packet delay variations or packet jitter, and packet drop for given throughput in networks plays well known important role not only in achieving better Quality of Service but also have evident effects on network utilization, efficiency and better quality of experience for services being carried over networks. Traffic intensities (TI) are generally used for dimensioning and capacity planning in telecom networks. Usually TI depends on point of presence(POP) and its characteristics like area served, customer density, usage pattern and grade of service that service provider intends to offer for given economic constraints. Most of time, for TI in busiest hour is used to determine required capacities of network resources. In this paper, we have extended original concepts of traffic intensity to period-to-period base rather than busiest hour and utilized period based traffic intensities to make packet scheduling decisions. This provides an opportunity to manage packet scheduling decisions based on dynamics of planned or actual or policy based traffic intensities for given POP and enables us to optimize scheduling decisions and hence end to end delay, jitter, drop behavior of network nodes and increases the interface utilization. This paper provides complete details of methodology and improvements in QoS and Network utilization. To evaluate effects of packet scheduling based on TI, using NS2 we have simulated a model network, and used Traffic Intensity Based Packet Scheduling (TIPS) algorithm to schedule packets and evaluated results of end delay, jitter, and throughput and drop behavior of various network nodes. We have found that TIPS offers optimized results for better QoS and network utilization as compared to existing packet scheduling approaches like Drop Tail, Round Robin, Deficit Round Robin, RED and SFQ. This research paper shall provide details of packet scheduling methodology, TIPS algorithm, simulation results and their comparison with some commonly used packet scheduling algorithms.