An Experimental Comparison of Three Machine Learning Techniques for Web Cost Estimation

Many comparative studies on the performance of machine learning (ML) techniques for web cost estimation (WCE) have been reported in the literature. However, not much attention have been given to understanding the conceptual differences and similarities that exist in the application of these ML techniques for WCE, which could provide credible guide for upcoming practitioners and researchers in predicting the cost of new web projects. This paper presents a comparative analysis of three prominent machine learning techniques – Case-Based Reasoning (CBR), Support Vector Regression (SVR) and Artificial Neural Network (ANN) – in terms of performance, applicability, and their conceptual differences and similarities for WCE by using data obtained from a public dataset (www.tukutuku.com). Results from experiments show that SVR and ANN provides more accurate predictions of effort, although SVR require fewer parameters to generate good predictions than ANN. CBR was not as accurate, but its good explanation attribute gives it a higher descriptive value. The study also outlined specific characteristics of the 3 ML techniques that could foster or inhibit their adoption for WCE

An Intelligent Framework for Estimating Software Development Projects using Machine Learning

The IT industry has faced many challenges related to software effort and cost estimation. A cost assessment is conducted after software effort estimation, which benefits customers as well as developers. The purpose of this paper is to discuss various methods for the estimation of software effort and cost in the context of software engineering, such as algorithmic methods, expert judgment methods, analogy-based estimation methods, and machine learning methods, as well as their different aspects. In spite of this, estimation of the effort involved in software development are subject to uncertainty. Several methods have been developed in the literature for improving estimation accuracy, many of which involve the use of machine learning techniques. A machine learning framework is proposed in this paper to address this challenging problem. In addition to being completely independent of algorithmic models and estimation problems, this framework also features a modular architecture. It has high interpretability, learning capability, and robustness to imprecise and uncertain inputs

ROUTING IN MOBILE AD-HOC NETWORKS: SCALABILITY AND EFFICIENCY

Mobile Ad-hoc Networks (MANETs) have received considerable research interest in recent years. Because of dynamic topology and limited resources, it is challenging to design routing protocols for MANETs. In this dissertation, we focus on the scalability and efficiency problems in designing routing protocols for MANETs. We design the Way Point Routing (WPR) model for medium to large networks. WPR selects a number of nodes on a route as waypoints and divides the route into segments at the waypoints. Waypoint nodes run a high-level inter-segment routing protocol, and nodes on each segment run a low-level intra-segment routing protocol. We use DSR and AODV as the inter-segment and the intra-segment routing protocols, respectively. We term this instantiation the DSR Over AODV (DOA) routing protocol. We develop Salvaging Route Reply (SRR) to salvage undeliverable route reply (RREP) messages. We propose two SRR schemes: SRR1 and SRR2. In SRR1, a salvor actively broadcasts a one-hop salvage request to find an alternative path to the source. In SRR2, nodes passively learn an alternative path from duplicate route request (RREQ) packets. A salvor uses the alternative path to forward a RREP when the original path is broken. We propose Multiple-Target Route Discovery (MTRD) to aggregate multiple route requests into one RREQ message and to discover multiple targets simultaneously. When a source initiates a route discovery, it first tries to attach its request to existing RREQ packets that it relays. MTRD improves routing performance by reducing the number of regular route discoveries. We develop a new scheme called Bilateral Route Discovery (BRD), in which both source and destination actively participate in a route discovery process. BRD consists of two halves: a source route discovery and a destination route discovery, each searching for the other. BRD has the potential to reduce control overhead by one half. We propose an efficient and generalized approach called Accumulated Path Metric (APM) to support High-Throughput Metrics (HTMs). APM finds the shortest path without collecting topology information and without running a shortest-path algorithm. Moreover, we develop the Broadcast Ordering (BO) technique to suppress unnecessary RREQ transmissions

Enhancing Performance by Salvaging Route Reply Messages in On-Demand Routing Protocols for MANETs

Researchers prefer on-demand routing protocols in mobile ad hoc networks where resources such as energy and bandwidth are constrained. In these protocols, a source discovers a route to a destination typically by flooding the entire or a part of the network with a route request (RREQ) message. The destination responds by sending a route reply (RREP) message to the source. The RREP travels hop by hop on the discovered route in the reverse direction or on another route to the source. Sometimes the RREP can not be sent to the intended next hop by an intermediate node due to node mobility or network congestion. Existing on-demand routing protocols handle the undeliverable RREP as a normal data packet - discard the packet and initiate a route error message. This is highly undesirable because a RREP message has a lot at stake – it is obtained at the cost of a large number of RREQ transmissions, which is an expensive and timeconsuming process. In this paper, we propose the idea of salvaging route reply (SRR) to improve the performance of on-demand routing protocols. We present two schemes to salvage an undeliverable RREP. Scheme one actively sends a one-hop salvage request message to find an alternative path to the source, while scheme two passively maintains a backup path to the source. Furthermore, we present the design of two SRR schemes in AODV and prove that routes are loop-free after a salvaging. We conduct extensive simulations to evaluate the performance of SRR, and the simulation results confirm the effectiveness of the SRR approach

Cooperation as a Service in VANET: Implementation and Simulation Results

The past decade has witnessed the emergence of Vehicular Ad-hoc Networks (VANET), specializing from the well-known Mobile Ad Hoc Networks (MANET) to Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. While the original motivation for Vehicular Networks was to promote traffic safety, recently it has become increasingly obvious that Vehicular Networks open new vistas for Internet access, providing weather or road condition, parking availability, distributed gaming, and advertisement. In previous papers [27,28], we introduced Cooperation as a Service (CaaS); a new service-oriented solution which enables improved and new services for the road users and an optimized use of the road network through vehicle\u27s cooperation and vehicle-to-vehicle communications. The current paper is an extension of the first ones; it describes an improved version of CaaS and provides its full implementation details and simulation results. CaaS structures the network into clusters, and uses Content Based Routing (CBR) for intra-cluster communications and DTN (Delay and disruption-Tolerant Network) routing for inter-cluster communications. To show the feasibility of our approach, we implemented and tested CaaS using Opnet modeler software package. Simulation results prove the correctness of our protocol and indicate that CaaS achieves higher performance as compared to an Epidemic approach