Mohammad Azzeh

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Accurate and credible software effort estimation is a challenge for academic research and software industry. From many software effort estimation models in existence, Estimation by Analogy (EA) is still one of the preferred techniques by software engineering practitioners because it mimics the human problem solving approach. Accuracy of such a model depends(More)
Context: Software effort estimation at early stage is a crucial task for project bedding and feasibility study. Since collected data at early stage of software development lifecycle is always imprecise and uncertain, it is very hard to deliver accurate estimate. Analogy-based estimation, which is one of the popular estimation methods, is rarely used at(More)
Variants of adaptation techniques have been proposed in previous studies to improve the performance of analogy-based effort estimation. The results of these studies are often contradictory and cannot simply be generalized because there are many uncontrollable source of variations between adaptation studies. The study presented in this paper has been carried(More)
Software effort prediction is an important task in the software development life cycle. Many models including regression models, machine learning models, algorithmic models, expert judgment and estimation by analogy have been widely used to estimate software effort and cost. In this work, a Tree boost (Stochastic Gradient Boosting) model is put forward to(More)
Delivering accurate software effort estimation has been a research challenge for a long time, where none of the existing estimation methods has proven to consistently deliver an accurate estimate. Previous studies have demonstrated that estimation by analogy (EBA) is a viable alternative to other conventional estimation methods in terms of predictive(More)
This paper investigates the applicability of Use Case Point estimation model to global software project development. Nowadays, there is growing trend among leading software companies to outsource their project geographically, in countries with lower labor rate. This new trend increases competitiveness in the software market, which in turn shortens the(More)
One of the major problems with software project management is the difficulty to predict accurately the required effort for developing software applications. Analogy Software effort estimation appears well suited to model problems of this nature. The analogy approach may be viewed as a systematic development of the expert opinion through experience learning(More)
Context: Effort adjustment is an essential part of analogy-based effort estimation, used to tune and adapt nearest analogies in order to produce more accurate estimations. Currently, there are plenty of adjustment methods proposed in literature, but there is no consensus on which method produces more accurate estimates and under which settings. Objective:(More)
Background: Adaptation technique is a crucial task for analogy based estimation. Current adaptation techniques often use linear size or linear similarity adjustment mechanisms which are often not suitable for datasets that have complex structure with many categorical attributes. Furthermore, the use of nonlinear adaptation technique such as neural network(More)
Background: Analogy-Based Effort Estimation (ABE) is one of the efficient methods for software effort estimation because of its outstanding performance and capability of handling noisy datasets. Problem & Objective: Conventional ABE models usually use the same number of analogies for all projects in the datasets in order to make good estimates. Our claim is(More)