Ali Bou Nassif

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Software estimation is a tedious and daunting task in project management and software development. Software estimators are notorious in predicting software effort and they have been struggling in the past decades to provide new models to enhance software estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. This paper presents a novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams. A fuzzy logic approach is used to calibrate the productivity factor in the regression model. Moreover, a multilayer perceptron (MLP) neural network model was developed to predict software effort based on the software size and team productivity. Experiments show that the proposed approach outperforms the original UCP model. Furthermore, a comparison between the MLP and log-linear regression models was conducted based on the size of the projects. Results demonstrate that the MLP model can surpass the regression model when small projects are used, but the log-linear regression model gives better results when estimating larger projects.

Estimating Software Effort Based on Use Case Point Model Using Sugeno Fuzzy Inference System

Software effort estimation is one of the most important tasks in software engineering. Software developers conduct software estimation in the early stages of the software life cycle to derive the required cost and schedule for a project. In the requirements stage, where most software estimation is conducted, the available information is usually imprecise or incomplete. In this paper, a new regression model is created for software effort estimation based on use case point model. Furthermore, a Sugeno Fuzzy Inference System (FIS) approach is applied on this model to improve the estimation. Results show that an improvement of 11 % can be achieved in MMRE after applying the Sugeno fuzzy logic approach.

An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation

Ensembles of adjustment methods are not always superior to single methods.Ensembles of linear methods are more accurate than ensembles of nonlinear methods.Adjustment methods based on GA and NN got the worst accuracy.Changing the value of k makes the prediction models behave diversely.RTM variants is the top ranked type based on Scott-Knott and two-way ANOVA. ContextEffort 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. ObjectiveThis paper investigates the potential of ensemble learning for variants of adjustment methods used in analogy-based effort estimation. The number k of analogies to be used is also investigated. MethodWe perform a large scale comparison study where many ensembles constructed from n out of 40 possible valid variants of adjustment methods are applied to eight datasets. The performance of each method was evaluated based on standardized accuracy and effect size. ResultsThe results have been subjected to statistical significance testing, and show reasonable significant improvements on the predictive performance where ensemble methods are applied. ConclusionOur conclusions suggest that ensembles of adjustment methods can work well and achieve good performance, even though they are not always superior to single methods. We also recommend constructing ensembles from only linear adjustment methods, as they have shown better performance and were frequently ranked higher.

Software Effort Estimation in the Early Stages of the Software Life Cycle Using a Cascade Correlation Neural Network Model

Software cost estimation is a crucial element in project management. Failing to use a proper cost estimation method might lead to project failures. According to the Standish Chaos Report, 65% of software projects are delivered over budget or after the delivery deadline. Conducting software cost estimation in the early stages of the software life cycle is important and this would be helpful to project managers to bid on projects. In this paper, we propose a novel model to predict software effort from use case diagrams using a cascade correlation neural network approach. The proposed model was evaluated based on the MMER and PRED criteria using 214 industrial and 26 educational projects against a multiple linear regression model and the Use Case Point model. The results show that the proposed cascade correlation neural network can be used with promising results as an alternative approach to predict software effort.

Estimating Software Effort Using an ANN Model Based on Use Case Points

In this paper, we propose a novel Artificial Neural Network (ANN) to predict software effort from use case diagrams based on the Use Case Point (UCP) model. The inputs of this model are software size, productivity and complexity, while the output is the predicted software effort. A multiple linear regression model with three independent variables (same inputs of the ANN) and one dependent variable (effort) is also introduced. Our data repository contains 240 data points in which, 214 are industrial and 26 are educational projects. Both the regression and ANN models were trained using 168 data points and tested using 72 data points. The ANN model was evaluated using the MMER and PRED criteria against the regression model, as well as the UCP model that estimates effort from use cases. Results show that the ANN model is a competitive model with respect to other regression models and can be used as an alternative to predict software effort based on the UCP method.

A Treeboost Model for Software Effort Estimation Based on Use Case Points

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 predict software effort based on the Use Case Point method. The inputs of the model include software size in use case points, productivity and complexity. A multiple linear regression model was created and the Tree boost model was evaluated against the multiple linear regression model, as well as the use case point model by using four performance criteria: MMRE, PRED, MdMRE and MSE. Experiments show that the Tree boost model can be used with promising results to estimate software effort.

Data mining techniques in social media

Today, the use of social networks is growing ceaselessly and rapidly. More alarming is the fact that these networks have become a substantial pool for unstructured data that belong to a host of domains, including business, governments and health. The increasing reliance on social networks calls for data mining techniques that is likely to facilitate reforming the unstructured data and place them within a systematic pattern. The goal of the present survey is to analyze the data mining techniques that were utilized by social media networks between 2003 and 2015. Espousing criterion-based research strategies, 66 articles were identified to constitute the source of the present paper. After a careful review of these articles, we found that 19 data mining techniques have been used with social media data to address 9 different research objectives in 6 different industrial and services domains. However, the data mining applications in the social media are still raw and require more effort by academia and industry to adequately perform the job. We suggest that more research be conducted by both the academia and the industry since the studies done so far are not sufficiently exhaustive of data mining techniques.

A hybrid model for estimating software project effort from Use Case Points

Display Omitted Project productivity is key factor in estimating effort from UCP.Project productivity must be flexible and adjustable when historical data is available.Environmental factors are good indicators for predicting productivity.Class decomposition is a good method to produce fine-grained productivity labels.Using fixed productivity ratios is not a good practice from managerial perspective. Early software effort estimation is a hallmark of successful software project management. Building a reliable effort estimation model usually requires historical data. Unfortunately, since the information available at early stages of software development is scarce, it is recommended to use software size metrics as key cost factor of effort estimation. Use Case Points (UCP) is a prominent size measure designed mainly for object-oriented projects. Nevertheless, there are no established models that can translate UCP into its corresponding effort; therefore, most models use productivity as a second cost driver. The productivity in those models is usually guessed by experts and does not depend on historical data, which makes it subject to uncertainty. Thus, these models were not well examined using a large number of historical data. In this paper, we designed a hybrid model that consists of classification and prediction stages using a support vector machine and radial basis neural networks. The proposed model was constructed over a large number of observations collected from industrial and student projects. The proposed model was compared against previous UCP prediction models. The validation and empirical results demonstrated that the proposed model significantly surpasses these models on all datasets. The main conclusion is that the environmental factors of UCP can be used to classify and estimate productivity.

Fuzzy-ExCOM Software Project Risk Assessment

A software development project is considered to be risky due to the uncertainty of the information (customer requirements), the complexity of the process, and the intangible nature of the product. Under these conditions, risk management in software development projects is mandatory, but often it is difficult and expensive to implement. Expert COCOMO is an efficient approach to software project risk management, which leverages existing knowledge and expertise from previous effort estimation activities to assess the risks in new software projects. However, the original method has limitation because it cannot effectively deal with imprecise and uncertain inputs in the form of linguistic terms such as: Very Low (VL), Low (L), Nominal (N), High (H), Very High (VH) and Extra High (XH). This paper introduces the fuzzy-ExCOM methodology that combines the advantages of a fuzzy technique with Expert COCOMO methodology for risk assessment in software projects. The validation of this approach with industrial data shows that fuzzy-ExCOM provides better risk assessment results with a higher level of sensitivity with respect to risk identification compared to the original Expert COCOMO methodology.

Moving from SaaS Applications towards SOA Services

This paper presents a brief introduction of Software as a Service (SaaS) and Service Oriented Architecture (SOA). Specifically, the paper introduces a five-step model to show how SaaS can be offered as SOA services. Furthermore, a real-life scenario is provided to demonstrate the benefits of using the proposed model.