Mohammad Azzeh

Fuzzy grey relational analysis for software effort estimation

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 on the characteristics of the dataset, which is subject to considerable uncertainty. The inherent uncertainty in software attribute measurement has significant impact on estimation accuracy because these attributes are measured based on human judgment and are often vague and imprecise. To overcome this challenge we propose a new formal EA model based on the integration of Fuzzy set theory with Grey Relational Analysis (GRA). Fuzzy set theory is employed to reduce uncertainty in distance measure between two tuples at the kth continuous feature

Improving analogy software effort estimation using fuzzy feature subset selection algorithm

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

.GRA is a problem solving method that is used to assess the similarity between two tuples with M features. Since some of these features are not necessary to be continuous and may have nominal and ordinal scale type, aggregating different forms of similarity measures will increase uncertainty in the similarity degree. Thus the GRA is mainly used to reduce uncertainty in the distance measure between two software projects for both continuous and categorical features. Both techniques are suitable when relationship between effort and other effort drivers is complex. Experimental results showed that using integration of GRA with FL produced credible estimates when compared with the results obtained using Case-Based Reasoning, Multiple Linear Regression and Artificial Neural Networks methods.

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

Background: Early stage software effort estimation is a crucial task for project bedding and feasibility studies. Since collected data during the early stages of a software development lifecycle is always imprecise and uncertain, it is very hard to deliver accurate estimates. Analogy-based estimation, which is one of the popular estimation methods, is rarely used during the early stage of a project because of uncertainty associated with attribute measurement and data availability. Aims: We have integrated analogy-based estimation with Fuzzy numbers in order to improve the performance of software project effort estimation during the early stages of a software development lifecycle, using all available early data. Particularly, this paper proposes a new software project similarity measure and a new adaptation technique based on Fuzzy numbers. Method: Empirical evaluations with Jack-knifing procedure have been carried out using five benchmark data sets of software projects, namely, ISBSG, Desharnais, Kemerer, Albrecht and COCOMO, and results are reported. The results are compared to those obtained by methods employed in the literature using case-based reasoning and stepwise regression. Results: In all data sets the empirical evaluations have shown that the proposed similarity measure and adaptation techniques method were able to significantly improve the performance of analogy-based estimation during the early stages of software development. The results have also shown that the proposed method outperforms some well know estimation techniques such as case-based reasoning and stepwise regression. Conclusions: It is concluded that the proposed estimation model could form a useful approach for early stage estimation especially when data is almost uncertain.

Software project similarity measurement based on fuzzy C-means

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 and exposure to analogue case studies. The accuracy of such model depends on characteristics of datasets. This paper examines the impact of feature subset selection algorithms on improving the accuracy of analogy software effort estimation model. We proposed a feature subset selection algorithm based on fuzzy logic for analogy software effort estimation models. Validation using two established datasets (ISBSG, Desharnais) shows that using fuzzy features subset selection algorithm in analogy software effort estimation contribute to significant results as other algorithms: Hill climbing, Forward subset selection, and backward subset selection do.

Software effort estimation based on weighted fuzzy grey relational analysis

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 out in order to replicate the assessment and comparison of different adaptation techniques utilised in analogy-based software effort prediction. Empirical evaluation of variants of adaptation techniques with Jack-knifing procedure have been carried out. Seven datasets come from PROMISE data repository were used for benchmarking. The results are also investigated within the presence/absence of feature subset selection algorithm. The current study permitted us to discover that linear adjustment approaches are more accurate than nonlinear adjustment because of the nature of the employed datasets that have, in most cases, normality characteristics.

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

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 based on optimized model tree

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.