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Many online platforms providing crowd with opportunities to participate in software development projects have been existed for a while. Meanwhile, many enterprises are using crowd source to collaboratively develop their software via these platforms in recent years. However, some software development projects in these platforms hardly attract users to join. Therefore, these project owners need a way to effectively predict the number of participants in their projects and accordingly well plan their software and project specifications, such as the program language and the size of the documentation, in order to attract more individuals to participant in the projects. Compared with the past prediction models, our proposed model can dynamically add the factors, such as number of participants in the initial stage of the project, within the project life cycle and make the adjustment to the prediction model. The proposed model was also verified by using cross validation method. The results show that: 1) The models with the factor “the number of user participation” is more accurate than the model without it. 2) The factors of crowd dimension are more influential on the prediction accuracy than those of software project and owner dimensions. It is suggested that the project owners not only just consider those factors of the software project dimension in the initial stage of the project life cycle but also those factors of crowd and interaction dimensions in the late stage to attract more participants in their projects.

Software classification models have been regarded as an essential support tool in performing measurement and analysis processes. Most of the established models are single-cycled in the model usage stage, and thus require the measurement data of all the model's variables to be simultaneously collected and utilized for classifying an unseen case within only a single decision cycle. Conversely, the multi-cycled model allows the measurement data of all the model's variables to be gradually collected and utilized for such a classification within more than one decision cycle, and thus intuitively seems to have better classification efficiency but poorer classification accuracy. Software project managers often have difficulties in choosing an appropriate classification model that is better suited to their specific environments and needs. However, this important topic is not adequately explored in software measurement and analysis literature. By using an industrial software measurement dataset of NASA KC2, this paper explores the quantitative performance comparisons of the classification accuracy and efficiency of the discriminant analysis (DA)- and logistic regression (LR)-based single-cycled models and the decision tree (DT)-based (C4.5 and ECHAID algorithms) multi-cycled models. The experimental results suggest that the re-appraisal cost of the Type I MR, the software failure cost of Type II MR and the data collection cost of software measurements should be considered simultaneously when choosing an appropriate classification model.

Precision in estimating the required software development effort plays a critical factor in the success of software project management. Most existing software effort estimation models only compare the accuracies of software effort estimates from the historical data without clustering. A potential factor that can affect the accuracies of the established effort estimation models is the homogeneity of the data. However, such investigation on the effects of the accuracies of the derived effort estimates is seldom explored in software effort estimation literature. Therefore, this paper aims to explore the effects of accuracies of the software effort estimation models established from the clustered data by using the International Software Benchmarking Standards Group (ISBSG) repository. The ordinary least square (OLS) regression method is adopted to establish a respective effort estimation model in each cluster of datasets. The empirical experiment results show that the estimation accuracies do not reveal significant differences within the respective dataset clustered by each software effort driver. It also demonstrates that software effort estimation models from the clustered data present almost similar accuracy results compared to models from the entire data without clustering.

Accurate estimates of efforts in software development are necessary in project management practices. Project managers or domain experts usually conduct software effort estimation using their experience; hence, subjective or implicit estimates occur frequently. As most software projects have incomplete information and uncertain relations between effort drivers and the required development effort, the grey relational analysis (GRA) method has been applied in building a formal software effort estimation model for this study. The GRA in the grey system theory is a problem-solving method that is used when dealing with similarity measures of complex relations. This paper examines the potentials of the software effort estimation model by integrating a genetic algorithm (GA) to the GRA. The GA method is adopted to find the best fit of weights for each software effort driver in the similarity measures. Experimental results show that the software effort estimation using an integration of the GRA with GA method presents more precise estimates over the results using the case-based reasoning (CBR), classification and regression trees (CART), and artificial neural networks (ANN) methods.

The ability to accurately and consistently estimate software development efforts is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle, are prone to a certain degree of estimation errors. A software effort estimation model which adopts a fuzzy inference method provides a solution to fit the uncertain and vague properties of software effort drivers. The present paper proposes a fuzzy neural network (FNN) approach for embedding artificial neural network into fuzzy inference processes in order to derive the software effort estimates. Artificial neural network is utilized to determine the significant fuzzy rules in fuzzy inference processes. We demonstrated our approach by using the 63 historical project data in the well-known COCOMO model. Empirical results showed that applying FNN for software effort estimates resulted in slightly smaller mean magnitude of relative error (MMRE) and probability of a project having a relative error of less than or equal to 0.25 (Pred(0.25)) as compared with the results obtained by just using artificial neural network and the original model. The proposed model can also provide objective fuzzy effort estimation rule sets by adopting the learning mechanism of the artificial neural network.

Analogy-based estimation is a widely adopted problem solving method that has been evaluated and confirmed in software effort or cost estimation domains. The similarity measures between pairs of projects play a critical role in the analogy-based software effort estimation models. Such a model calculates a distance between the software project being estimated and each of the historical software projects, and then retrieves the most similar project for generating an effort estimate. Although there exist numerous analogy-based software effort estimation models in literature, little theoretical or experimental works have been reported on the method of deriving an effort estimate from the adjustment of the reused effort based on the similarity distance. The present paper investigates the effect on the improvement of estimation accuracy in analogy-based estimations when the genetic algorithm method is adopted to adjust reused effort based on the similarity distances between pairs of projects. The empirical results show that applying a suitable linear model to adjust the analogy-based estimations is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the proposed model is comparable with those obtained when using other effort estimation methods.

A reliable and accurate estimate of software development effort has always been a challenge for both the software industry and academia. Analogy is a widely adopted problem solving technique that has been evaluated and confirmed in software effort or cost estimation domains. Similarity measures between pairs of effort drivers play a central role in analogy-based estimation models. However, hardly any research has addressed the issue of how to decide on suitable weighted similarity measures for software effort drivers. The present paper investigates the effect on estimation accuracy of the adoption of genetic algorithm (GA) to determine the appropriate weighted similarity measures of effort drivers in analogy-based software effort estimation models. Three weighted analogy methods, namely, the unequally weighted, the linearly weighted and the nonlinearly weighted methods are investigated in the present paper. We illustrate our approaches with data obtained from the International Software Benchmarking Standards Group (ISBSG) repository and the IBM DP services database. The experimental results show that applying GA to determine suitable weighted similarity measures of software effort drivers in analogy-based software effort estimation models is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the nonlinearly weighted analogy method presents better estimate accuracy over the results obtained using the other methods.