Narrow Results

Software size estimation at the early analysis phase of software development lifecycle is crucial for predicting the associated effort and cost. Analysis phase captures the functionality addressed in the software to be developed in object-oriented software development life-cycle. Unified modeling language captures the functionality of the software at the analysis phase based on use case model. This paper proposes a new method named as use case model function point to estimate the size of the object-oriented software at the analysis phase itself. While this approach is based on use case model, it also adapts the function point analysis technique to use case model. The various features such as actors, use cases, relationship, external reference, flows, and messages are extracted from use case model. Eleven rules have been derived as guidelines to identify the use case model components. The function point analysis components are appropriately mapped to use case model components and the complexity based on the weightage is specified to calculate use case model function point. This proposed size estimation approach has been evaluated with the object-oriented software developed in our software engineering laboratory to assess its ability to predict the developmental size. The results are empirically analysed based on statistical correlation for substantiating the proposed estimation method.

Software size estimation is an important aspect in software development projects because poor estimations can lead to late delivery, cost overruns and possibly project failure. Backfiring is a popular technique for sizing and predicting the volume of source code by converting the function point metric into source lines of code mathematically using conversion ratios. While this technique is popular and useful, there is a high margin of error in backfiring. This research introduces a new method to reduce this margin of error. Neural networks and fuzzy logic in software prediction models have been demonstrated in the past to have improved performance over traditional techniques. For this reason, a neuro-fuzzy approach is introduced to the backfiring technique to calibrate the conversion ratios. This paper presents the neuro-fuzzy calibration solution and compares the calibrated model against the default conversion ratios currently used by software practitioners.

The cloud-desktop based on Virtual Desktop Infrastructure (VDI) is deployed more often as the advanced mobile office solution. However, it is an assignable challenge to choose a fit VDI product at low cost for too many testing features to be investigated. In this paper, a multi-dimension factor decision-making model framework using back-propagation neural networks (MDMFBP) is proposed for the VDI-based cloud-desktop application evaluation. MDMFBP is highly data adaptive, applies and is able to account for correlation as well as interactions among features. This makes MDMFBP particularly appealing for high-dimensional cloud-desktop testing feature analysis. The experiment results show that our MDMFBP is workable, easy to implement and result in good estimation accuracies.