Estimating software project effort using analogies

Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. We describe an alternative approach to estimation based upon the use of analogies. The underlying principle is to characterise projects in terms of features (for example, the number of interfaces, the development method or the size of the functional requirements document). Completed projects are stored and then the problem becomes one of finding the most similar projects to the one for which a prediction is required. Similarity is defined as Euclidean distance in n-dimensional space where n is the number of project features. Each dimension is standardised so all dimensions have equal weight. The known effort values of the nearest neighbours to the new project are then used as the basis for the prediction. The process is automated using a PC based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From this work we argue that estimation by analogy is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques

Software project economics: A roadmap

The objective of this paper is to consider research progress in the field of software project economics with a view to identifying important challenges and promising research directions. I argue that this is an important sub-discipline since this will underpin any cost-benefit analysis used to justify the resourcing, or otherwise, of a software project. To accomplish this I conducted a bibliometric analysis of peer reviewed research articles to identify major areas of activity. My results indicate that the primary goal of more accurate cost prediction systems remains largely unachieved. However, there are a number of new and promising avenues of research including: how we can combine results from primary studies, integration of multiple predictions and applying greater emphasis upon the human aspects of prediction tasks. I conclude that the field is likely to remain very challenging due to the people-centric nature of software engineering, since it is in essence a design task. Nevertheless the need for good economic models will grow rather than diminish as software becomes increasingly ubiquitous

Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods