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Effort estimation by analogy uses information from former similar projects to predict the effort for a new project. Existing analogy-based methods are limited by their inability to handle non-quantitative data and missing values. The accuracy of predictions needs improvement as well. In this paper, we propose a new flexible method called AQUA that is able to overcome the limitations of former methods. AQUA combines ideas from two known analogy-based estimation techniques: case-based reasoning and collaborative filtering. The method is applicable to predict effort related to any object at the requirement, feature, or project levels. Which are the main contributions of AQUA when compared to other methods? First, AQUA supports non-quantitative data by defining similarity measures for different data types. Second, it is able to tolerate missing values. Third, the results from an explorative study in this paper shows that the prediction accuracy is sensitive to both the number N of analogies (similar objects) taken for adaptation and the threshold T for the degree of similarity, which is true especially for larger data sets. A fixed and small number of analogies, as assumed in existing analogy-based methods, may not produce the best accuracy of prediction. Fourth, a flexible mechanism based on learning of existing data is proposed for determining the appropriate values of N and T likely to offer the best accuracy of prediction. New criteria to measure the quality of prediction are proposed. AQUA was validated against two internal and one public domain data sets with non-quantitative attributes and missing values. The obtained results are encouraging. In addition, Empir Software Eng (2007) 12:65-106 a comparative analysis with existing analogy-based estimation methods was conducted using three publicly available data sets that were used by these methods. In two of the three cases, AQUA outperformed all other methods.

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A review is conducted to deeply analyse and map the research landscape of current technologies in finger vein (FV) biometric authentication in medical systems into a coherent taxonomy. This research focuses on articles related to the keywords 'biometrics', 'finger veins' and 'verification' and their variations in three major databases, namely, Web of Science, ScienceDirect and IEEE Xplore. The final set of collected articles related to FV biometric authentication systems is divided into software-and hardware-based systems. In the first category, software development attempts are described. The experiment results, frameworks, algorithms and methods that perform satisfactorily are presented. Moreover, the experiences obtained from conducting these studies are discussed. In the second category, hardware development attempts are described. The final articles are discussed from three aspects, namely, (1) number of publications, (2) problem type, proposed solutions, best results and evaluation methods in the included studies and (3) available databases containing different scientific work collected from volunteers, such as staff and students. The basic characteristics of this emerging field are identified from the following aspects: motivations of using FV biometric technology in authentication systems, open challenges that impede the technology's utility, authors' recommendations and future research prospects. A new solution is proposed to address several issues, such as leakage of biometrics that leads to serious risks due to the use of stolen FV templates and various spoofing and brute-force attacks in decentralised network architectures in medical systems, including access points and various database nodes without a central point. This work contributes to literature by providing a detailed review of feasible alternatives and research gaps, thereby enabling researchers and developers to develop FV biometric authentication medical systems further. Insights into the importance of such a technology and its integration into different medical applications and fields are also provided.

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Software release planning can be described as a process consisting of the following three phases: (i) strategic release planning, i.e. the assignment of features to subsequent releases, (ii) operational release planning, i.e. the allocation of resources to tasks within each individual release, and (iii) dynamic re‐planning, i.e. the revision of plans to handle unexpected changes imposed on product/project managers responsible for the realization of individual releases. Example changes include the addition or removal of features and/or developers, adjustments due to over‐estimated developer productivity, or under‐estimated work volume of feature‐specific tasks, and adjusted degrees of task dependencies. The research presented in this article mainly focuses on phase (iii), in conjunction with phase (ii), of the release planning process, assuming that phase (i) has already been completed. For that purpose, we present a hybrid intelligence decision‐support method PRP (Planning/Re‐planning), and as its integral part a discrete‐event simulation model called DynaReP (Dynamic Re‐planner). The applicability, effectiveness, and efficiency of the proposed method and model are illustrated through a series of typical release planning and re‐planning scenarios on operational level. Copyright © 2008 John Wiley & Sons, Ltd.

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