Mapping General System Characteristics to Non- Functional Requirements

The Function point analysis (FPA) method is the preferred scheme of estimation for project managers to determine the size, effort, schedule, resource loading and other such parameters. The FPA method by International Function Point Users Group (IFPUG) has captured the critical implementation features of an application through fourteen general system characteristics. However, Non- functional requirements (NFRs) such as functionality, reliability, efficiency, usability, maintainability, portability, etc. have not been included in the FPA estimation method. This paper discusses some of the NFRs and tries to determine a degree of influence for each of them. An attempt to factor the NFRs into estimation has been made. This approach needs to be validated with data collection and analysis.Comment: 5 page

Organisational learning model for utility asset management using knowledge engineering approach.

Under the evolving environment, a utility company is required to improve the operation and maintenance of its physical assets usually in the forms of an asset management program. This paper proposes an organisational learning model for the utility companies with respect to the asset management activities. CommonKADS is utilised as a tool to capture the knowledge associated with managing the assets from the learning processes of the utility company. A case study of Bangpakong power plant in Thailand is presented. The results show that by applying the proposed methodologies, the learning processes within the utility companies can be categorised and explained by five major learning steps of breakdown, corrective, preventive, predictive, and proactive maintenances

Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms.

Adjustment of a given age distribution to a desired age distribution within a required time frame is dynamically performed for the purpose of Human Resource (HR) planning in Human Resource Management (HRM). The adjustment process is carried out by adding the adjustment magnitudes to the existing number of employees at the selected age groups on the yearly basis. A model of a discrete dynamical system is employed to emulate the evolution of the age distribution used under the adjustment process. Genetic Algorithms (GA) is applied for determining the adjustment magnitudes that influence the dynamics of the system. An interesting aspect of the problem lies in the high number of constraints; though the constraints are fundamental, they are considerably higher in number than in many other optimization problems. An adaptive penalty scheme is proposed for handling the constraints. Numerical examples show that GA with the utilized adaptive penalty scheme provides potential means for HR planning in HRM

Pressure Ulcer Risk and Prevention: Examining the Inter-Rater Reliability of the National Database of Nursing Quality Indicators® (NDNQI)

ABSTRACT Measuring and reporting performance have become the norm. The purpose of this descriptive multi-site (N = 36 NDNQI-participating hospitals) study was to examine the reliability of the National Database of Nursing Quality Indicators® (NDNQI®) pressure ulcer (PrU) risk and prevention measures. This is the first known study to examine the inter-rater reliability of these measures. Data for Part 1 of this two-part study were extracted from 1,637 patient records by 120 raters. One rater at each hospital was considered the "expert". Agreement between the expert and non-expert raters was calculated for the risk measures. Among the patients, 530 were "at risk" for PrU, and included in calculations of agreement for the prevention measures. In Part 2, raters completed an online survey about the methods they use to collect these data. Cohen's kappa values varied widely within and across hospitals. Because most patients were assessed for PrU risk, and those at risk received prevention, the prevalence of a "Yes" response was high suggesting prevalence-adjusted kappa (PAK) may be a better estimate of inter-rater reliability than Cohen's kappa. PAK values for: Skin assessment, PAK = .977, 95% CI [.966 - .989]; Risk assessment, PAK = .978, 95% CI [.964 -.993]; Time since last risk assessment, PAK = .790, 95% CI [.729 - .852]; Risk assessment scale, PAK = .997, 95% CI [.991 - 1.0]; Risk status, PAK = .877, 95% CI [.838 - .917]; Any prevention, PAK = .856, 95% [.769 - .943]; Skin assessment documented, PAK = .956, 95% CI [.904 - 1.0]; and Pressure-redistribution surface use, PAK = .839, 95% CI [.763 - .916] indicated substantial to near perfect agreement. PAK values for: Routine repositioning, PAK = .577, 95% CI [.494 - .661]; Nutritional support, PAK = .500, 95% CI [.418 - .581]; and Moisture management, PAK = .556, 95% CI [.469 - .643] indicated moderate agreement. Results provide support for the reliability of all (5) PrU risk measures, and three of six prevention measures. Areas of disagreement between the expert and non-expert raters should direct education to improve reliability. Results of the online survey suggest raters need further training on the NDNQI guidelines for PrU data collection

Test data generation from UML state machine diagrams using GAs

Automatic test data generation helps testers to validate software against user requirements more easily. Test data can be generated from many sources; for example, experience of testers, source program, or software specification. Selecting a proper test data set is a decision making task. Testers have to decide what test data that they should use, and a heuristic technique is needed to solve this problem automatically. In this paper, we propose a framework for generating test data from software specifications. The selected specification is Unified Modeling Language (UML) state machine diagram. UML state machine diagram describes a system in term of state which can be changed when there is an action occurring in the system. The generated test data is a sequence of these actions. These sequences of action help testers to know how they should test the system. The quality of generated test data is measured by the number of transitions which is fired using the test data. The more transitions test data can fire, the better quality of test data is. The number of coverage transitions is also used as a feedback for a heuristic search for a better test set. Genetic algorithms (GAs) are selected for searching the best test data. Our experimental results show that the proposed GA-based approach can work well for generating test data for some types of UML state machine diagrams

An automatic test data generation from UML state diagram using genetic algorithm.

Software testing is a part of software development process. However, this part is the first one to miss by software developers if there is a limited time to complete the project. Software developers often finish their software construction closed to the delivery time, they usually don¿t have enough time to create effective test cases for testing their programs. Creating test cases manually is a huge work for software developers in the rush hours. A tool which automatically generates test cases and test data can help the software developers to create test cases from software designs/models in early stage of the software development (before coding). Heuristic techniques can be applied for creating quality test data. In this paper, a GA-based test data generation technique has been proposed to generate test data from UML state diagram, so that test data can be generated before coding. The paper details the GA implementation to generate sequences of triggers for UML state diagram as test cases. The proposed algorithm has been demonstrated manually for an example of a vending machine

Heavy Quarkonium Melting in Large N Thermal QCD

Large N QCD is mostly governed by planar diagrams and should show linear confinement when these diagrams are suitably summed. The linear confinement of quarks in a class of these theories using gravity duals that capture the logarithmic runnings of the coupling constants in the IR and strongly coupled asymptotic conformal behavior in the UV was studied in our previous work. We also extended the theories to high temperatures and argued the possibilities of meltings and suppressions of heavy quarkonium states. In this paper we give a formal proof of melting using very generic choices of UV completions, and point out some subtleties associated with meltings in generic large N theories. Our proof requires only the existence of well defined UV behaviors that are devoid of Landau poles and UV divergences of the Wilson loops, allowing degrees of freedom to increase monotonously with energy scale. We determine the melting temperatures of heavy quarkonium states, which could suggest the presence of deconfinement phase transitions in these theories.Comment: 15 pages, LaTex file, 6 eps figures; v2: typos corrected and references added; v3: some additional typos corrected, and the draft slightly enlarged. Final version to appear in Physics Letters

Enhancing university research activities with knowledge management.

In the new economy, innovation is regarded as one of the solutions for almost every organisation to survive in the new business era. Universities, especially in terms of research activities, are no difference since they strive for novelties which potentially lead to innovation. An experienced researcher in the university has continually created tacit knowledge in a specific domain, but typically found it difficult to share this tacit knowledge among other researchers for the problem solving purpose. To overcome this problem and to better stimulate knowledge sharing activities among university researchers, Knowledge Management and Knowledge Engineering, particularly KADS, are utilised in this paper to assist a group of different domain researchers in putting their experiences together. In this way, each researcher can make explicit his or her tacit knowledge into KADS task, inference and domain knowledge models. The structured knowledge models captured from different researchers can then be merged together. In this paper, the research in Knowledge Management is selected as a case study, and the results show that the relevant tacit knowledge has been made explicit from a researcher and allow other researchers to share the knowledge as well as to add their own knowledge. Hence, their common research theme is effectively created, and also maintained by a group of researchers

Argyres-Douglas Loci, Singularity Structures and Wall-Crossings in Pure N=2 Gauge Theories with Classical Gauge Groups

N=2 Seiberg-Witten theories allow an interesting interplay between the Argyres-Douglas loci, singularity structures and wall-crossing formulae. In this paper we investigate this connection by first studying the singularity structures of hyper-elliptic Seiberg-Witten curves for pure N=2 gauge theories with SU(r+1) and Sp(2r) gauge groups, and propose new methods to locate the Argyres-Douglas loci in the moduli space, where multiple mutually non-local BPS states become massless. In a region of the moduli space, we compute dyon charges for all 2r+2 and 2r+1 massless dyons for SU(r+1) and Sp(2r) gauge groups respectively for rank r>1. From here we elucidate the connection to the wall-crossing phenomena for pure Sp(4) Seiberg-Witten theory near the Argyres-Douglas loci, despite our emphasis being only at the massless sector of the BPS spectra. We also present 2r-1 candidates for the maximal Argyres-Douglas points for pure SO(2r+1) Seiberg-Witten theory.Comment: 81 pages, 41 figures, LaTeX; v2: Minor cosmetic changes and correction of a typographical error in acknowledgement. Final version to appear in JHE