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Abstract-This paper presents NALASS, a novel software tool that attempts to automate a large part of the Requirements Engineering (RE) process. The tool is based on a methodology that utilizes elements of natural language syntax and semantics to formalize activities of requirements discovery, analysis and specification. NALASS automates the creation of specific question sets for the elicitation stage, the organisation and classification of requirements for the analysis stage, with the use of predefined patterns, and the generation of diagrammatic notations, use case specifications and the SRS document.Index Terms-Automated requirements engineering, natural language.

High body mass index (BMI) has been shown to be associated with asthma, but the pattern of this association is still unclear and may differ by gender or stage of puberty. BMI is only a proxy of adiposity, whereas estimation of body fat percent (BF%) by the bioimpedance technique is considered an accurate measure of adiposity. We investigated whether BMI and BF% behave differently in their association with asthma between genders, before and during adolescence. Design and Methods: In this cross-sectional study of 10,981 schoolchildren, we used logistic regression models to examine the pattern of association of BMI and BF% with asthma. Results: In the case of BF%, both the highest (odds ratio [OR]: 1.68, 95% confidence interval [95% CI]: 1.21-2.30) and lowest (OR: 1.59, 95% CI: 1.13-2.23) z-score categories conferred an increased adjusted risk for active asthma. The likelihood ratio test (LRT) of nonlinearity yielded significant results (P < 0.01) for BF%. In contrast, the LRT for BMI yielded a nonsignificant result (P ¼ 0.45) indicating a linear association of asthma with BMI. A unit increase in BMI z-score conferred an increase in the adjusted odds of active asthma (OR: 1.14, 95% CI: 1.02-1.27). In the case of BF%, the adjusted ORs for active asthma at the highest and lowest z-score categories in both genders, before and during adolescence, were similarly elevated, exhibiting a U-shape pattern. Conclusions: In contrast to the linear association observed with BMI, BF% displayed a U-shaped association with asthma and may be the preferred measure of adiposity in epidemiological studies of asthma in children.

This paper proposes an approach that formalizes specific elements and activities of the use case modeling process in order to overcome problematic issues common to the conventional use case methods, namely the lack of systematic elicitation support in the identification of use case elements, the vagueness introduced by the use of informal natural language to define use case specifications, and the limited support of dedicated software tools that makes UCDA a timeconsuming and error-prone activity. In particular, with the use of our approach, formalization of the stage for identifying the use case elements is achieved with the use of predefined types of use cases and actors, specific guidelines to define associations, relationships and business rules, and formalized sentential patterns. Formalization and clarity of the use case specification is achieved with the use of specific types of actions and guidelines, on one hand, and natural language-based authoring rules, on the other. A dedicated software tool supports the automation of the proposed approach including the automated generation of use case diagrams and specifications. Preliminary empirical evaluation of the proposed approach indicated its effectiveness and efficiency.

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