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Use Case based requirements analysis has attained vast acceptance in requirements engineering. Use Case Diagram represents the functional requirements of the system to be developed from user's perspective and also forms the starting point for documenting requirements using Unified Modeling Language (UML). Therefore the quality of Use Case Diagram has substantial impact on the quality of the resulting system. This paper presents a novel approach for deriving a Use Case Complexity Metric based on dependency and association relationships in the Use Case Diagram. The proposed Use Case Complexity Metric can be used to measure the complexity of the software to be developed and will be particularly useful for early software development estimations.

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Abstract-A key artifact produced during object oriented requirements analysis is Use Case Diagram. Functional requirements of the system under development and relationship of the system and the external world are displayed with the help of Use Case Diagram. Therefore, the quality aspect of the artifact Use Case Diagram must be assured in order to build good quality software. Use Case Diagram quality is assessed by metrics that have been proposed in the past by researchers, based on Use Case Diagram countable features such as the number of actors, number of scenarios per Use Case etc., but they have not considered Use Case dependency relations for metric calculation. In our previous paper, we had proposed a complexity metric. This metric was defined considering association relationships and dependency prevailing in the Use Case Diagram. The key objective in this paper is to validate this complexity metric theoretically by using Briand"s Framework and empirically by performing a Controlled experiment. The results show that we are able to perform the theoretical and empirical validation successfully.

Objective:The nature and magnitude of molecular interactions on hair surfaces underpin the design of formulated products, of which the application involves a competitive adsorption process between cationic surfactants, fatty alcohols and surface actives such as silicone. The knowledge of molecular interaction with hair surface will not only provide insight on the surface binding affinity but also offer an effective methodology in characterizing surface deposits.Methods: Untreated and chemically treated hair samples were treated with either conditioner chassis alone (gel network) or conditioner chassis plus silicone (chassis/TAS). Hair surface interactions against four different chemical functional groups, namely methyl (−CH 3 ), acid (−COOH), amine (−NH 2 ) and hydroxyl (−OH), were quantified in both ambient and aqueous environment using Chemical Force Microscopy, a method based on atomic force microscopy (AFM).Results: Surface adhesion on hair in ambient is dominated by capillary force that is determined by both the wettability of hair fibre (hydrophobic vs. hydrophilic), presence of any deposits and the chemical functionality of the AFM cantilever.Capillary force is diminished and replaced by electrostatic interaction when polar groups are present on both hair and AFM cantilever. A distinctively different force, hydrophobic interaction, plays a major role when virgin hair and hydrophobic functionalized AFM cantilever make contact in water. Conclusion: Results acquired by AFM cantilevers of different functional groupsshow that hydrophobic interaction is a key driver for deposition on virgin hair, whilst electrostatic interaction is the most important one for bleached hair.Interfacial conformation of chassis components upon deposition is determined by the hair surface properties. Our study highlights the possibility of a range of polar groups, not necessarily negatively charged, on the damaged hair. Unlike