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Business organizations have become heavily dependent on information technology (IT) services. The process of alignment is defined as the mutual synchronization of business goals and IT services. However, achieving mature alignment between business and IT is difficult due to the rapid changes in the business and IT environments. This article provides a systematic review of studies on the alignment of business and IT. The research articles reviewed are based on topics of alignment, the definition of alignment, history, alignment challenges, phases of alignment, alignment measurement approaches, the importance of alignment in business industries, how software engineering helps in better alignment, and the role of the business environment in aligning business with IT. It aims to present a thorough understanding of business-IT alignment and to provide a list of future research directions regarding alignment. To perform the systematic review, we used the guidelines developed by Kitchenham for reviewing the available research papers relevant to our topic. ACM Reference Format:Ullah, A. and Lai, R. 2013. A systematic review of business and information technology alignment.

A component-based system (CBS) is integration centric with a focus on assembling individual components to build a software system. In CBS, component source code information is usually unavailable. Each component also introduces added properties such as constraints associated with its use, interactions with other components and customizability properties. Recent research suggests that most faults are found in only a few system components. A complexity measure at a specification phase can identify these components. However, traditional complexity metrics are not adequate for a CBS as they focus mainly on either lines of code (LOC) or information based on object and class properties. There is therefore a need to develop a new technique for measuring the complexity of a CBS specification (CBSS). This paper describes a structural complexity measure for a CBSS written in Unified Modelling Language (UML) from a system analyst's point of view. A CBSS consists of individual component descriptions characterized by its syntactic, semantic and interaction properties. We identify three factors, interface, constraints and interaction, as primary contributors to the complexity of a CBSS. We also present an application of our technique to a university course registration system.(CBS) constitutes assembling individual components in an interoperable manner, its complexity not only depends on individual components but also on associated interactions. The traditional complexity metrics are not adequate for a CBS because they are either dependent on lines of code (LOC) or measure complexity based on classes, objects and their inheritance properties. Further, information on a component's source code and class structure is usually unavailable. Added properties specific to a component are also introduced such as interface structure, associated constraints associated with its use, interactions among components, and customizability and reusability properties. Thus, the applicability of traditional metrics for a CBS is limited and a method is needed that adequately considers these properties during a CBS specification (CBSS) complexity measure.Recent research suggests that most faults are found in only a few system's components [7]. If we can identify these components at an early specification phase, then precautionary actions can avoid the likelihood of failure and costly maintenance. A CBS complexity measure at a specification level provides an earlier quantitative assessment for more effective identification of fault-prone components. In this paper, we adopt Szyperski's et al. component definition [8]: 'A software component is a unit of composition with contractually specified interface and explicit context dependencies only. A software component can be deployed independently and is subject to composition by a third party'. Thus, we can characterize a CBSS, which consists of individual component definitions, by the component characteristics, context dependencies and the interaction between the other components.The interface plays a fund...

Fuzzy logic controllers (FLCs) are gaining in popularity across a broad array of disciplines because they allow a more human approach to control. Recently, the design of the fuzzy sets and the rule base has been automated by the use of genetic algorithms (GAs) which are powerful search techniques. Though the use of GAs can produce near optimal FLCs, it raises problems such as messy overlapping of fuzzy sets and rules not in agreement with common sense. This paper describes an enhanced genetic algorithm which constrains the optimization of FLCs to produce well-formed fuzzy sets and rules which can be better understood by human beings. To achieve the above, we devised several new genetic operators and used a parallel GA with three populations for optimizing FLCs with 3x3, 5x5, and 7x7 rule bases, and we also used a novel method for creating migrants between the three populations of the parallel GA to increase the chances of optimization. In this paper, we also present the results of applying our GA to designing FLCs for controlling three different plants and compare the performance of these FLC's with their unconstrained counterparts.

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