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Code smells are the faults in design that reduces the code maintainability. It is essential to identify and control these code smells during the design and development stages of enterprise application implementation in order to achieve higher code maintainability and quality. This research paper presents a framework that engages in modelling and measuring various code smells so that practitioners can focus their efforts on most critical code smells and thus achieve higher code maintainability and quality. The framework uses Total Interpretive Structural Modelling (TISM) for modelling and structuring various code smells. TISM helps in identifying Interrelationship among these code smells. Using MICMAC analysis, these code smells are classified into four clusters based on their driving power and dependence power. Two-way assessment helps in measuring the code smells by deriving the utility measure based on the expert opinion of two set of stakeholders. An experiment is conducted on an enterprise application project and code smells are measured using two-way assessment. It is demonstrated that the code smells having high driving power are optimized which resulted in the elevation of the overall code maintainability of the enterprise applications. The proposed framework optimizes the process of enhancing the overall code maintainability by identification of most critical code smells having higher driving power and then optimizing them.

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Selection of a suitable disaster recovery solution is an essential activity performed in an enterprise to facilitate recovery of critical business functions and Information Technology (IT) systems within a tolerable time limit known as disaster recovery time (DRT). The estimation of optimal DRT plays a significant role in IT as it influences overall costs required to ensure business continuity. The estimation of optimal DRT depends upon the capabilities of a chosen disaster recovery solution and multiple conflicting attributes. This paper presents an integrated approach to selecting the best disaster recovery solution using analytic network process (ANP) and estimating optimal DRT using Multi-Attribute Utility Theory (MAUT). ANP is applied to determine the best disaster recovery solution using seven criteria: people, recovery objectives, security, technology, cost, site infrastructure, and regulatory. MAUT estimates the optimal DRT for the best disaster recovery solution based on three conflicting attributes: cost, reliability, and processed backlog transactions. The proposed approach applies to an enterprise application in the banking sector and this paper tests its effectiveness by comparing the results from four different enterprises. This study offers valuable insights to the disaster recovery practitioner to select the best disaster recovery solution and to estimate optimal DRT.