A prescriptive analytical logic model for error analysis on enterprise level of software application
List of Authors
  • Hoo Meng Wong , Sagaya Sabestinal Amalathas

Keyword
  • Analytic Hierarchy Process, Log File Analysis, Logic Model, Supervised Learning

Abstract
  • Despite its multiple periods, Information Technology (IT) continues to accelerate its growth. Today, the great bulk of corporate operations rely heavily on software programs. Particularly for businesses that rely on the software application to handle a high volume of commercial transactions. As the volume of daily business transactions increases, it is undesirable to extend the downtime window associated with a malfunctioning software application. Regardless of the variety of variables or causes that can result in a severe error in a software application and consequent downtime. If the fundamental cause of a software application's unavailability can be reliably determined within the service level agreement (SLA) or even lower, the downtime timeframe can be reduced. However, the principal cause of the error could be within the software application layer or could be caused by an external factor. This complicates the root cause analysis procedure, much more so when dealing with an enterprise software application with many levels of security (such as a client tier, a web tier, an application layer, middleware, and database tier). In these circumstances, accurately identifying the root cause becomes far more challenging, as root cause analysis requires more than one event recording file (i.e., log file). With this degree of complication, the duration of the root cause analysis activity may be prolonged. The total amount of time required increases, and the rise in total analysis time suggests that it may take longer to restore the software service for users. It is essential to have a logic model capable of precisely identifying the error's root cause. The significance of the Prescriptive Analytical Logic Model (PALM) lies in the fact that it includes not only supervised learning for error detection and optimal treatment of the identified root cause but also AHP for prioritizing which errors to resolve first. The optimum treatment is to reduce the time required for root cause analysis procedures and increase their accuracy by resolving the legitimate error with the highest priority.

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