The Performance Mining Method: Extracting Performance Knowledge from Software Operation Data

The Performance Mining Method: Extracting Performance Knowledge from Software Operation Data

Stella Pachidi, Marco Spruit
Copyright: © 2016 |Pages: 19
DOI: 10.4018/978-1-4666-9840-6.ch009
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Software Performance is a critical aspect for all software products. In terms of Software Operation Knowledge, it concerns knowledge about the software product's performance when it is used by the end-users. In this paper the authors suggest data mining techniques that can be used to analyze software operation data in order to extract knowledge about the performance of a software product when it operates in the field. Focusing on Software-as-a-Service applications, the authors present the Performance Mining Method to guide the process of performance monitoring (in terms of device demands and responsiveness) and analysis (finding the causes of the identified performance anomalies). The method has been evaluated through a prototype which was implemented for an online financial management application in the Netherlands.
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Software Performance Evaluation

Performance is described by the proportion of the amount of effective work that is accomplished by a software system, over the time and resources used, in order to carry out this work (Arinze, Igbaria, & Young, 1992). In practice, software performance may be characterized by different aspects, which are evaluated in measurable terms using different performance metrics, such as response time, throughput, availability, latency, or utilization of resources (e.g. percentage of CPU or memory usage). Software Performance Evaluation is a critical process for all types of software products, indispensable at every stage of the product’s life (Arinze et al., 1992). It is performed to determine that the system meets the user-defined performance goals and detect possible improvement points; to compare a number of alternative designs and select the best design; or to compare a number of different solutions and select the system that is most appropriate for a given set of applications (Jain, 1991).

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