A support vector machine model for intelligent selection of data representations

Abstract

The design and implementation of efficient abstract data types are important issues for software developers. Selecting and creating the appropriate data structure for implementing an abstract data type is not a trivial problem for a software developer, as it is hard to anticipate all the usage scenarios of the deployed application. Moreover, it is not clear how to select a good implementation for an abstract data type when access patterns to it are highly variant, or even unpredictable. The problem of automatic data structure selection is a complex one because each particular data structure is usually more efficient for some operations and less efficient for others, that is why a static analysis for choosing the best representation can be inappropriate, as the performed operations cannot be statically predicted. Therefore, we propose a predictive model in which the software system learns to choose the appropriate data representation, at runtime, based on the effective data usage pattern. This paper describes a novel approach in using a support vector machine model in order to dynamically select the most suitable representation for an aggregate according to the software system's execution context. Computational experiments confirm a good performance of the proposed model and indicates the potential of our proposal. The advantages of our approach in comparison with similar existing approaches are also emphasized.