Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.asoc.2014.01.026 | Appl. Soft Comput. |
Keywords | Field | DocType |
particular data structure,software system,best representation,efficient abstract data type,intelligent selection,abstract data type,appropriate data representation,effective data usage pattern,automatic data structure selection,appropriate data structure,software developer,support vector machine model,machine learning,support vector machine,data structure | Abstract data type,Data mining,Data structure,External Data Representation,Computer science,Support vector machine,Logical data model,Software system,Software,Artificial intelligence,Machine learning,Test data generation | Journal |
Volume | ISSN | Citations |
18, | 1568-4946 | 1 |
PageRank | References | Authors |
0.35 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriela Czibula | 1 | 80 | 19.53 |
| István Gergely Czibula | 2 | 91 | 11.79 |
| Radu Dan Gaceanu | 3 | 10 | 1.25 |