Authors:
Sebastian Abele
and
Peter Göhner
Affiliation:
University of Stuttgart, Germany
Keyword(s):
Machine Learning, Test Case Prioritization, Test Suite Optimization, Software Agents.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Evolutionary Computing
;
Fuzzy Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Multi-Agent Systems
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
Abstract:
Test case prioritization is an important technique to improve the planning and management of a system test.
The system test itself is an iterative process, which accompanies a software system during its whole life cycle.
Usually, a software system is altered and extended continuously. Test case prioritization algorithms find
and order the most important test cases to increase the test efficiency in the limited test time. Generally, the
knowledge about a system’s characteristics grows throughout the development. With better experience and
more empirical data, the test case prioritization can be optimized to rise the test efficiency. This article introduces
a learning agent-based test case prioritization system, which improves the prioritization automatically
by drawing conclusions from actual test results.