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 systemâs 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.
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Improving Proceeding Test Case Prioritization with Learning Software Agents
Semantic Scholar · Computer Science · 2014
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 systemâs 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.