Smoke Test Planning using Answer Set Programming

Smoke testing is an important method to increase stability and reliability of hardware- gramming, Testing depending systems. Due to concurrent access to the same physical resource and the impracticality of the use of virtualization, smoke testing requires some form of planning. In this paper, we propose to decompose test cases in terms of atomic actions consisting of preconditions and effects. We present a solution based on answer set programming with multi-shot solving that automatically generates short parallel test plans. Experiments suggest that the approach is feasible for non-inherently sequential test cases and scales up to thousands of test cases

System Testing with an AI Planner

System testing of software with command language interfaces can be automated using grammar based test generation or through generating tests from an application domain specification. When viewing test case generation as constructing a sequence of commands to achieve a testing goal, AI planning systems appear very promising. This paper reports on automated test generation using an AI planning system and compares the results to tests generated by Sleuth, a tool for automated application domain testing. 1 Introduction Large commercial software systems must be tested extensively before being released to customers. The process of generating and executing test sequences currently involves programmers deriving test cases based on their experience with the software, a process which can be costly and error prone. Alternatively, we can automatically generate these sequences. Automated test generation can significantly affect the success of a software product by reducing the time required to tes..