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An effective software test suite optimization framework using intelligent agents with black board architecture based learning

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  1. Abstract
  2. Introduction
    1. Problems identified
    2. Related work
    3. Intelligent agents ? An introduction
  3. Proposed approach
    1. Black board architecture
  4. Design of intelligent test path optimizer using blackboard architecture
    1. Algorithm - Intelligent Test Path Optimizer Agent (ITPOA)
  5. Design of Intelligent Test Case Optimizer Agent (ITCOA)
    1. Algorithm - Intelligent Test Case Optimizer Agent - ITCOA
  6. Implementation
  7. Comparison charts
  8. Conclusion
  9. References

Artificial Intelligence methods played a vital role in the research of software engineering areas. In particular, Software Testing, a part of Software Development Life Cycle (SDLC) plays a crucial role in quality software development uses the application of artificial intelligence techniques. We need a new testing approach that has both human like intelligence and at the same time completely automated. This is achieved by applying Intelligent Agents in the software testing activity and that in turn helps in identifying only the optimal few test cases which will reduce the total time and cost needed in the testing process. We applied blackboard based learning for the agents to act and react in the specified environment. In our proposed approach, we developed a framework called Intelligent Test Suite Optimizer which consists of two agents namely Intelligent Test Path Optimizer Agent (ITPOA) and Intelligent Test Case Optimizer Agent (ITCOA) for test path and test case optimization respectively. The test adequacy criterions used are all state, branch and statement coverage criterion. Finally, we compared our results against existing algorithm Ant Colony Optimization (ACO) in test optimization and proved that our approach out performed ACO. Keywords: Intelligent Agents, Software Testing, Test optimization, Software Quality, Software under Test (SUT)

[...] Replace a test case with the effective test case with highest mutation score by the following condition: If mut_score(testcasei) > mut_score(testcasej) then replace the testcasei with testcasej. Else retain the test case testcasei. Step Generate the next set of test cases based on the previously selected test cases. Step Repeat steps 1 to 4 till the termination condition is reached. The test cases that have the highest coverage criterion and mutation score are selected and stored in the repositories. V. [...]

[...] Proposed Approach The Intelligent Agent based test optimization framework consists of the following agents: Intelligent Test Path optimizer Agent (ITPOA) Intelligent Test Case optimizer Agent (ITCOA) The agents have several Knowledge Bases and a Rules engine which will be controlled by a controller in them Controller that mediates among these knowledge sources. As per Nii, purpose of the black board is to hold computational and solution state data needed by the knowledge sources. The black board consists of objects from the solution space. [...]

[...] The proposed agents employ black board based learning for intelligent decision making. Once the learning is done, subsequent modifications in the software are monitored by the agent and the agents automatically do the modifications in the representation sub set. Also the changes in test paths and test cases are reflected in the repository and the knowledge base. This includes the monitoring of the modifications made and the situations in which the said change was done and so on. In Section II, we provided the overall framework for the test optimization task. [...]

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