This document discusses using ant colony optimization (ACO) to evaluate software test suites. It begins by introducing software testing and describing how test cases are generated and test suites created. It then proposes using ACO to execute the test suite by formulating it as a traveling salesman problem (TSP) and having "ants" find optimal paths through test cases. The paper outlines the ACO algorithm and applies it to a sample test suite evaluation. It evaluates the accuracy and efficiency of the approach using metrics like precision, recall, iterative best cost, and average node branching. The technique is shown to evaluate test suites more efficiently than other algorithms like Dijkstra's algorithm.