Efficient Dynamic Test Case Generation for Path-Based Coverage Criteria

2026-02-21

AI Summary

This work addresses the limitations of traditional test case generation approaches, which require precomputing complete test suites to satisfy path coverage criteria such as Prime Path and Simple Cycle, resulting in high memory overhead and inflexibility. To overcome these issues, the paper proposes a dynamic, incremental generation method based on an improved Johnson’s algorithm. By incorporating a streaming generation mechanism and a novel theoretical characterization of strongly connected components, the approach eliminates the need to enumerate all paths upfront and instead supports on-demand generation of test cases for multiple coverage criteria. It also enables users to control the number of loop iterations in generated paths, thereby balancing test suite size and testing strength. Experimental results demonstrate that the proposed method significantly outperforms existing techniques in memory efficiency, scalability, and execution time, making it suitable for efficient test generation on control flow graphs of arbitrary size.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Security and Privacy: Large-scale security measurements

Abstract

We present a novel approach to test-case generation that satisfies four white-box, path-based coverage criteria: Prime Path, Simple Cycle, Simple Path, and Edge-Acyclic Path. Our method builds on a modified version of Johnson algorithm and enables test cases to be generated incrementally and on demand, rather than requiring the entire test suite to be computed upfront. This streaming capability represents a substantial advancement over existing approaches, as it allows testers to begin executing and refining tests immediately, thereby significantly improving the efficiency of test design. Our solution is inherently memory efficient, as it does not store all discovered coverage items; instead, it retains only the minimal set of paths required to generate subsequent coverage items on the fly. As a result, the approach scales to arbitrarily large graphs. In addition, the algorithm gives testers explicit control over the size of the generated test suite by allowing them to restrict the number of cycles permitted in a test path. The approach is grounded in new theoretical insights, most notably a novel characterization of prime paths in terms of the strongly connected components of control-flow graphs. We complement these theoretical contributions with a practical implementation and a comprehensive empirical evaluation. The results demonstrate that our method not only outperforms existing techniques in terms of execution time and memory consumption, but also provides testers with a more flexible and efficient tool for achieving high coverage while substantially reducing test design overhead.

Problem

• test case generation
• path-based coverage
• dynamic testing
• control-flow graph
• software testing

Innovation

• dynamic test case generation
• path-based coverage
• incremental testing
• memory-efficient algorithm
• strongly connected components