Unit Test Data Generation for C Using Rule-Directed Symbolic Execution

Zhang, Mingzhe; Gong, Yi; Wang, Yawen; Jin, Dahai

doi:10.1007/s11390-019-1935-7

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Critical paths are paths that are more likely to have potential faults. The paths that PSODS deals with are generated by CTS [14,15], which is a code testing system for C programs. CTS selects paths by traversing a control flow graph (CFG).…”

Section: Critical Path Analysismentioning

confidence: 99%

Path-Sensitive Oracle Data Selection via Static Analysis

et al. 2021

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A test oracle is a procedure that is used during testing to determine whether software behaves correctly or not. One of most important tasks for a test oracle is to choose oracle data (the set of variables monitored during testing) to observe. However, most literature on test oracles has focused either on formal specification generation or on automated test oracle construction, whereas little work exists for supporting oracle data selection. In this paper, we present a path-sensitive approach, PSODS (path-sensitive oracle data selection), to automatically select oracle data for use by expected value oracles. PSODS ranks paths according to the possibility that potential faults may exist in them, and the ranked paths help testers determine which oracle data should be considered first. To select oracle data for each path, we introduce quantity and quality analysis of oracle data, which use static analysis to estimate oracle data for their substitution capability and fault-detection capability. Quantity analysis can reduce the number of oracle data. Quality analysis can rank oracle data based on their fault-detection capability. By using quantity and quality analysis, PSODS reduces the cost of oracle construction and improves fault-detection efficiency and effectiveness. We have implemented our approach and applied it to a real-world project. The experimental results show that PSODS is efficient in helping testers construct test oracles. Moreover, the oracle datasets produced by our approach are more effective and efficient than output-only oracles at detecting faults.

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Section: Critical Path Analysismentioning

confidence: 99%

Path-Sensitive Oracle Data Selection via Static Analysis

et al. 2021

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“…Step 3: Fitness value calculation. Calculate the fitness value of individual M i according to formula (8). In the algorithm, the greater the fitness value of an individual is, the better it is, and thus has a greater chance to be selected to the next generation.…”

Section: E Steps Of the Algorithmmentioning

confidence: 99%

“…Given the fact that the key step of software testing is to produce effective test data [8], this paper intends to study the problem of generating test data for programs with randomness. First, we propose a novel adequacy criterion for testing programs with randomness, and then establish an optimization model for the problem of generating test data for this kind of programs.…”

Section: Introductionmentioning

confidence: 99%

Testing Method for Software With Randomness Using Genetic Algorithm

Yao

Gong

et al. 2020

IEEE Access

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The main task of software testing is the generation of test data with high quality in restricted time. But for software with uncertainties, such as randomness, existing methods of generating test data often lose their effectiveness. For software with randomness, a test datum might cover the test target in one run, but obtains different results in the next run. Therefore, special testing methods must be exploited for this kind of software. In order to test software with randomness, we first propose a novel test adequacy criterion, and then build a mathematical model for generating test data according to the new criterion. Finally, we present a method of solving the optimization model using a set-based genetic algorithm. We apply the proposed method to test 12 programs, and compare with traditional genetic algorithm and the random method. From the experimental results we can see that, the proposed adequacy criterion is available for the software with randomness and the proposed algorithm can effectively generate test data.

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