Test Data Generation for C Programs with String-Handling Functions

Ruan, Hang; Zhang, Jian; Yan, Jun

doi:10.1109/tase.2008.25

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…In the context of symbolic execution of programs, string abstractions has been recently studied by Ruan et.al. [13], where an approach based on a first-order encoding of string functions is proposed. They study C programs where strings are zero-terminated arrays whose lengths are bounded by constants.…”

Section: Resultsmentioning

confidence: 99%

Path Feasibility Analysis for String-Manipulating Programs

Bjørner

Tillmann

Воронков

2009

Tools and Algorithms for the Construction and Analysis of Systems

142

169

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Abstract. We discuss the problem of path feasibility for programs manipulating strings using a collection of standard string library functions. We prove results on the complexity of this problem, including its undecidability in the general case and decidability of some special cases. In the context of test-case generation, we are interested in an efficient finite model finding method for string constraints. To this end we develop a two-tier finite model finding procedure. First, an integer abstraction of string constraints are passed to an SMT (Satisfiability Modulo Theories) solver. The abstraction is either unsatisfiable, or the solver produces a model that fixes lengths of enough strings to reduce the entire problem to be finite domain. The resulting fixed-length string constraints are then solved in a second phase. We implemented the procedure in a symbolic execution framework, report on the encouraging results and discuss directions for improving the method further.

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Section: Resultsmentioning

confidence: 99%

Path Feasibility Analysis for String-Manipulating Programs

Bjørner

Tillmann

Воронков

2009

Tools and Algorithms for the Construction and Analysis of Systems

142

169

View full text Add to dashboard Cite

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“…The manual software test case generation relies on the experience and level of the testers to a large extent, which is time-consuming. To this end, many researchers have proposed a large number of automated test case generation methods, which include searchbased test case generation [36][37][38][39], constraint solving-based test case generation [40][41][42], requirement-based test case generation [43,44], symbol-based test case generation, executed test case generation [45][46][47][48], and random test case generation method [49]. Search-based test case generation originates from research in artificial intelligence.…”

Section: Test Case Generationmentioning

confidence: 99%

An Approach of Improving the Efficiency of Software Fault Localization based on Feedback Ranking Information

Yang,

Ma,

Guo

et al. 2023

Applied Sciences

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Fault localization, a critical process of software debugging, can be implemented by ranking program statements according to their suspiciousness of being faulty, which, in turn, is calculated based on the execution behaviors of test cases. The performance of fault localization will deteriorate if the actual faulty statement is ranked low in suspiciousness. Intuitively speaking, the quality of the used test cases affects the suspiciousness ranking and thus the efficacy of fault localization. As such, it is necessary to generate test cases with “better” quality to improve the chance of faulty statements being ranked as highly suspicious. In this paper, we propose a software fault localization approach based on feedback ranking information, namely FLFR, according to an improved genetic algorithm. The starting point of the new method is the execution of a set of test cases, which gives a preliminary suspiciousness ranking of program statements. The improved genetic algorithm is iteratively applied to generate new test cases. The new method is evaluated through a series of experiments on four C programs and two Java programs. The experimental results show that the test cases automatically generated by the method can improve the suspiciousness ranking of the faulty statement, and thus enhance the performance of fault localization.

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“…Every path along the tree is a set of constraints that can affect the execution of the program. By using a theorem prover, the satisfiability of constraints can be reasoned [20]. Unfortunately, due to the limitations of theorem provers, a constraint containing statements, such as function calls, is not solvable.…”

Section: Related Workmentioning

confidence: 99%

Scalable Path Search for Automated Test Case Generation

Wang

2022

Electronics

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Test case generation is an important task during software testing. In this paper, we present a new test-case generation framework for C programs. This approach combines dataflow analysis and dynamic symbolic execution together, and more importantly, it efficiently searches the program path space for potential faults based on the tabu search strategy and the program fault statistics. Unlike the traditional symbolic execution, which explores the program space exhaustively and is difficult to apply to complicated programs effectively, our approach automatically explores the feasible paths of hidden faults with high probability. The scalable and efficient path search strategy facilitates bug finding with much fewer test cases generated. We implemented this approach, and the experimental results presented in this paper are attractive.

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Test Data Generation for C Programs with String-Handling Functions

Cited by 9 publications

References 18 publications

Path Feasibility Analysis for String-Manipulating Programs

Path Feasibility Analysis for String-Manipulating Programs

An Approach of Improving the Efficiency of Software Fault Localization based on Feedback Ranking Information

Scalable Path Search for Automated Test Case Generation

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