Hybrid Concolic Testing

Majumdar, Rupak; Sen, Koushik

doi:10.1109/icse.2007.41

Cited by 325 publications

(206 citation statements)

References 21 publications

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“…Those that exist apart from our own limited previous work are, to the best of our knowledge, studies in concolic execution [4,5]. One concolic approach by Majumdar and Sen [20] has even merged random testing with symbolic execution, though their evaluation only focused on two case examples, and did not explore fault finding effectiveness.…”

Section: Related Workmentioning

confidence: 99%

On the Danger of Coverage Directed Test Case Generation

Staats

Gay

Whalen

et al. 2012

Fundamental Approaches to Software Engineering

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Abstract. In the avionics domain, the use of structural coverage criteria is legally required in determining test suite adequacy. With the success of automated test generation tools, it is tempting to use these criteria as the basis for test generation. To more firmly establish the effectiveness of such approaches, we have generated and evaluated test suites to satisfy two coverage criteria using counterexample-based test generation and a random generation approach, contrasted against purely random test suites of equal size.Our results yield two key conclusions. First, coverage criteria satisfaction alone is a poor indication of test suite effectiveness. Second, the use of structural coverage as a supplement-not a target-for test generation can have a positive impact. These observations points to the dangers inherent in the increase in test automation in critical systems and the need for more research in how coverage criteria, generation approach, and system structure jointly influence test effectiveness.

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Section: Related Workmentioning

confidence: 99%

On the Danger of Coverage Directed Test Case Generation

Staats

Gay

Whalen

et al. 2012

Fundamental Approaches to Software Engineering

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“…Random assignment (Q4) is typically not applicable in this case, e.g., two di erent bug prediction algorithms are applied to the same code [78,85]. (2) A large number of studies, e.g., [46,90] conduct the analysis using descriptive statistics only. Descriptive statistics do not have assumptions to check (Q5).…”

Section: Survey Resultsmentioning

confidence: 99%

Statistical Errors in Software Engineering Experiments: A Preliminary Literature Review

Reyes

Dieste

Fonseca

et al. 2018

EasyChair Preprints

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Background: Statistical concepts and techniques are o en applied incorrectly, even in mature disciplines such as medicine or psychology. Surprisingly, there are very few works that study statistical problems in so ware engineering (SE). Aim: Assess the existence of statistical errors in SE experiments. Method: Compile the most common statistical errors in experimental disciplines. Survey experiments published in ICSE to assess whether errors occur in high quality SE publications. Results: e same errors identi ed in others disciplines were found in ICSE experiments, exhibiting rather large prevalences, over 30% of the reviewed papers in several types of errors such as: a) Missing statistical hypotheses, b) missing sample size calculation, c) failure to assess statistical tests assumptions, and d) uncorrected multiple testing. When experiments restrict to the validation section of a larger research paper, the prevalence of errors increases.e origin of the errors can be traced back to: a) Researchers' inadequate statistical training, and, b) abundance of exploratory research. Conclusions: is paper provides preliminary evidence that SE research su ers the same statistical problems than other experimental disciplines. However, SE community does not seem to be aware of the existence of shortcomings in their experiments, whereas other disciplines work hard to avoid them. Further research is necessary to nd the underlying causes and set corrective measures, but at the outset some actions could be e ective: a) Improve the statistical training of SE researchers, and b) enforce quality assessment and reporting guidelines in SE publications. CCS CONCEPTS•General and reference →Surveys and overviews;

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“…Approaches based on similar ideas that augment testing with formal analysis include [20,18,6,24,28]. While Synergy [20] considers only sequential programs, we concentrate on multithreaded programs.…”

Section: Related Workmentioning

confidence: 99%

“…While Synergy [20] considers only sequential programs, we concentrate on multithreaded programs. Concolic testing [18,6,24] runs symbolic executions simultaneously with concrete executions, but the purpose is to generate new test inputs for better path coverage. In our approach, the purpose of symbolic analysis is to consider all related feasible thread interleavings implicitly, and in the event of inconclusive results, to guide the next concrete execution to follow a different thread schedule (that obeys program control flow semantics) under the same test vector.…”

Section: Related Workmentioning

confidence: 99%

Trace-Driven Verification of Multithreaded Programs

Yang

Sakallah

2010

Formal Methods and Software Engineering

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Abstract. We present a new method that combines the efficiency of testing with the reasoning power of satisfiability modulo theory (SMT) solvers for the verification of multithreaded programs under a user specified test vector. Our method performs dynamic executions to obtain both under-and over-approximations of the program, represented as quantifier-free first order logic formulas. The formulas are then analyzed by an SMT solver which implicitly considers all possible thread interleavings. The symbolic analysis may return the following results: (1) it reports a real bug, (2) it proves that the program has no bug under the given input, or (3) it remains inconclusive because the analysis is based on abstractions. In the last case, we present a refinement procedure that uses symbolic analysis to guide further executions.

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Hybrid Concolic Testing

Cited by 325 publications

References 21 publications

On the Danger of Coverage Directed Test Case Generation

On the Danger of Coverage Directed Test Case Generation

Statistical Errors in Software Engineering Experiments: A Preliminary Literature Review

Trace-Driven Verification of Multithreaded Programs

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