Symbolic PathFinder: integrating symbolic execution with model checking for Java bytecode analysis

Păsăreanu, Corina S.; Visser, Willem; Bushnell, David; Geldenhuys, Jaco; Mehlitz, Peter; Rungta, Neha

doi:10.1007/s10515-013-0122-2

Cited by 226 publications

(229 citation statements)

References 38 publications

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“…Also we show our approach is better than state-of-the-art constraint-based test data generator Symbolic PathFinder [21].…”

Section: Experimental Analysismentioning

confidence: 92%

“…We compare to Symbolic PathFinder (SPF) [21], which is the state-ofthe-art of constraint-based test data generation approaches. Consider asample Java program as below:…”

Section: Compare To Constraint-based Test Data Generation Approachesmentioning

confidence: 99%

“…Symbolic execution (SE) is the state-of-theart of CBTDG approaches [21]. Even though there have been significant achievements, SE still faces difficulties in handling infinite loops, array, procedure calls and pointer references in each PUT [22].…”

Section: Introduction *mentioning

confidence: 99%

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Generating Test Data for Software Structural Testing using Particle Swarm Optimization

Thi

2017

JCSCE

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Search-based test data generation is a very popular domain in the field of automatic test data generation. However, existing search-based test data generators suffer fromsome problems. By combining static program analysis and search-based testing, our proposed approach overcomesone of these problems. Considering the automatic ability and the path coverage as the test adequacycriterion, this paper proposes using Particle Swarm Optimization, an alternative search technique, for automating the generation of test data for evolutionary structural testing. Experimental results demonstrate that our test data generator can generate suitable test data with higher path coverage than the previous one.

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“…Also we show our approach is better than state-of-the-art constraint-based test data generator Symbolic PathFinder [21].…”

Section: Experimental Analysismentioning

confidence: 92%

“…We compare to Symbolic PathFinder (SPF) [21], which is the state-ofthe-art of constraint-based test data generation approaches. Consider asample Java program as below:…”

Section: Compare To Constraint-based Test Data Generation Approachesmentioning

confidence: 99%

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Generating Test Data for Software Structural Testing using Particle Swarm Optimization

Thi

2017

JCSCE

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“…There are many kinds of coverage ratio, such as method coverage ratio, branch coverage ratio, and condition coverage ratio. In this part, we use the tool Java Pathfinder [14] to analyse the code coverage ratio from the perspective of method coverage ratio and branch coverage ratio.…”

Section: Analysis Of Code Coveragementioning

confidence: 99%

Large scale software test data generation based on collective constraint and weighted combination method

2017

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Original scientific paper Software reliability test is to test software with the purpose of verifying whether the software achieves reliability requirements and evaluating software reliability level. Statistical-based software reliability testing generally includes three parts: building usage model, test data generation and testing. The construction of software usage model should reflect user's real use as far as possible. A huge number of test cases are required to satisfy the probability distribution of the actual usage situation; otherwise, the reliability test will lose its original meaning. In this paper, we first propose a new method of structuring software usage model based on modules and constraint-based heuristic method. Then we propose a method for the testing data generation in consideration of the combination and weight of the input data, which reduces a large number of possible combinations of input variables to a few representative ones and improves the practicability of the testing method. To verify the effectiveness of the method proposed in this paper, four groups of experiments are organized. The goodness of fit index (GFI) shows that the proposed method is closer to the actual software use; we also found that the method proposed in this paper has a better coverage by using Java Pathfinder to analyse the four sets of internal code coverage.Keywords: constraint; data generation; GFI; software reliability testing; usage model; weighted combination Generiranje ispitnih podataka za softver zasnovano na kolektivnom ograničenju i ponderiranoj metodi kombinacijeIzvorni znanstveni članak Ispitivanje pouzdanosti softvera znači ispitivanje softvera kako bi se provjerilo da li udovoljava zahtjevima pouzdanosti i kako bi se procijenio njegov stupanj pouzdanosti. Statistički temeljeno ispitivanje pouzdanosti softvera općenito uključuje tri dijela: izgradnju modela, generiranje ispitnih podataka i ispitivanje. Stvaranje modela upotrebe softvera treba što je više moguće odražavati korisnikovu stvarnu primjenu. Potreban je ogroman broj ispitivanih slučajeva da bi se zadovoljila distribucija vjerojatnoće u slučaju stvarne upotrebe; inače će ispitivanje pouzdanosti izgubiti originalno značenje. U ovom radu najprije predlažemo novu metodu strukturiranja modela primjene softvera zasnovanu na modulima i heurističkoj metodi koja se temelji na ograničenjima. Zatim predlažemo metodu za generiranje podataka za ispitivanje uzimajući u obzir kombinaciju i težinu ulaznih podataka što smanjuje veliki broj mogućih kombinacija ulaznih varijabli na samo nekoliko reprezentativnih i povećava praktičnost primjene ispitne metode. U svrhu provjere učinkovitosti metode predložene u ovom radu, organizirane su četiri grupe eksperimenata. Ispravnost odgovarajućeg indeksa (GFI-goodness of fit index) pokazuje da je predložena metoda bliža upotrebi aktualnog softvera; također smo ustanovili da ima bolju pokrivenost kod uporabe Java Pathfinder-a za analizu četiri niza pokrivenosti internog koda.

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“…Here, we plan to also cope with missing input data (analogously to the tool Java Pathfinder [12] for model checking of Java bytecode). Our symbolic systems are now triples of the form (Π, Q, C), where the new element C is the so called path constraint (initialized to true).…”

Section: Fig 5 Symbolic Executionmentioning

confidence: 99%

Towards Symbolic Execution in Erlang

Vidal

2015

Lecture Notes in Computer Science

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Symbolic PathFinder: integrating symbolic execution with model checking for Java bytecode analysis

Cited by 226 publications

References 38 publications

Generating Test Data for Software Structural Testing using Particle Swarm Optimization

Generating Test Data for Software Structural Testing using Particle Swarm Optimization

Large scale software test data generation based on collective constraint and weighted combination method

Towards Symbolic Execution in Erlang

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