The Art of Software Testing

Myers, Glenford J.; Badgett, Tom; Sandler, Corey

doi:10.1002/9781119202486

Cited by 231 publications

(8 citation statements)

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“…The tests may be seen as a set of processes that are designed, developed and applied throughout the life of software development to detect and correct defects and faults found during and following the implementation of the code, and thus raise the quality and reliability of the program delivered to the end user [2]. However, applying thorough software tests that allow the testing of the software component in its entirety, as well as being both an expensive and timeconsuming task, is an almost impossible one.…”

Section: Introductionmentioning

confidence: 99%

Exciting FPGA cryptographic Trojans using combinatorial testing

Kitsos

Simos

Torres-Jiménez

et al. 2015

2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE)

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Covering Arrays (CA) are mathematical objects used in the functional testing of software components. They enable the testing of all interactions of a given size of input parameters in a procedure, function, or logical unit in general, using the minimum number of test cases. Building CA is a complex task that involves lengthy execution times and high computational loads. The most effective methods for building CAs are algebraic, Greedy, and metaheuristic-based. The latter have reported the best results to date. This paper presents a description of the major contributions made by a selection of different metaheuristics, including simulated annealing, tabu search, genetic algorithms, ant colony algorithms, particle swarm algorithms, and harmony search algorithms. It is worth noting that simulated annealing-based algorithms have evolved as the most competitive, and currently form the state of the art.Keywords: ant colony optimization; Covering Array; genetic algorithms; harmony search algorithm; metaheuristics; particle swarm optimization; simulated annealing; tabu search. ResumenLos Covering Arrays (CA) son objetos matemáticos usados en pruebas funcionales de componentes software. Los CA permiten probar todas las interacciones de un tamaño determinado, de los parámetros de entrada de un Metaheuristic algorithms for building Covering Arrays: A review procedimiento, función o unidad lógica en general, usando el mínimo número de casos de prueba. La construcción de CA es una tarea compleja que requiere largos periodos de ejecución y gran capacidad computacional. Los métodos más efectivos para construir CA son los algebraicos, voraces y basados en metaheurísticas. Estos últimos son los que han arrojado mejores resultados hasta la fecha. Este artículo presenta una descripción de las contribuciones más importantes hechas por diferentes metaheurísticas, incluyendo el simulated annealing (recocido simulado), búsqueda tabú, algoritmos genéticos, algoritmo de la colonia de hormigas, algoritmo de enjambre de partículas y algoritmo de búsqueda armónica. Cabe anotar que los algoritmos basados en recocido simulado se han convertido en los más competitivos y actualmente son el estado del arte.Palabras clave: algoritmo de búsqueda armónica; algoritmos genéticos; búsqueda tabú; Covering Array; metaheurística; optimización por colonia de hormigas; optimización por enjambre de partículas; recocido simulado. ResumoOs Covering Arrays (CA) são objetos matemáticos usados em provas funcionais de componentes software. Os CA permitem provar todas as interações de um tamanho determinado, dos parâmetros de entrada de um procedimento, função ou unidade lógica em geral, usando o mínimo número de casos de prova. A construção de CA é uma tarefa complexa que precisa longos períodos de execução e grande capacidade computacional. Os métodos mais efetivos para construir CA são os algébricos, vorazes e baseados em metaheurísticas. Estes últimos são os que têm produzido melhores resultados até esta data. Este artigo apresenta uma descrição das ...

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Section: Introductionmentioning

confidence: 99%

Exciting FPGA cryptographic Trojans using combinatorial testing

Kitsos

Simos

Torres-Jiménez

et al. 2015

2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE)

View full text Add to dashboard Cite

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“…The graph is traversed back from the effects to the causes and test cases are created according to specific decision rules (cf. [32] and [33]). These rules achieve the maximum probability of finding failures while avoiding the complexity of generating 2 n test cases, where is the number of causes.…”

Section: Fundamentalsmentioning

confidence: 99%

“…Subsequently, CiRA applies the BPST (cf. [32] and [33]) to derive the minimum number of required test cases from each CEG. Figure 6 presents an overview of the CEGs created by CiRA and the respective automatically generated test specifications for each causal requirement included in the THEMAS requirements specification (see Figure 1).…”

Section: Running Examplementioning

confidence: 99%

Automatic Creation of Acceptance Tests by Extracting Conditionals from Requirements: NLP Approach and Case Study

Fischbach¹,

Frattini²,

Vogelsang³

et al. 2022

Preprint

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Acceptance testing is crucial to determine whether a system fulfills end-user requirements. However, the creation of acceptance tests is a laborious task entailing two major challenges: (1) practitioners need to determine the right set of test cases that fully covers a requirement, and (2) they need to create test cases manually due to insufficient tool support. Existing approaches for automatically deriving test cases require semi-formal or even formal notations of requirements, though unrestricted natural language is prevalent in practice. In this paper, we present our tool-supported approach CiRA (Conditionals in Requirements Artifacts) capable of creating the minimal set of required test cases from conditional statements in informal requirements. We demonstrate the feasibility of CiRA in a case study with three industry partners. In our study, out of 578 manually created test cases, 71.8 % can be generated automatically. Additionally, CiRA discovered 80 relevant test cases that were missed in manual test case design. CiRA is publicly available at www.cira.bth.se/demo/.

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“…Over the past few decades, software testing has been the most widely used method for ensuring software quality [1]. The tests may be seen as a set of processes that are designed, developed and applied throughout the life of software development to detect and correct defects and faults found during and following the implementation of the code, and thus raise the quality and reliability of the program delivered to the end user [2]. However, applying thorough software tests that allow the testing of the software component in its entirety, as well as being both an expensive and timeconsuming task, is an almost impossible one.…”

Section: Introductionmentioning

confidence: 99%

“…Even trying to test the simplest software, a large number of test cases can be generated due to the many possible combinations of input parameters. Creating test cases for all these possibilities is an unfeasible and impractical process [1,2]. For example, if it is required to test a software component that receives as input parameters values from five sensors and each sensor can take 10 different values, 10 5 or 100,000 different test cases would then be required to exhaustively test that component.…”

Section: Introductionmentioning

confidence: 99%