Test conditions for fault classes in Boolean specifications

Kapoor, Kalpesh; Bowen, Jonathan P.

doi:10.1145/1243987.1243988

Cited by 29 publications

(36 citation statements)

References 24 publications

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“…The following formula can be designated intuitively to compute the priorities: (27) This formula fulfills the requirement of Definition 8 that p i and Inf i are inversely proportional and the priority for input i also represents the contribution of concerning influence to the total influence. Applying Formula (27), the test pairs can be assigned the priorities as shown below: Below, the effect of the proposed prioritization method will be demonstrated using only one fault hypothesis, ORF for the sake of simplicity. According to ORF, there exist three mutants of expression f as given below: that belong to two different decisions, e.g.…”

Section: Definition 8 (Spectral Test Prioritization) Let F (X)mentioning

confidence: 99%

Prioritizing MCDC test cases by spectral analysis of Boolean functions

Ayav

2017

Software Testing Verif & Rel

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SUMMARYTest case prioritization aims at scheduling test cases in an order that improves some performance goal. One performance goal is a measure of how quickly faults are detected. Such prioritization can be performed by exploiting the Fault Exposing Potential (FEP) parameters associated to the test cases. FEP is usually approximated by mutation analysis under certain fault assumptions. Although this technique is effective, it could be relatively expensive compared to the other prioritization techniques. This study proposes a costeffective FEP approximation for prioritizing Modified Condition Decision Coverage (MCDC) test cases. A strict negative correlation between the FEP of a MCDC test case and the influence value of the associated input condition allows to order the test cases easily without the need of an extensive mutation analysis. The method is entirely based on mathematics and it provides useful insight into how spectral analysis of Boolean functions can benefit software testing.

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Section: Definition 8 (Spectral Test Prioritization) Let F (X)mentioning

confidence: 99%

Prioritizing MCDC test cases by spectral analysis of Boolean functions

Ayav

2017

Software Testing Verif & Rel

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“…Figure 4 shows one IRF of the cause-effect graph in Figure 3 where C1 is replaced by C4. IRF is similar to the fault class, referred to as Variable Reference Fault (VRF) in the fault model for Boolean expressions [3,7,12]. IRF and VRF differ in a way of dealing with a nonsingular expression which contains multiple occurrences of a Boolean variable.…”

Section: B Fault Model Includesmentioning

confidence: 99%

Fault Model For Cause Effect Graph Testing

2017

IJRTER

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Abstract-Cause-effect graph is a directed Graph that maps a set of causes to a set of effects. The Causes may be thought of as input and the effect is thought of as the output. Cause-effect graphs can be used for specifying safety critical systems including avionics control software that are often intended to satisfy Boolean expressions. While Boolean expressions are useful to model predicates and complex conditions for state transitions, it is also true that they are very error prone to introduction of faults. Even though many Boolean specification based testing techniques have been proposed to detect faults of implementations of such specifications, there is almost no research about experimental investigation of the effectiveness of testing techniques with cause-effect graphs. In this paper, we present a new fault model which encompasses a variety of fault classes being hypothesized on the cause-effect graph. We have developed a tool to generate faults according to the fault model and to determine if a testing technique can detect those faults.

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“…The described typical programming errors involve missing or extra literals/variables, and the use of incorrect operators and operands. Some researchers have recently introduced ten fault types for general Boolean expressions, including ENF, SA0, SA1, VNF, ASF, ORF, VRF, MVF, CCD, and CDF [24,10]. However, these terminologies are a bit confusing.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…The value range for the fault-detection ratios on LIF is from 34.45% to 100%, just marginally higher than that of LOF (from 31.6% to 98.51%). Kapoor and Bowen [24] conjectured that ENF might be the weakest one among eight fault types for general Boolean expressions; in other words, the detection of any other fault type can also guarantee the detection of ENF. Though this conjecture has been proven wrong by Chen et al [10], our observation with respect to ENF demonstrated that ENF at least can be revealed very easily as compared with other fault types.…”

Section: Rq1: Fault-detection Effectivenessmentioning

confidence: 99%

Evaluating and Comparing Fault-Based Testing Strategies for General Boolean Specifications: A Series of Experiments

Sun

Zai

Liu

2014

The Computer Journal

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A great amount of fault-based testing strategies have been proposed to generate test cases for detecting certain types of faults in Boolean specifications. However, most of the previous studies on these strategies were focused on the Boolean expressions in the disjunctive normal form, even the irredundant disjunctive normal form -little work has been conducted to comprehensively investigate their performance on general Boolean specifications. In this study, we conducted a series of experiments to evaluate and compare eighteen fault-based testing strategies using over four thousand randomly generated fault-seeded Boolean expressions. In the experiments, a testing strategy is regarded as effective and efficient if it can detect most of the seeded faults using a small number of test cases. Our experimental results show that if a testing strategy is highly effective and efficient when testing the Boolean expressions in the irredundant disjunctive normal form, it also shows high effectiveness and efficiency on general Boolean expressions. It is found that one family of fault-based testing strategies, namely MUMCUT, normally deliver the best performance among all the eighteen strategies. Our study provides an in-depth understanding and insight of fault-based testing for general Boolean expressions.

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Test conditions for fault classes in Boolean specifications

Cited by 29 publications

References 24 publications

Prioritizing MCDC test cases by spectral analysis of Boolean functions

Prioritizing MCDC test cases by spectral analysis of Boolean functions

Fault Model For Cause Effect Graph Testing

Evaluating and Comparing Fault-Based Testing Strategies for General Boolean Specifications: A Series of Experiments

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