Constraint-Based Approaches to the Covering Test Problem

Hnich, Brahim; Prestwich, Steven; Selensky, Evgeny

doi:10.1007/11402763_13

Cited by 28 publications

(21 citation statements)

References 10 publications

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“…Empirical studies are needed to identify the best prioritization criteria for test suites designed for interaction testing. In addition, effective heuristics for handling hard constraints (forbidden configurations) are sorely needed; constraint satisfaction methods [16] may offer an appropriate mechanism. 1 0 3 5 2 1 4 5 3 2 3 6 4 1 3 6 …”

Section: Discussionmentioning

confidence: 99%

Prioritized interaction testing for pair-wise coverage with seeding and constraints

Bryce

Colbourn

2006

Information and Software Technology

169

141

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Section: Discussionmentioning

confidence: 99%

Prioritized interaction testing for pair-wise coverage with seeding and constraints

Bryce

Colbourn

2006

Information and Software Technology

169

141

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“…Exact algorithms (integer programming, backtracking) have met success only for very small problems [18]. Constraint programming has been applied for strengths greater than two [37]. Meta-heuristic search has been quite effective.…”

Section: Algorithmic Methodsmentioning

confidence: 99%

Locating and detecting arrays for interaction faults

2007

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The identification of interaction faults in component-based systems has focussed on indicating the presence of faults, rather than their location and magnitude. While this is a valuable step in screening a system for interaction faults prior to its release, it provides little information to assist in the correction of such faults. Consequently tests to reveal the location of interaction faults are of interest. The problem of nonadaptive location of interaction faults is formalized under the hypothesis that the system contains (at most) some number d of faults, each involving (at most) some number t of interacting factors. Restrictions on the number and size of the putative faults lead to numerous variants of the basic problem. The relationships between this class of problems and interaction testing using covering arrays to indicate the presence of faults, designed experiments to measure and model faults, and combinatorial group testing to locate faults in a more general testing scenario, are all examined. While each has some definite similarities with the fault location problems for componentbased systems, each has some striking differences as well. In this paper, we formulate the combinatorial problems for locating and detecting arrays to undertake interaction fault location. Necessary conditions for existence are established, and using a close connection to covering arrays, asymptotic bounds on the size of minimal locating and detecting arrays are established.

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“…The SAT solver is a program for checking logical constraints [19], [22], [23]. The solver checks validity and consistency.…”

Section: Evaluate Logical Formulasmentioning

confidence: 99%

Detecting Logical Inconsistencies by Clustering Technique in Natural Language Requirements

Masuda

Matsuodani²,

Tsuda

2016

IEICE Trans. Inf. & Syst.

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SUMMARYIn the early phases of the system development process, stakeholders exchange ideas and describe requirements in natural language. Requirements described in natural language tend to be vague and include logical inconsistencies, whereas logical consistency is the key to raising the quality and lowering the cost of system development. Hence, it is important to find logical inconsistencies in the whole requirements at this early stage. In verification and validation of the requirements, there are techniques to derive logical formulas from natural language requirements and evaluate their inconsistencies automatically. Users manually chunk the requirements by paragraphs. However, paragraphs do not always represent logical chunks. There can be only one logical chunk over some paragraphs on the other hand some logical chunks in one paragraph. In this paper, we present a practical approach to detecting logical inconsistencies by clustering technique in natural language requirements. Software requirements specifications (SRSs) are the target document type. We use k-means clustering to cluster chunks of requirements and develop semantic role labeling rules to derive "conditions" and "actions" as semantic roles from the requirements by using natural language processing. We also construct an abstraction grammar to transform the conditions and actions into logical formulas. By evaluating the logical formulas with input data patterns, we can find logical inconsistencies. We implemented our approach and conducted experiments on three case studies of requirements written in natural English. The results indicate that our approach can find logical inconsistencies.

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Constraint-Based Approaches to the Covering Test Problem

Cited by 28 publications

References 10 publications

Prioritized interaction testing for pair-wise coverage with seeding and constraints

Prioritized interaction testing for pair-wise coverage with seeding and constraints

Locating and detecting arrays for interaction faults

Detecting Logical Inconsistencies by Clustering Technique in Natural Language Requirements

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