Constraint Models for the Covering Test Problem

Hnich, Brahim; Prestwich, Steven; Selensky, Evgeny; Smith, Barbara M.

doi:10.1007/s10601-006-7094-9

Cited by 107 publications

(69 citation statements)

References 22 publications

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“…These tools accept a test model and constraints as input data and generate the set of test cases. In addition, in [9], we showed how constraints can be handled when constraint programming is used to construct test cases [10], [11]. Table 3 represents a set of test cases for the cross-browser testing constructed with considering constraints.…”

Section: Pair-wise Testing and Constraintsmentioning

confidence: 99%

A Search-Based Constraint Elicitation in Test Design

Nakagawa

Tsuchiya

2016

IEICE Trans. Inf. & Syst.

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SUMMARYPair-wise testing is an effective test planning technique for finding interaction faults using a small set of test cases. Constraint elicitation is an important process in the pair-wise testing design since constraints determine the test space; however, the constraint elicitation process has not been well studied. It usually requires manual capturing and precise definition of constraints. In this paper, we propose a constraint elicitation process that helps combinatorial test design. Our elicitation process consists of two steps: parameter combination identification and value pair determination. We conduct experiments on some test models, and demonstrate that some extracted rules match constraints and others helps to define constraints.

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Section: Pair-wise Testing and Constraintsmentioning

confidence: 99%

A Search-Based Constraint Elicitation in Test Design

Nakagawa

Tsuchiya

2016

IEICE Trans. Inf. & Syst.

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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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“…Prestwich, E. Selensky, B.M. Smith [9] have developed models for constraint programming for finding an optimal covering array. It is shown that the compound variables that represent tuples of variables in the original model, allow the constraints of the problem to be represented more easily, propagating better.…”

Section: Review Analysismentioning

confidence: 99%

A Review of Constraint Programming

Dabas¹,

Cooner²

2014

IJCATR

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A constraint is defined as a logical relation among several unknown quantities or variables, each taking a value in a given domain. Constraint Programming (CP) is an emergent field in operations research. Constraint programming is based on feasibility which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the constraints and variables domain rather than the objective functions. While defining a set of constraints, this may seem a simple way to model a real-world problem but finding a good model that works well with a chosen solver is not that easy. A model could be very hard to solve if it is poorly chosen.

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Constraint Models for the Covering Test Problem

Cited by 107 publications

References 22 publications

A Search-Based Constraint Elicitation in Test Design

A Search-Based Constraint Elicitation in Test Design

Detecting Logical Inconsistencies by Clustering Technique in Natural Language Requirements

A Review of Constraint Programming

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