An integrated search-based approach for automatic testing from extended finite state machine (EFSM) models

Kalaji, Abdul Salam; Hierons, Robert M.; Swift, Stephen

doi:10.1016/j.infsof.2011.06.004

Cited by 63 publications

(79 citation statements)

References 54 publications

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“…Kalaji [8] presented a novel fitness metric to estimate the feasibility of a path, which is evaluated by being used in a genetic algorithm to guide the search towards TPs that are likely to be FTPs. Lefticaru [16] used independent component-based fitness (ICF) for path data generation from EFSMs.…”

Section: Figure 6 Reduce Rate Of Explored Nodesmentioning

confidence: 99%

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A Test Generation Method for EFSM-based Protocols Using the Transitions Feasibility Estimation

Shu¹,

Yin³

et al. 2016

IJCA

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Section: Figure 6 Reduce Rate Of Explored Nodesmentioning

confidence: 99%

“…In recent years, genetic algorithm (GA) has been used to generate executable test sequences [8]. Its working procedure is divided into two stages: the first phase generates transition paths (TPs) to meet the specified coverage criterion; the second phase finds input data to fire the TPs produced in the first phase.…”

Section: Introductionmentioning

confidence: 99%

A Test Generation Method for EFSM-based Protocols Using the Transitions Feasibility Estimation

Shu¹,

Yin³

et al. 2016

IJCA

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“…We generate the initial population randomly. To make every randomly generated traversal of the graph representing an EFSM valid, we use an encoding similar to the one in [8].…”

Section: Chromosomes and Genesmentioning

confidence: 99%

“…This info gene (test path) in a chrom for the chromosome as the s a chromosome c i , made of where feasibility(g j ) is comp Our intuition, as well as possible to increase fault de larities between pairs of te (chromosome). Different si the measures [2], which is n length chromosomes), is Labiche randomly selected gene is removed from the chromosom a randomly selected gene is replaced by a new random ving a transition from a test path (gene), i.e., a random th) is mutated by removing a randomly selected transit a transition to a test path (gene), i.e., a randomly selec mly adding a transition to it; (6) Changing a transition o domly selected gene is mutated by randomly replacing ew one; (7) Exchanging randomly selected transitions est paths (genes) of the same test suite (chromosome m [8]. Each time a test path is modified, we ensure, simi n of a entire path, that the resulting path is a valid trave he EFSM.…”

Section: Fitness Functionsmentioning

confidence: 99%

A Multi-objective Genetic Algorithm for Generating Test Suites from Extended Finite State Machines

Asoudeh

Labiche

2013

Search Based Software Engineering

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Abstract. We propose a test suite generation technique from extended finite state machines based on a genetic algorithm that fulfills multiple (conflicting) objectives. We aim at maximizing coverage and feasibility of a set of test cases while minimizing similarity between these cases and minimizing overall cost. IntroductionExtended Finite State Machines (EFSMs) are widely used in system modeling and a great volume of research exists in the area of state-based testing. Due to the presence of guard conditions and action in the EFSM, not all the paths in an EFSM are feasible [1]. Another challenge is decreasing cost and increasing effectiveness of generated test suites to make them scalable to large industrial applications. This can be achieved for instance through increasing test case diversity [2]. Search-Based Software Engineering (SBSE) has emerged in the field of software engineering since the nature of software engineering problems makes them perfect for the application of meta-heuristic search techniques. Search-based methods can be very helpful in solving problems in which there are tradeoffs between different constraints. They can provide solutions when optima are either theoretically impossible or practically infeasible [3]. SBSE has proved to be very successful and there has been a significant increase in the interest and research contributions in this field in the past five years [5]. One of the first applications of SSBSE has been in software testing and dates back to 1976 [6]. In search-based software testing (SBST) a meta-heuristic search method is used to automate or partially automate a testing task [4].In this paper, we propose an SBST technique for test suite generation from EFSMs. We use a multi-objective genetic algorithm to search for an adequate test suite that is most likely feasible, has minimum cost, and has low similarity between its test paths. Related WorkOthers before us have investigated the application of SBST to state-based testing. Hemmati et al. proposed a similarity based test case selection technique that uses search based techniques to maximize test case diversity [2]. In a series of case studies they used different similarity measures as fitness function and considered different

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“…There has been some work on using artificial intelligence techniques, like genetic algorithms (GAs) and other meta-heuristic algorithms, to automate software testing [23,38,32,16,47,31]. Our group has lately been very active in this area of research [24,40,9,25,41,42]. In this paper we concentrate on the use of GAs to generate test sequences in complex timed systems.…”

Section: Introductionmentioning

confidence: 99%

Using genetic algorithms to generate test sequences for complex timed systems

et al. 2012

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The generation of test data for state based specifications is a computationally expensive process. This problem is magnified if we consider that time constraints have to be taken into account to govern the transitions of the studied system. The main goal of this paper is to introduce a complete methodology, supported by tools, that addresses this issue by representing the test data generation problem as an optimisation problem. We use heuristics to generate test cases. In order to assess the suitability of our approach we consider two different case studies: a communication protocol and the scientific application BIPS3D. We give details concerning how the test case generation problem can be presented as a search problem and automated. Genetic algorithms (GAs) and random search are used to generate test data and evaluate the approach. GAs outperform random search and seem to scale well as the problem size increases. It is worth to mention that we use a very simple fitness function that can be easily adapted to be used with other evolutionary search techniques.

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An integrated search-based approach for automatic testing from extended finite state machine (EFSM) models

Cited by 63 publications

References 54 publications

A Test Generation Method for EFSM-based Protocols Using the Transitions Feasibility Estimation

A Test Generation Method for EFSM-based Protocols Using the Transitions Feasibility Estimation

A Multi-objective Genetic Algorithm for Generating Test Suites from Extended Finite State Machines

Using genetic algorithms to generate test sequences for complex timed systems

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