A theoretical and empirical study of EFSM dependence

Androutsopoulos, Kelly; Gold, Nicolas; Harman, Mark; Li, Zheng; Tratt, Laurence

doi:10.1109/icsm.2009.5306309

Cited by 30 publications

(29 citation statements)

References 28 publications

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“…Formal modeling techniques provide the basis to validate the system design [39,40], to generate system level test suites [38,41,40,37,14,31,32], to simulate the system behavior by executing the model [42,18] and to determine properties of the system. Although modeling techniques can help eliminate complexity of many of today's systems, the resulting models are difficult to understand, analyze, and modify [28,29], especially for large and complex systems [27,47] where there are a large number of states and transitions. During software maintenance developers are frequently interested in a partial system model related to an element of interest, e.g., a requirement, a transition, or a feature [28,30,31,32,33,34], that they need to analyze, understand, or modify, e.g., slices of models [34,35].…”

Section: State-based Modelingmentioning

confidence: 99%

“…There has been a significant amount of research that uses system model for several applications such as: managing requirements changes [48], regression testing [3,4,7,26], model slicing [34,46,47], and test prioritization and reduction [36,50].…”

Section: Related Researchmentioning

confidence: 99%

“…Due to the unavailability of system models for real world commercial software, we used EFSM system models that are in the public domain for the empirical study. These EFSM models are: an ATM model [10,11,53], a cruise control model [45], a fuel pump model [46], the Transfer Control Protocol-communication dialer (TCP) [44], Print-Token [24], and the Integrated Service Digital Network (ISDN) protocol [47]. The sizes of models range from 5 to 20 states and 20 to 89 transitions.…”

Section: Empirical Studymentioning

confidence: 99%

“…System modeling reduces ambiguity, misunderstanding, and misinterpretation of system specifications. However, modern systems tend to be very large and complex [27,47] and, as a result, they are hard to understand, difficult to modify and debug [28,29,27,47].…”

Section: Introductionmentioning

confidence: 99%

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Identifying the Effect of Model Modifications in State-Based Models and Systems

Tahat¹,

Almasri

2012

Journal of Advanced Computer Science &Amp; Technology

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System modeling is a widely used technique to model state-based systems. System models are often used during the development of a software system, e.g., in partial code generation and in test generation. Several modeling languages have been developed to model state-based software systems, e.g., EFSM, SDL, and State Charts. Although state-based modeling is very useful, system models are usually large and complex, and they are frequently modified because of specification changes. Identifying the effect of these changes on the model and consequently on the underlying system is usually challenging and time-consuming. In this paper, we present an approach to automatically identify the effect of modifications made to the model. The goal is to identify those parts of the model that may exhibit different behaviors because of the modification. These are usually critical parts of the system that should be carefully tested. In this approach, the difference between the original model and the modified model is identified, and then the affected parts of the model are computed based on model dependence analysis. An empirical study on different EFSM models is performed in order to identify the affected parts of the model after a modification. The results of the study suggest that our approach could considerably reduce the amount of time and efforts spent to validate the model after a modification.

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Section: State-based Modelingmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Section: Empirical Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying the Effect of Model Modifications in State-Based Models and Systems

Tahat¹,

Almasri

2012

Journal of Advanced Computer Science &Amp; Technology

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“…EFSMs can be conceptualised as conventional Finite State-Machines that operate on an underlying memory, where transitions are annotated by guards on the memory. EFSMs have been used as a basis for testing [23], and dependence analysis [3]. They can be trivially converted to other notations such as Abstract State Machines [7], and X-Machines [24], which have been used extensively for tasks such as model-checking and the specification of agent-based systems.…”

Section: Introductionmentioning

confidence: 99%

Inferring extended finite state machine models from software executions

2015

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The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering techniques focus either on control-specific behaviour (e.g., in the form of Finite State Machines), or data-specific behaviour (e.g., as pre / post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these techniques can be non-deterministic, the inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presentsWe thank Ivo Krka at the University of Southern California for providing us with some of the traces for the evaluation. Ramsay Taylor and John Derrick are supported by the EU FP7 PROWESS projects. Neil Walkinshaw is grateful for the support provided by the UK Ministry of Defence (MOD) through the BATS and HASTE projects. Information contained in this document should not be interpreted as representing the views of the MOD, nor should it be assumed that it reflects any current or future MOD policy. The information cannot supersede any statutory or contractual requirements or liabilities and is offered without prejudice or commitment.

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Regression test suite selection using dependence analysis

Ural

Yenigün

2012

J Software Evolu Process

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Dependence analysis on an Extended Finite State Machine representation of the requirements of a system under test identifies various types of control and data dependencies between transitions caused by a set of modifications on the requirements. These particular types of dependencies capture the effects of the modifications, that is, their direct effects on the changed parts of the system and their side effects on the unchanged parts of the system. Recent work on model-based regression testing shows that dependencies capturing direct effects and side effects of the changes made on the requirements can be used for regression test suite (RTS) reduction (reducing the size of a given test suite by eliminating redundancies), for RTS prioritization (ordering test cases in a given test suite for early fault detection), or for RTS generation (designing a test suite covering the identified dependencies). This paper proposes an additional use of such dependencies, namely, RTS selection, which is the process of selecting a subset of a given test suite to form an RTS by considering the coverage of dependencies related to the effects of the modifications. The dependencies marked during this process as uncovered provide a basis for augmenting an (incomplete) RTS with test cases covering uncovered dependencies.

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A theoretical and empirical study of EFSM dependence

Cited by 30 publications

References 28 publications

Identifying the Effect of Model Modifications in State-Based Models and Systems

Identifying the Effect of Model Modifications in State-Based Models and Systems

Inferring extended finite state machine models from software executions

Regression test suite selection using dependence analysis

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