Validating Software Reliability Early through Statistical Model Checking

Kim, Youngjoo; Choi, Okjoo; Kim, Moonzoo; Baik, Jongmoon; Kim, Tai-Hyo

doi:10.1109/ms.2013.24

Cited by 18 publications

(17 citation statements)

References 2 publications

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“…This technique provides a number of advantages in comparison to traditional model checking techniques. First (and perhaps the most important one), this technique does not suffer from the state space explosion problem since it does not analyze the internal logic of the system under verification, neither requires the entire representation of the state space, thus making it a promising approach for verifying complex large-scale and critical software systems [12]. Second, SMC requires only the system be able to be simulated, so that it can be applied to larger classes of systems, including black-box and infinite-state systems.…”

Section: Statistical Model Checkingmentioning

confidence: 99%

“…SMC is a probabilistic, simulation-based technique intended to verify, at a given confidence level, if a certain property is satisfied during the execution of a system [13]. Unlike conventional formal verification techniques such as model checking, SMC does not analyze the internal logic of the target system, thereby not suffering from the state space explosion problem [12]. Therefore, an architect wishing to assess the correctness of an architecture has to build an executable model of the system.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Model Checking of Dynamic Software Architectures

Cavalcante

Quilbeuf

Traonouez

et al. 2016

Software Architecture

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Abstract. The critical nature of many complex software-intensive systems calls for formal, rigorous architecture descriptions as means of supporting automated verification and enforcement of architectural properties and constraints. Model checking has been one of the most used techniques to automatically analyze software architectures with respect to the satisfaction of architectural properties. However, such a technique leads to an exhaustive exploration of all possible states of the system under verification, a problem that becomes more severe when verifying dynamic software systems due to their typical non-deterministic runtime behavior and unpredictable operation conditions. To tackle these issues, we propose using statistical model checking (SMC) to support the analysis of dynamic software architectures while aiming at reducing effort, computational resources, and time required for this task. In this paper, we introduce a novel notation to formally express architectural properties as well as an SMC-based toolchain for verifying dynamic software architectures described in π-ADL, a formal architecture description language. We use a flood monitoring system to show how to express relevant properties to be verified, as well as we report the results of some computational experiments performed to assess the efficiency of our approach.

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Section: Statistical Model Checkingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Model Checking of Dynamic Software Architectures

Cavalcante

Quilbeuf

Traonouez

et al. 2016

Software Architecture

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“…First, SMC does not suffer from the exponential growth of the state space (the so-called state space explosion problem) as it does not build the entire representation of the state space, thus making it a promising approach for verifying complex large-scale and critical software systems [15]. Second, SMC can be applied as soon as a simulator of the system to verify is available.…”

Section: Statistical Model Checking: An Overviewmentioning

confidence: 99%

A Logic for the Statistical Model Checking of Dynamic Software Architectures

Quilbeuf

Cavalcante

Traonouez

et al. 2016

Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques

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Abstract. Dynamic software architectures emerge when addressing important features of contemporary systems, which often operate in dynamic environments subjected to change. Such systems are designed to be reconfigured over time while maintaining important properties, e.g., availability, correctness, etc. Verifying that reconfiguration operations make the system to meet the desired properties remains a major challenge. First, the verification process itself becomes often difficult when using exhaustive formal methods (such as model checking) due to the potentially infinite state space. Second, it is necessary to express the properties to be verified using some notation able to cope with the dynamic nature of these systems. Aiming at tackling these issues, we introduce DynBLTL, a new logic tailored to express both structural and behavioral properties in dynamic software architectures. Furthermore, we propose using statistical model checking (SMC) to support an efficient analysis of these properties by evaluating the probability of meeting them through a number of simulations. In this paper, we describe the main features of DynBLTL and how it was implemented as a plug-in for PLASMA, a statistical model checker.

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“…Incidentally very few works are available in this area (Jiang et al (2007); Kim et al (2013); Mohan et al (2011);Yadav et al 2012Yadav et al , 2014Mohanta et al 2010;Cheung et al 2008;Pandey andGoyal 2009, 2010;Kumar and Misra 2008;Smidts et al 1998;Tripathi and Mall 2005;Fenton et al 2008;Xie et al 1999) compared to the SRGMs based on failure data gathered during testing phase. Among these models some are based on fuzzy logic (Yadav et al 2012(Yadav et al , 2014Pandey andGoyal 2009, 2010;Kumar and Misra 2008).…”

Section: Introductionmentioning

confidence: 99%

A new fuzzy rule based algorithm for estimating software faults in early phase of development

Chatterjee

Maji

2015

Soft Comput

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Estimation of reliability and the number of faults present in software in its early development phase, i.e., requirement analysis or design phase is very beneficial for developing reliable software with optimal cost. Software reliability prediction in early phase of development is highly desirable to the stake holders, software developers, managers and end users. Since, the failure data are unavailable in early phase of software development, different reliability relevant software metrics and similar project data are used to develop models for early software fault prediction. The proposed model uses the linguistic values of software metrics in fuzzy inference system to predict the total number of faults present in software in its requirement analysis phase. Considering specific target reliability, weightage of each input software metrics and size of software, an algorithm has been proposed here for developing general fuzzy rule base. For model validation of the proposed model, 20 real software project data have been used here. The linguistic values from four software metrics related to requirement analysis phase have been considered as model inputs. The performance of the proposed model has been compared with two existing early software fault prediction models.

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Validating Software Reliability Early through Statistical Model Checking

Cited by 18 publications

References 2 publications

Statistical Model Checking of Dynamic Software Architectures

Statistical Model Checking of Dynamic Software Architectures

A Logic for the Statistical Model Checking of Dynamic Software Architectures

A new fuzzy rule based algorithm for estimating software faults in early phase of development

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