An effective approach to estimating the parameters of software reliability growth models using a real-valued genetic algorithm

Kim, Taehyoun; Lee, Kwang-Kyu; Baik, Jongmoon

doi:10.1016/j.jss.2015.01.001

Cited by 55 publications

(22 citation statements)

References 17 publications

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“…¶ The popular maximum likelihood estimate is adopted here to estimate model parameters. Other approaches are feasible, including the EM algorithm in Okamura et al [42] and the genetic algorithm in Kima et al [45], and could replace MLE in DWA-SRGM. ‖ Note that to keep the treatment simple, not all combinations of detection and correction models are considered, but only the combinations of correction models with the selected detection model.…”

Section: Step 2: Extension Of the Srgm Fitting Fault Correction Datamentioning

confidence: 99%

Debugging‐workflow‐aware software reliability growth analysis

Cinque

Cotroneo

Pecchia

et al. 2017

Software Testing Verif & Rel

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Software reliability growth models support the prediction/assessment of product quality, release time, and testing/debugging cost. Several software reliability growth model exten-\ud sions take into account the bug correction process. However, their estimates may be significantly inaccurate when debugging fails to fully fit modelling assumptions. This paper proposes debugging-workflow-aware software reliability growth method (DWA-SRGM), a method for reliability growth analysis leveraging the debugging data usually managed by companies in bug tracking systems. On the basis of a characterization of the debugging workflow within the soft- ware project under consideration (in terms of bug features and treatment phases), DWA-SRGM pinpoints the factors impacting the estimates and to spot bottlenecks, thus supporting process improvement decisions. Two industrial case studies are presented, a customer relationship man- agement system and an enterprise resource planning system, whose defects span a period of about 17 and 13 months, respectively. DWA-SRGM revealed effective to obtain more realistic estimates and to capitalize on the awareness of critical factors for improving debugging

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Section: Step 2: Extension Of the Srgm Fitting Fault Correction Datamentioning

confidence: 99%

Debugging‐workflow‐aware software reliability growth analysis

Cinque

Cotroneo

Pecchia

et al. 2017

Software Testing Verif & Rel

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“…In the thesis entitled "Accurate Software Reliability Estimation", Jason Allen Denton [10] examines the impact of the parameter estimation technique on model accuracy and claims that the maximum likelihood method provides estimators which are more reliable than the least squares method. Taehyoun et al [11] proposed an effective approach to estimate the parameters of software reliability growth model using a real valued genetic algorithm and proved that RGA can be a promising solution to effectively managing software quality through the accurate reliability estimates than using method of maximum likelihood estimation or the method of least squares. Weiwen et al [12] presented a Bayesian model updating approach (BMUA) for life cycle reliability assessment of new products.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Software Reliability for Littlewood Pareto Failure Time Model

Tantri¹,

Murulidhar²

2017

IJSEIA

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“…Due to insufficient failure data, these models fail to predict the reliability of safety critical systems. Therefore, the uncertainties involved in the model will not give accurate results in case of SCS.Kim et al 13 came up with a novel approach for the estimations of the parameters for the SRGM. The proposed methodology is validated using experiments performed on real data of 12 safety-critical control systems of nuclear power plants.…”

mentioning

confidence: 99%

“…However, to perform the reliability analysis, the throughput or rate of transitions must be known. Therefore, the uncertainties involved in the model will not give accurate results in case of SCS.Kim et al 13 came up with a novel approach for the estimations of the parameters for the SRGM. The authors proposed a methodology for finding the parameters using real-valued genetic algorithm instead of the approaches like…”

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confidence: 99%

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Software reliability analysis for safety‐critical and control systems

Kumar

Singh

Kumar

2019

Quality & Reliability Eng

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The transition from analog to digital safety-critical instrumentation and control (I&C) systems has introduced new challenges for software experts to deliver increased software reliability. Since the 1970s, researchers are continuing to propose software reliability models for reliability estimation of software. However, these approaches rely on the failure history for the assessment of reliability. Due to insufficient failure data, these models fail to predict the reliability of safety critical systems. This paper utilizes the Bayesian update methodology and proposes a framework for the reliability assessment of the safety-critical systems (SCSs). The proposed methodology is validated using experiments performed on real data of 12 safety-critical control systems of nuclear power plants.We believe that SRM for SCS must have some special desirable characteristics:1. There should be acceptable documentation of the method and its applications that can be understood and evaluated. 2. There should be a strong justification of the assumptions made. 3. It should be possible to consider the specific operating conditions of the software. 4. It should be able to address all the uncertainties for better accuracy. 5. There should be sufficiency of model parameters. 6. The models should have proper verification and validation ways.Several models do not have description of intended uses of the method and all assumptions. 6,7 In general, the existing SRM assumes unrealistic assumptions and use failure history to estimate the parameters of the models in order to calculate the reliability. As of our knowledge, we have found very few SRM which consider the specific operating conditions of the software. 8 Software reliability growth model (SRGM) estimates the software reliability with high accuracy, if there is sufficiency of failure data and therefore are not suitable to SCS.Software errors are generally due to unstated, ambiguous, and unrealistic requirements or because of design errors. From this, we believe that if it is possible to formulate any model that can accommodate errors in each phase of software development life cycle (SDLC) and also can propagate them in next phase, prior to the completion of software development, it will give more accurate reliability prediction results. A perfect designed software requirement and specification (SRS) not only depends on the requirements and analysis but also on the expert's opinion, the domain knowledge, and the experience of the SRS designing team, the resources available, and many other constraints. Taking these as the inputs for the requirements phase, a perfect SRS is designed. These inputs may be considered as "safety cases" or "safety arguments" as these are the collection of evidence responsible for the proper development of final product. Evidence for safety cases or safety arguments for each phase exists. Using these evidences, we can create BBN model for each phase of life cycle model and calculate the inferred probability using the safety argument values. A proper conditio...

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An effective approach to estimating the parameters of software reliability growth models using a real-valued genetic algorithm

Cited by 55 publications

References 17 publications

Debugging‐workflow‐aware software reliability growth analysis

Debugging‐workflow‐aware software reliability growth analysis

Estimation of Software Reliability for Littlewood Pareto Failure Time Model

Software reliability analysis for safety‐critical and control systems

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