Two-Dimensional Multi-Release Software Reliability Modeling for Fault Detection and Fault Correction Processes

Kumar, Vijay; Mathur, Paridhi; Sahni, Ramita; Anand, M.

doi:10.1142/s0218539316400027

Cited by 22 publications

(6 citation statements)

References 13 publications

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“…Authors proposed a resource allocation plan to reduce total testing costs while maintaining reliability criteria and utilizing a flexible software reliability growth model that considers testing effort in a dynamic environment. Kumar et al 15 proposed a model to segregate the process of fault removal into two stages including fault removal and fault detection process. The authors used a Cobb-Douglas production function for the software multirelease problem to consider the joint effect of resource restriction and release pressure.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel CRITIC‐TOPSIS approach for optimal selection of software reliability growth model (SRGM)

Saxena

Kumar

Ram

2022

Quality & Reliability Eng

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Various Software Reliability Growth Models (SRGMs) have been developed over last five decades to estimate the failure rate, software reliability and remaining number of faults in the software during testing phase of software development life cycle. There are numerous SRGMs available in the research literature and in the presence of numerous SRGM, appropriate selection is a staggering task for the software testing professionals. In the software reliability area, researchers have always been interested to select the optimal SRGM to use in a particular situation. In this study, we have developed an integrated technique based on Criteria Importance Through Inter Criteria Correlation (CRITIC) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to select the optimal SRGM. The weights of criteria are obtained by the CRITIC method and SRGMs are ranked by order using TOPSIS method. The working of proposed integrated CRITIC‐TOPSIS approach is illustrated on the two real‐time failure data sets. Furthermore, the methodology validation idea supports the current study by comparing the results obtained with the previous multi‐criteria decision making (MCDM) methodologies namely Additive Ratio Assessment (ARAS). The study revealed that the ranking of SRGMs can be solved effectively using the integration of CRITIC and TOPSIS. The result of this study plays a significant role to take decisions and to evaluate the feasibility of SRGM.

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Section: Literature Reviewmentioning

confidence: 99%

A novel CRITIC‐TOPSIS approach for optimal selection of software reliability growth model (SRGM)

Saxena

Kumar

Ram

2022

Quality & Reliability Eng

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“… Bias: It is the ratio of summation of the difference between predicted and the actual data to that of number of observations, ie,

italicBias = \frac{\sum_{j = 1}^{z} ((), m ((), t_{j}) - x_{j})}{z} .

Sum of squared errors (SSE): SSE is defined mathematically as

SSE = \sum_{j = i}^{z} {(x_{j} - m (t_{j}))}^{2} .

Akaike information criterion (AIC): It is defined mathematically as

AIC = 2 * u + z * 1 normaln ((), SSE) .

AIC counts both the statistical goodness of fit and the unknown parameters of the model. Variance: The standard deviation of the bias is known as variance, ie,

Variance = \sqrt{\frac{1}{z - 1} \sum_{j = 1}^{z} {(x_{j} - m (t_{j}) - Bias)}^{2}} .

Root mean‐square prediction error (RMSPE): With which closeness a model predicts the observation is measured by RMSPE given as

RMSPE = \sqrt{{Variance}^{2} + {Bias}^{2}} .

Coefficient of multiple determination (R 2 ): It measures how successful the goodness of fit is in describing the variation of data and if two variables have a meaningful relation . The range of its value lies from 0 to 1.…”

Section: Parameter Estimation and Comparison Criteriamentioning

confidence: 99%

“…It measures how successful the goodness of fit is in describing the variation of data 38 and if two variables have a meaningful relation. 32 The range of its value lies from 0 to 1. It is mathematically defined as…”

Section: Variancementioning

confidence: 99%

“…Then, in Singh et al, a unified multi‐upgraded model for two‐stage detection and correction is proposed where they have incorporated two types of imperfect debugging. The model has been extended to incorporate testing effort along with imperfect debugging in Kapur et al In previous studies, multi‐release framework/models have been forwarded wherein two stage fault detection and correction processes have been considered. More of latest work in the field of multi‐release have been put forth by researchers in .…”

Section: Literature Surveymentioning

confidence: 99%

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Generalized multi‐release modelling of software reliability growth models from the perspective of two types of imperfect debugging and change point

Saraf

Iqbal

2019

Quality & Reliability Eng

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Owing to release of software in multiple releases, code changes take place in software. Because of this added complexity in software, the testing team may be unable to correct the fault upon detection, leaving the actual fault to reside in the software, termed as imperfect debugging or there may be replacement of original fault by other fault, leading to error generation. Many other factors exist that affect the testing phase of software like strategies of testing, test cases, skill, efficiency, and learning of testing team. All these factors cannot be kept stable during the whole process of testing. They may change at any time moment causing the background processes to experience change, which is known as change‐point. Keeping all these critical testing environment factors under consideration, a new software reliability growth model has been proposed, which is derived from an non homogenous Poisson process (NHPP)based unified scheme for multi‐release two‐stage fault detection/observation and correction/removal software reliability models. The developed model is numerically illustrated on tandem data set for four releases.

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“…Software developers across the globe extensively employ software reliability growth models (SRGMs) in quantitatively modeling the fault removal phenomenon of various software systems by considering testing time, effort, coverage, etc. Several such models (Lyu, 1996; Kapur et al , 2011; Kumar, Mathur, Sahni and Anand, 2016; Kumar, Sahni and Shrivastava, 2016; Pham, 2000, 2007) under a varying set of assumptions have been laid out by both researchers and practitioners to model the number of faults debugged during testing, operational and post-operational phases of software development. SRGMs are generally employed in the later stages of software testing to gain major insights into assessing software reliability.…”

Section: Introductionmentioning

confidence: 99%

Generalized software release and testing stop time policy

Shrivastava

Sachdeva²

2019

IJQRM

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Purpose Almost everything around us is the output of software-driven machines or working with software. Software firms are working hard to meet the user’s requirements. But developing a fault-free software is not possible. Also due to market competition, firms do not want to delay their software release. But early release software comes with the problem of user reporting more failures during operations due to more number of faults lying in it. To overcome the above situation, software firms these days are releasing software with an adequate amount of testing instead of delaying the release to develop reliable software and releasing software patches post release to make the software more reliable. The paper aims to discuss these issues. Design/methodology/approach The authors have developed a generalized framework by assuming that testing continues beyond software release to determine the time to release and stop testing of software. As the testing team is always not skilled, hence, the rate of detection correction of faults during testing may change over time. Also, they may commit an error during software development, hence increasing the number of faults. Therefore, the authors have to consider these two factors as well in our proposed model. Further, the authors have done sensitivity analysis based on the cost-modeling parameters to check and analyze their impact on the software testing and release policy. Findings From the proposed model, the authors found that it is better to release early and continue testing in the post-release phase. By using this model, firms can get the benefits of early release, and at the same time, users get the benefit of post-release software reliability assurance. Originality/value The authors are proposing a generalized model for software scheduling.

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Two-Dimensional Multi-Release Software Reliability Modeling for Fault Detection and Fault Correction Processes

Cited by 22 publications

References 13 publications

A novel CRITIC‐TOPSIS approach for optimal selection of software reliability growth model (SRGM)

A novel CRITIC‐TOPSIS approach for optimal selection of software reliability growth model (SRGM)

Generalized multi‐release modelling of software reliability growth models from the perspective of two types of imperfect debugging and change point

Generalized software release and testing stop time policy

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