On allocation of resources during testing phase incorporating flexible software reliability growth model with testing effort under dynamic environment

Kumar, Vijay; Kapur, P. K.; Taneja, Naman; Sahni, Ramita

doi:10.1504/ijor.2017.087829

Cited by 12 publications

(10 citation statements)

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“…Das et al 13 presented a continuous time software reliability model for error generation in each fault based on the nonhomogeneous Poisson process (NHPP) assumption for periodic debugging, in which errors are rectified at prespecified debugging periods rather than at the time of detection. Kumar et al 14 developed an optimal model for allocating effort optimally between correction and detection processes during software development life cycle testing phase. 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.…”

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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“…Optimal control theory and GA have been applied to find the optimum value of testing efforts. Kumar et al (2017) used Ohba (1984) to develop a resource allocation plan and used GA to attain the optimum value of testing efforts. developed a resource allocation model by considering fault detection and correction process to be independent simultaneous activities with different budgetary constraints.…”

Section: Review Of Literaturementioning

confidence: 99%

“…There are many optimization methods for finding a suitable optimal solution to such problems, but GA aids to look for the best solution among a number of possible solutions. GA is one of the best problem-solving approaches among other metaheuristics when large-scale problems are considered (Soleimani et al, 2013;Kumar et al, 2014Kumar et al, , 2017Kumar et al, , 2019. Researchers (Pasandideh et al, 2011;Yang et al, 2012) applied GA to inventory management-related problems successfully.…”

Section: Testing Resource Allocation Modelingmentioning

confidence: 99%

Dynamic testing resource allocation modeling for multi-release software using optimal control theory and genetic algorithm

Kumar

Sahni

2020

IJQRM

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PurposeThe use of software is overpowering our modern society. Advancement in technology is directly proportional to an increase in user demand which further leads to an increase in the burden on software firms to develop high-quality and reliable software. To meet the demands, software firms need to upgrade existing versions. The upgrade process of software may lead to additional faults in successive versions of the software. The faults that remain undetected in the previous version are passed on to the new release. As this process is complicated and time-consuming, it is important for firms to allocate resources optimally during the testing phase of software development life cycle (SDLC). Resource allocation task becomes more challenging when the testing is carried out in a dynamic nature.Design/methodology/approachThe model presented in this paper explains the methodology to estimate the testing efforts in a dynamic environment with the assumption that debugging cost corresponding to each release follows learning curve phenomenon. We have used optimal control theoretic approach to find the optimal policies and genetic algorithm to estimate the testing effort. Further, numerical illustration has been given to validate the applicability of the proposed model using a real-life software failure data set.FindingsThe paper yields several substantive insights for software managers. The study shows that estimated testing efforts as well as the faults detected for both the releases are closer to the real data set.Originality /valueWe have proposed a dynamic resource allocation model for multirelease of software with the objective to minimize the total testing cost using the flexible software reliability growth model (SRGM).

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“…In order to obtain the dynamic promotional effort policies, we use Pontryagin's Maximum Principle (Seierstad and Sydsaeter, 1987;Kumar et al, 2014;Kumar and Sahni, 2016;Kumar et al, 2017;Kumar et al, 2019) to solve the optimal control problem expressed in Eq. (6).…”

Section: The Optimal Dynamic Promotional Strategymentioning

confidence: 99%

Optimal Promotional Effort Policy in Innovation Diffusion Model Incorporating Dynamic Market Size in Segment Specific Market

Mehta¹,

Chaudhary²,

Kumar³

2020

Int J Math, Eng, Manag Sci

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Due the increasing globalization of market and diverse consumer groups, market segmentation becomes an ever more important concept in current market scenario. After market segmentation, firms use the available promotional strategies to target market. In this paper, we present a model of determining the optimal dynamic promotional policies for a product in segmented market incorporating dynamic market size, where sales is assumed to be evolved through mass and differentiated promotion. Mass promotional effort is allowed in whole market with a fixed spectrum for each segment while differentiated promotion is targeted to each segment independently. The optimal promotional effort policy for each segment is obtained by applying maximum principle. Numerical illustrations are provided to show the effectiveness of the proposed method and solution procedure by discretizing the optimal control model. Furthermore, sensitivity analysis of the discount rate parameter is carried out and presented.

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On allocation of resources during testing phase incorporating flexible software reliability growth model with testing effort under dynamic environment

Cited by 12 publications

References 0 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)

Dynamic testing resource allocation modeling for multi-release software using optimal control theory and genetic algorithm

Optimal Promotional Effort Policy in Innovation Diffusion Model Incorporating Dynamic Market Size in Segment Specific Market

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