Testing Time and Effort-Based Successive Release Modeling of a Software in the Presence of Imperfect Debugging

Kumar, Vijay; Kapur, P. K.; Sahni, Ramita; Shrivastava, Avinash K.

doi:10.1007/978-981-10-5577-5_33

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Mishra et al (2018) proposed a generalized framework for modelling multi-release of software considering single change point in each release. Kumar et al (2018) have incorporated imperfect debugging along with Cobb–Douglas production function to explain the role of testing time and effort used for multiple release problem of software. The undetected faults of previous release are included in the next release.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a hybrid software reliability growth model

Shrivastava

Sharma²

2021

IJQRM

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PurposeThe purpose of this paper is to develop a new software reliability growth model considering different fault distribution function before and after the change point.Design/methodology/approachIn this paper, the authors have developed a framework to incorporate change-point in developing a hybrid software reliability growth model by considering different distribution functions before and after the change point.FindingsNumerical illustration suggests that the proposed model gives better results in comparison to the existing models.Originality/valueThe existing literature on change point-based software reliability growth model assumes that the fault correction trend before and after the change is governed by the same distribution. This seems impractical as after the change in the testing environment, the trend of fault detection or correction may not follow the same trend; hence, the assumption of same distribution function may fail to predict the potential number of faults. The modelling framework assumes different distributions before and after change point in developing a software reliability growth model.

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Section: Literature Reviewmentioning

confidence: 99%

Developing a hybrid software reliability growth model

Shrivastava

Sharma²

2021

IJQRM

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“…Subsequently, many studies have also been developed by assimilating the impact of testing efforts on the debugging process (Huang and Kuo, 2002;Huang and Lyu, 2005;Kapur et al, 2008;Li et al, 2011;Kumar et al, 2018a). Testing resources is the most decisive factor to develop a realistic growth model as it directly influences the reliability of the software systems (Zhang et al, 2016).…”

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confidence: 99%

Software reliability prediction and release time management with coverage

Panwar

Kumar

Kapur

et al. 2021

IJQRM

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PurposeSoftware testing is needed to produce extremely reliable software products. A crucial decision problem that the software developer encounters is to ascertain when to terminate the testing process and when to release the software system in the market. With the growing need to deliver quality software, the critical assessment of reliability, cost of testing and release time strategy is requisite for project managers. This study seeks to examine the reliability of the software system by proposing a generalized testing coverage-based software reliability growth model (SRGM) that incorporates the effect of testing efforts and change point. Moreover, the strategic software time-to-market policy based on costreliability criteria is suggested.Design/methodology/approachThe fault detection process is modeled as a composite function of testing coverage, testing efforts and the continuation time of the testing process. Also, to assimilate factual scenarios, the current research exhibits the influence of software users refer as reporters in the fault detection process. Thus, this study models the reliability growth phenomenon by integrating the number of reporters and the number of instructions executed in the field environment. Besides, it is presumed that the managers release the software early to capture maximum market share and continue the testing process for an added period in the user environment. The multiattribute utility theory (MAUT) is applied to solve the optimization model with release time and testing termination time as two decision variables.FindingsThe practical applicability and performance of the proposed methodology are demonstrated through real-life software failure data. The findings of the empirical analysis have shown the superiority of the present study as compared to conventional approaches.Originality/valueThis study is the first attempt to assimilate testing coverage phenomenon in joint optimization of software time to market and testing duration.

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