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

Saraf, Iqra; Iqbal, Javaid

doi:10.1002/qre.2516

Cited by 25 publications

(9 citation statements)

References 54 publications

(89 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aggarwal et al 49 also proposed an SRGM that integrate time-variable FRF with imperfect debugging environment for multi-release software systems. Saraf and Iqbal 35 proposed unified scheme for multirelease two-stage FDP and FCP SRGMs.…”

Section: Model Namementioning

confidence: 99%

“…Many researchers work on multi-release model. 31,34,35 However, there is a risk in software upgradation because of software complexity, and the upgraded version may have new faults, which can lead to a software breakdown. 36 Therefore, the failure rate increases during up-gradation and gradually decreases before the next release because bugs are corrected in this period.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time

Pradhan

Kumar

Dhar

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

The fault reduction factor (FRF) is a significant parameter for controlling the software reliability growth. It is the ratio of net fault correction to the number of failures encountered. In literature, many factors affect the behaviour of FRF, namely fault dependency, debugging time-lag, human learning behaviour and imperfect debugging. Besides this, several distributions, for example, inflection S-shaped, Weibull and Exponentiated-Weibull, are used as FRF. However, these standard distributions are not flexible to describe the observed behaviour of FRFs. This paper proposes three different software reliability growth models (SRGMs), which incorporate a three-parameter generalized inflection S-shaped (GISS) distribution as FRF. To model realistic SRGMs, time lags between fault detection and fault correction processes are also incorporated. This study proposed two models for the single release, whereas the third model is designed for multi-release software. Moreover, the first model is in perfect debugging, while the rest of the two are in an imperfect debugging environment. The extensive experiments are conducted for the proposed models with six single release and one multi-release data-sets. The choice of GISS distribution as an FRF improves the software reliability evaluation in comparison with the existing systems in the literature. Finally, the development cost and optimal release time are calculated in a perfect debugging environment.

show abstract

Section: Model Namementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time

Pradhan

Kumar

Dhar

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…Chatterjee and Shukla 3 developed a unified approach to develop testing coverage‐based SRGM by incorporating fault detection probability, imperfect debugging, and change‐point. Saraf and Iqbal 15 developed a generalized framework to model fault prediction for multi version of software by incorporating imperfect debugging and change‐point.…”

Section: Literature Reviewmentioning

confidence: 99%

Change‐points‐based software scheduling

Shrivastava¹,

Kapur

2021

Quality & Reliability Eng

View full text Add to dashboard Cite

Software reliability literature consists of various change‐point‐based software reliability growth models and related release time problems. The primary assumption of the existing models is the existence of change‐point before software release time only. This does not look practical as the testing team becomes more proficient in detecting the faults due to their continuous involvement in software development by the software release time. Hence the fault detection rate in the pre‐ and postrelease phase is not the same. To capture this change in fault detection rate in the pre‐ and postrelease testing phase, we propose a new software reliability modeling framework by considering two change‐points during the software lifecycle; that is, there exists a change‐point before release time and release time as a change‐point. Further, in the last one‐decade software firms have changed their strategy of stop testing the software after release and continue to test even after release to remove the number of faults to provide better user experiences. This phenomenon attracted academicians to develop theoretical as well empirical study on postrelease testing and formulation of related release time problem. In this paper, we propose a software cost model to determine optimal release and testing stop time considering under the assumption of two change‐points as mentioned above. The proposed model is validated on real‐life data set.

show abstract

“…Thus, the next software version consists of leftover faults of the immediate previous release that is,

( {{\eta ^*}_{(i - 1)2} - M({t_{i - 1}})} )

and faults observed in the current release

{\eta _i}

that get created due to the inclusion of new features. Considering the assumption that the MVF for the next releases comprises of the faults of the existing release and the leftover faults from immediate previous release only, the equation for the next releases becomes 29–30 :

M_{i} (t) badbreak= ({, \begin{matrix} 0true \frac{η_{i} + (η_{false(i - 1 false) 2}^{*} - M (t_{i - 1})}{false(1 - δ_{i 1} false)} [1 - {(1 - false(F_{i 1} \otimes G_{i 1} false) false(t false))}^{ρ_{i 1}} false(1 goodbreak- δ_{i 1} false)]; & f o r t \leq λ_{i} \\ 0true \frac{η_{i} + (η_{false(i - 1 false) 2}^{*} - M (t_{i - 1})}{false(1 - δ_{i 2} false)} true[1 - {false(1 - false(F_{i 1} \otimes G_{i 1} false) false(λ_{i} false) false)}^{ρ_{i 1} (1 - δ_{i 1})} \end{matrix})

…”

Section: Software Reliability Modeling Framework For Multi‐release So...mentioning

confidence: 99%

Modelling reliability growth for multi‐version open source software considering varied testing and debugging factors

Saraf

Iqbal

Shrivastava

et al. 2021

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

Due to the continuous dependence of society on technology and the fast growth of Open Source Software (OSS), there is a need for the software industry to shift to multi‐release software development. To include the revisions in user demands and testing environment, factors that affect OSS reliability have to be considered. Here, we propose a quantitative method for assessing the reliability of multi‐release OSS by using Software Reliability Growth Models based on the Non‐Homogenous Poisson Process. Various factors like imperfect debugging, error generation, change‐point have been considered. The model has been estimated on Statistical Package for Social Sciences using three releases of the Apache dataset. It is concluded that the results obtained are improved than the existing ones.

show abstract

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

Cited by 25 publications

References 54 publications

Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time

Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time

Change‐points‐based software scheduling

Modelling reliability growth for multi‐version open source software considering varied testing and debugging factors

Contact Info

Product

Resources

About