Software Reliability Growth Models with Testing-Effort

Yamada, Shigeru; Ohtera, Hiroshi; Narihisa, Hiroyuki

doi:10.1109/tr.1986.4335332

Cited by 245 publications

(164 citation statements)

References 10 publications

Supporting

Mentioning

163

Contrasting

Order By: Relevance

“…Early researches related SRGM with respect to testing time [3,17]. However, incorporation of testing resources leads to development of more accurate SRGMs [4,5,9,26]. The reason being, the detection of faults are more intimately linked to the amount of resources expended [18,24] on testing as it includes-(a) Manpower, that takes into account  Testing group (malfunction recognition personnel).…”

Section: 'Bmentioning

confidence: 99%

A study of Optimal Testing Resource Allocation Problem for Modular Software with Change Point

Kaur

Aggarwal

Kedia³

2018

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-For evolving trustworthy software, engrossing on uncovering process of fault in software is central. Nevertheless, during testing, modifications in the testing routine, defect gravity or testing-skill maturity and working environment, there can be notable change in fault detection rate. When this sort of pattern is observed in testing time it is called change point. In this article, we inquire a resource distribution problem that optimally distributes software developing resources in such a way that the cost of development is curtailed to optimization. In this problem, for all modules the effect of chief circumstantial element of change-point is considered. The constraint of pulling off the desired reliability level for every individual module is also incorporated in the formulation of the problem. A framework based on Karush Kuhn Tucker (KKT) conditions is presented to work out the resulting non-linear optimization problem. A simulated numerical illustration has been analyzed to reflect the formulation of the case and its solution by the algorithm proposed.

show abstract

Section: 'Bmentioning

confidence: 99%

A study of Optimal Testing Resource Allocation Problem for Modular Software with Change Point

Kaur

Aggarwal

Kedia³

2018

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…where r represents the fault-detection rate per expended testing-effort at testing time t and s(t)(  dS(t)/dt) is the amount of the testing-effort expended at arbitrary testing time t. In Equation (8), we assume that the fault-detection rate at testing-time t depends on the instantaneous testing-effort expenditures [9]. That means, the testing-team can detect or remove more software faults when the software development manager decides to expend more testing-effort to detect or remove software fault.…”

Section: Modelingmentioning

confidence: 99%

“…The testing-effort, such as the number of executed test-cases, testing-coverage, and CPU hours expended in the testing phase, is well known as one of the most important factors being related to the software reliability growth process [9]. Under the above background, there is necessity to discuss a testingeffort dependent SRGM on a continuous-state space for the purpose of developing a more plausible continuous-state space SRGM.…”

Section: Introductionmentioning

confidence: 99%

Lognormal Process Software Reliability Modeling with Testing-Effort

Inoue¹,

Yamada²

2013

JSEA

Self Cite

View full text Add to dashboard Cite

We propose a software reliability growth model with testing-effort based on a continuous-state space stochastic process, such as a lognormal process, and conduct its goodness-of-fit evaluation. We also discuss a parameter estimation method of our model. Then, we derive several software reliability assessment measures by the probability distribution of its solution process, and compare our model with existing continuous-state space software reliability growth models in terms of the mean square error and the Akaike's information criterion by using actual fault count data.

show abstract

“…Therefore, this function plays an important role in modeling software reliability and it can be described by different distributions. From the studies in [1][2][3][4][5]14], several testing-effort pattern expressions, such as Exponential, Rayleigh, and Weibull-type curves, can be applied.…”

Section: Review Of Srgm With Logistic Testing-effort Functionmentioning

confidence: 99%

“…Among these models, Goel and Okumoto considered an NHPP as the stochastic process to describe the fault process [11]. Yamada et al [1][2][3] modified the G-O model and incorporated the concept of testing-effort in an NHPP model to get a better description of the software fault phenomenon. Later, we [7][8] also proposed a new software reliability growth model with the logistic testing-effort function.…”

Section: Introductionmentioning

confidence: 99%