An Alternative to the Rayleigh Curve Model for Software Development Effort

Parr, F. N.

doi:10.1109/tse.1980.230475

Cited by 72 publications

(38 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From our past studies [27], [28], a logistic TEF with a structuring index was proposed, which can be used to consider & evaluate the effects of possible improvements on software development methodology. The idea of a logistic TEF was proposed by F. N. Parr [29]; it predicts essentially the same behavior as the Rayleigh curve, except during the early part of the project. For a sample of some two dozen projects studied in the Yourdon 1978-1980 project survey, the logistic TEF was fairly accurate in describing expended TE [30].…”

Section: A Software Reliability Modelingmentioning

confidence: 98%

See 1 more Smart Citation

Optimal Testing Resource Allocation, and Sensitivity Analysis in Software Development

Huang

Lyu

2005

IEEE Trans. Rel.

View full text Add to dashboard Cite

Abstract-We consider two kinds of software testing-resource allocation problems. The first problem is to minimize the number of remaining faults given a fixed amount of testing-effort, and a reliability objective. The second problem is to minimize the amount of testing-effort given the number of remaining faults, and a reliability objective. We have proposed several strategies for module testing to help software project managers solve these problems, and make the best decisions. We provide several systematic solutions based on a nonhomogeneous Poisson process model, allowing systematic allocation of a specified amount of testing-resource expenditures for each software module under some constraints. We describe several numerical examples on the optimal testing-resource allocation problems to show applications & impacts of the proposed strategies during module testing. Experimental results indicate the advantages of the approaches we proposed in guiding software engineers & project managers toward best testing resource allocation in practice. Finally, an extensive sensitivity analysis is presented to investigate the effects of various principal parameters on the optimization problem of testing-resource allocation. The results can help us know which parameters have the most significant influence, and the changes of optimal testing-effort expenditures affected by the variations of fault detection rate & expected initial faults.

show abstract

Section: A Software Reliability Modelingmentioning

confidence: 98%

“…However, the above may have some negative components if , making infeasible for (28) & (29). If this is the case, the solution can be corrected by the following steps [4], [5], [10]:…”

Section: A Minimizing the Number Of Remaining Faults With A Given Fimentioning

confidence: 99%

Optimal Testing Resource Allocation, and Sensitivity Analysis in Software Development

Huang

Lyu

2005

IEEE Trans. Rel.

View full text Add to dashboard Cite

show abstract

“…the Putnam Norden Rayleigh curve [1]) while another activity was to try and build new models to fit the empirical data (e.g. the Parr curve [7]). Once models were identified, their predictive nature was studied as a means of resource scheduling.…”

Section: Model Generationmentioning

confidence: 99%

Data collection and evaluation for experimental computer science research

Zelkowitz

1984

Information Processing & Management

View full text Add to dashboard Cite

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%

“…the Parr model [14]) is based on a description of the actual software development process and can be used to describe the work profile of software development. In addition, this function can be used to consider and evaluate the effects of possible improvements on software development methodology, such as top-down design or stepwise refinement.…”

Section: A Generalized Logistic Testing-effort Functionmentioning

confidence: 99%