Software-reliability growth with a Weibull test-effort: a model and application

Yamada, Shigeru; Hishitani, Jun; Osaki, Shunji

doi:10.1109/24.210278

Cited by 178 publications

(156 citation statements)

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“…The influence of software testing effort has been included in several SRGMs Putnam [30]; Pillai and Nair [23]; Kapur, Garg, and Kumar [18]; Xie [26]; Ichimori, Yamada, and Nishiwaki [11]; Kapur and Bardhan [16]; Huang et al [9]; Kapur et al [19], Myers [21] planned that software will be constructed and tested individually in a chronological step. Yamada et al [27], Hou, Kuo, and Chang [7], Pham and Zhang [22] have recommended that system level software testing occurred only after the system was completely developed. Recently, Blackburn, Scudder, and Van Wassenhove [2], however, recommended that software development and system debugging and testing should be viewed as simultaneous activities.…”

Section: Research Backgroundmentioning

confidence: 99%

“…Often these models are based on the assumptions that the testing effort consumption and testing time follows Rayleigh and exponential distribution. The time dependent activities of the testing effort has been studied by many authors Basili and Zelkowitz [1], Kapur and Garg [17], Huang, Kuo, and Chen [9] and Yamada, Hishitani, and Osaki [27].…”

Section: Introductionmentioning

confidence: 99%

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A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

Johri¹,

Nasar²,

Chanda³

2013

IJCA

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Allocation of limited testing efforts to a software development project is a complex task for software managers. The challenges become difficult when the nature of the development process is considered in the dynamic environment. Numerous software reliability growth models have been proposed in last one decade to minimize the whole testing effort expenditures, but generally under static assumption. The main purpose of this article is to distribute total testing resource optimally under dynamic condition. An elaborate optimization policy is proposed using genetic algorithm and numerical example is also demonstrated. Genetic Algorithms (GAs) works with a set of individuals, representing probable solutions of the task. The selection theory is applied by using a criterion, giving an evaluation for the individual with respect to the desired solution. This article also studies the optimal resource allocation problems for different conditions by investigative the activities of the model parameters. General Terms Evolutionary Algorithm KeywordsGenetic Algorithm, testing effort allocation, Software reliability, SRGM Nomenclature a is the initial fault content in the software. b is the fault detection rate.is the number of fault removed at time 't'. m(t) is the cumulative number of fault detected till time 't' due to the testing effort w1(t).T the planning period.C1(m(t),w2(t)) Cost per unit at time 't' for cumulative faults removed m(t) and debugging effort w2(t).C 2 is the cost of testing per unit testing efforts.W is the total resources utilized during the SDLC at any point of time 't'.W 1 (t) current effort expenditure due to testing at time 't' W 2 (t) current effort expenditure due to debugging the fault at time 't'

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Section: Research Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

Johri¹,

Nasar²,

Chanda³

2013

IJCA

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“…From the work by B. Boehm [16], we know C 2 >C 1 as C 2 is usually an order of magnitude greater than C 1 . In order to detect additional faults during testing, the testers and debuggers may use new automated tools or techniques.…”

Section: Optimal Software Release Time Problemmentioning

confidence: 99%

“…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%

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Software reliability modeling and cost estimation incorporating testing-effort and efficiency

Huang

Kuo

et al.

Proceedings 10th International Symposium on Software Reliability Engineering (Cat. No.PR00443)

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Software Reliability Modeling and Analysis

Xie

Yang

2007

Encyclopedia of Statistics in Quality and Reliability

120

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Computer systems can be found almost everywhere in our daily life and they are also extensively used in areas such as defense and finance. Unlike hardware reliability which is a well‐developed area, software reliability is relatively new and is generally more difficult to insure. In order to improve the reliability of a software product, the software has to be tested extensively prior to its release. Software reliability models are commonly used to monitor the testing process and to measure and predict future reliability of the software. Such study is also useful to determine the release time of the software product. During the past three decades there have been many software reliability models proposed and studied. This chapter reviews some of the models and discusses the applications of the models for decision making in software development.

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Software-reliability growth with a Weibull test-effort: a model and application

Cited by 178 publications

References 15 publications

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

Software reliability modeling and cost estimation incorporating testing-effort and efficiency

Software Reliability Modeling and Analysis

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