Making software process simulation modeling agile and pattern-based

Angkasaputra, Niniek

doi:10.1049/ic:20040462

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…If it is necessary to allow for the differing complexity of individual requirements a different sort of model would be needed. The method used by Angkasaputra and Pfahl (2004) using a discrete stochastic simulator has been used for this situation.…”

Section: Discussionmentioning

confidence: 99%

“…In many cases software development companies do not collect sufficient or relevant data to enable this comparison to be made even if they are willing to cooperate. Angkasaputra and Pfahl (2004) describe a comparison between an SD model and a discrete stochastic simulation model using Extend™ software and which was intended to be used to model the track of individual requirements in an agile process. Wernick and Hall (2004) give the results of a similar model to Andersson and Karlsson, examining the growth of a set of system requirements over 13 years based on the data for the International Computers Ltd (ICL) Virtual Machine Environment (VME) operating system.…”

Section: Reviewmentioning

confidence: 99%

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A control model of the software requirements process

White

2013

Kybernetes

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PurposeMany project failures can be attributed to changes in requirements as the project progresses. The purpose of this paper is to review several system dynamics models of the requirements process from the literature, especially the model of Andersson and Karlsson. This model addresses the deficiencies in the system dynamics model devised hitherto to create a model than can be used to predict requirements acquisition and staff resources for different levels of project complexity and size.Design/methodology/approachA new control‐theoretic model of requirements gathering is devised using the Software Test Model of Cangussu as a framework. Conventional control system techniques are then used to compute typical responses.FindingsThe results agree with the limited published data. This model can be used to predict an estimate of the possible number of requirements that could be devised for the project. It allows the incorporation of workforce, quality and complexity information to predict the time to complete the requirements process. This model is then used to predict how the requirements process responds to requirements creep and to changes in workforce used.Research limitations/implicationsThis model is theoretical and is proposed so that it can be simple tested. It makes a number of assumptions not yet recorded by industry that would have to be obtained.Practical implicationsIf it is supported by experimental data then a better project development model can be created with the prospect of greater control of project development.Originality/valueThis work, although based on the work of Cangussu et al., is devised by the author.

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Section: Discussionmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

A control model of the software requirements process

White

2013

Kybernetes

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“…Currently, several research groups worldwide are working on methods that aim at making the development of dependable SPS models faster and cheaper, e.g. by applying principles of agile development and process patterns (Angkasaputra and Pfahl, 2004). …”

Section: Future Developmentsmentioning

confidence: 99%

Virtual Software Engineering Laboratories in Support of Trade-off Analyses

2005

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Due to demanding customer needs and evolving technology, software organizations are forced to trade individual functional and non-functional product quality profiles against other factors such as cost, time, or productivity. The ability to influence or even control these factors requires a deep understanding of the complex relations between process and product attributes in relevant contexts. Based on such understanding, decision support is needed to adjust processes so that they match the product quality goals without violating given project constraints. We propose to use a Virtual Software Engineering Laboratory (VSEL) to establish such decision support cost-effectively. VSELs can be considered as being complementary to existing (empirical) Software Engineering Laboratories. This paper gives an introduction into the cornerstones of VSELs, discusses how they complement traditional empirically based Software Engineering Laboratories (SELs), and illustrates with the help of case examples from industrial and research environments, how to use them in support of product-focused trade-off analyses.

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A customizable pattern‐based software process simulation model: design, calibration and application

Garousi

Khosrovian

Pfahl

2009

Softw Process Imprv Pract

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Software process analysis and improvement relies heavily on empirical research. Empirical research requires measurement, experimentation, and modeling. However, whatever evidence is gained via empirical research is strongly context dependent. Thus, it is hard to combine results and capitalize upon them for the purpose of improvement in evolving development environments. The process simulation model GENSIM 2.0 addresses these challenges. GENSIM 2.0 is a generic process simulation tool representing V‐model type software development processes. Compared to existing process simulation models in the literature, the novelty of GENSIM 2.0 is twofold. Firstly, its model structure is customizable to organization‐specific processes. This is achieved by using a limited set of generic structures (macro‐patterns). Secondly, its model parameters can be easily calibrated to available empirical data and expert knowledge. This is achieved by making the internal model structures explicit and by providing guidance on how to calibrate model parameters. This article outlines the structure of GENSIM 2.0, gives examples on how to calibrate the model to available empirical data, and demonstrates its usefulness through two application scenarios The first scenario illustrates how GENSIM 2.0 helps in finding effective combinations of verification and validation techniques under given time and effort constraints. The second scenario shows how the simulator supports in finding the best combination of alternative verification techniques. Copyright © 2009 John Wiley & Sons, Ltd.

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Making software process simulation modeling agile and pattern-based

Cited by 9 publications

References 6 publications

A control model of the software requirements process

A control model of the software requirements process

Virtual Software Engineering Laboratories in Support of Trade-off Analyses

A customizable pattern‐based software process simulation model: design, calibration and application

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