Studying the impact of uncertainty in operational release planning – An integrated method and its initial evaluation

Al‐Emran, Ahmed; Kapur, Puneet; Pfahl, Dietmar; Ruhe, Guenther

doi:10.1016/j.infsof.2009.11.003

Cited by 23 publications

(19 citation statements)

References 28 publications

(35 reference statements)

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“…Al-Emran [71] combines Monte-Carlo simulation with DES and models the release planning phase. Without presenting any details of the evaluation, they claimed that it was found useful for early risk detection and the development of mitigation strategies in the case study conducted at a company.…”

Section: Evaluation Of Usefulnessmentioning

confidence: 99%

A systematic literature review on the industrial use of software process simulation

Ali

Petersen

Wohlin

2014

Journal of Systems and Software

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Context -Software process simulation modelling (SPSM) captures the dynamic behavior and uncertainty in the software process. Existing literature has conflicting claims about the practical usefulness of SPSM: SPSM is useful and has an industrial impact; SPSM is useful and has no industrial impact yet; SPSM is not useful and has little potential for industry. Objective -To assess the conflicting standpoints on the usefulness of SPSM. Method -A systematic literature review was performed to identify, assess and aggregate empirical evidence on the usefulness of SPSM. Results -In the primary studies, to date, the persistent trend is that of proof-of-concept applications of software process simulation for various purposes (e.g. estimation, training, process improvement, etc.). They score poorly on the stated quality criteria. Also only a few studies report some initial evaluation of the simulation models for the intended purposes. Conclusion -There is a lack of conclusive evidence to substantiate the claimed usefulness of SPSM for any of the intended purposes. A few studies that report the cost of applying simulation do not support the claim that it is an inexpensive method. Furthermore, there is a paramount need for improvement in conducting and reporting simulation studies with an emphasis on evaluation against the intended purpose.

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Section: Evaluation Of Usefulnessmentioning

confidence: 99%

A systematic literature review on the industrial use of software process simulation

Ali

Petersen

Wohlin

2014

Journal of Systems and Software

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“…System performance was concluded to be significantly affected by demand changes and supply uncertainties. Emran et al [30] examined the impact of uncertainty in operational release planning on total duration using Monte Carlo simulation. They concluded that every uncertain factor individually increases makespan; and for any combination of uncertainty factors, impact is bigger than the addition of their individual impacts.…”

Section: Current Methods To Tackle Uncertaintymentioning

confidence: 99%

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

Azizi

Ali

Ping

2015

The South African Journal of Industrial Engineering

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Production throughput measures the performance and behaviour of a production system. Production throughput modelling is complex because of uncertainties in the production line. This study examined the potential application of the adaptive neuro-fuzzy inference system (ANFIS) to modelling the throughput of production under five significant production uncertainties: scrap, setup time, break time, demand, and lead time of manufacturing. The effects of these uncertainties on the production of floor tiles were studied by performing 104 observations on the production uncertainties over 104 weeks, based on a weekly production plan in a tile manufacturing industry. The results of the ANFIS model were compared with the multiple linear regression (MLR) model. The results showed that the ANFIS model was capable of forecasting production throughput under uncertainty with higher accuracy than was the MLR model, indicated by an R-squared of 98 per cent. OPSOMMINGProduksie deurset meet die vertoning en gedrag van 'n produksiesisteem. Produksie deurset modellering is ingewikkeld as gevolg van die onsekerhede in die produksielyn. Hierdie studie ondersoek die toepassing van die aanpasbare neuro-wasige afleidingsisteem om die deurset van produksie onderhewe aan vyf noemenswaardige produksie onsekerhede, naamlik skroot, opstel tyd, breek tyd, aanvraag en die vervaardiging leityd. Die effek van hierdie onsekerhede op die vervaardiging van vloerteëls is ondersoek deur 104 weeklikse observasies op die produksie onsekerhede oor 'n tydperk van 104 weke te neem. Die resultate van die model is vergelyk met 'n meervoudige lineêre regressie model. Die resultate toon dat die aanpasbare neuro-wasige afleidingsisteem in staat was om produksie deurset onderhewe aan onsekerheid te voorspel met 'n hoër akkuraatheid as die meervoudige lineêre regressie model. Dit word aangedui met 'n bepaalheidskoëffisient van 98 persent.

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“…This approach measured parameter sensitivity by perturbing variables upward or downward to try out various 'what-if' scenarios. Al-Emran et al [52], [53] performed probabilistic sensitivity analysis to evaluate the impact of uncertainties in operational release planning and product release planning in 2010. To avoid stochastic nature of the meta-heuristic algorithms, in 2014, Harman et al [4] applied a naive exact algorithm, a variant of the Nemhauser-Ullmann's algorithm [54], to study precise sensitivity analysis of NRP without considering requirements interaction.…”

Section: Uncertainty and Risk Handling In Requirements Engineeringmentioning

confidence: 99%

The Value of Exact Analysis in Requirements Selection

Harman

et al. 2017

IIEEE Trans. Software Eng.

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Uncertainty is characterised by incomplete understanding. It is inevitable in the early phase of requirements engineering, and can lead to unsound requirement decisions. Inappropriate requirement choices may result in products that fail to satisfy stakeholders' needs, and might cause loss of revenue. To overcome uncertainty, requirements engineering decision support needs uncertainty management. In this research, we develop a decision support framework METRO for the Next Release Problem (NRP) to manage algorithmic uncertainty and requirements uncertainty. An exact NRP solver (NSGDP) lies at the heart of METRO. NSGDP's exactness eliminates interference caused by approximate existing NRP solvers. We apply NSGDP to three NRP instances, derived from a real world NRP instance, RALIC, and compare with NSGA-II, a widely-used approximate (inexact) technique. We find the randomness of NSGA-II results in decision makers missing up to 99.95 percent of the optimal solutions and obtaining up to 36.48 percent inexact requirement selection decisions. The chance of getting an inexact decision using existing approximate approaches is negatively correlated with the implementation cost of a requirement (Spearman r up to À0:72). Compared to the inexact existing approach, NSGDP saves 15.21 percent lost revenue, on average, for the RALIC dataset.

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Studying the impact of uncertainty in operational release planning – An integrated method and its initial evaluation

Cited by 23 publications

References 28 publications

A systematic literature review on the industrial use of software process simulation

A systematic literature review on the industrial use of software process simulation

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

The Value of Exact Analysis in Requirements Selection

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