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Göteborgs universitets publikationer

Analysing defect inflow distribution of automotive software projects

Författare och institution:
Rakesh Rana (Institutionen för data- och informationsteknik (GU)); Miroslaw Staron (Institutionen för data- och informationsteknik (GU)); Christian Berger (Institutionen för data- och informationsteknik (GU)); Jörgen Hansson (Institutionen för data- och informationsteknik, Software Engineering (Chalmers), Chalmers); martin nilsson (-)
Publicerad i:
PROMISE '14 Proceedings of the 10th International Conference on Predictive Models in Software Engineering, s. 22-31
Konferensbidrag, refereegranskat
Sammanfattning (abstract):
Defects are real and observable indicators of software quality that can be analyzed and modelled to track the quality and reliability of software system during development and testing. A number of software reliability growth models (SRGMs) have been introduced and evaluated which are based on different family of distributions such as exponential, Weibull, Non-Homogeneous Poisson Process etc. There exist no standard way of selecting the most appropriate SRGMs for given defect data and further the distribution of defect inflow for real software projects from different industrial domains is also not well documented. In this paper we explore the defect inflow distribution of four large software projects from the automotive domain. We evaluate six standard distributions for their ability to fit the defect inflow data and also assess which information criterion is practical for selecting the distribution with best fit. Our results show that beta distribution provides the best fit to the defect inflow data from all projects with different distribution characteristics. Finding the underlying distribution of defect inflow not only help applying the appropriate statistical techniques for data analysis but also to select the appropriate SRGMs for modelling reliability. The information about defect inflow distribution is further useful for modelling the prior beliefs or experience as prior probabilities in Bayesian analysis.
Ämne (baseras på Högskoleverkets indelning av forskningsämnen):
Data- och informationsvetenskap
Postens nummer:
Posten skapad:
2014-12-11 09:26
Posten ändrad:
2016-09-07 16:03

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