Till sidans topp

Sidansvarig: Webbredaktion
Sidan uppdaterades: 2012-09-11 15:12

Tipsa en vän
Utskriftsversion

Analysing defect inflow d… - Göteborgs universitet Till startsida
Webbkarta
Till innehåll Läs mer om hur kakor används på gu.se

Analysing defect inflow distribution of automotive software projects

Paper i proceeding
Författare Rakesh Rana
Miroslaw Staron
Christian Berger
Jörgen Hansson
martin nilsson
Publicerad i PROMISE '14 Proceedings of the 10th International Conference on Predictive Models in Software Engineering
ISBN 978-1-4503-2898-2
Förlag Association for Computing Machinery (ACM)
Publiceringsår 2014
Publicerad vid Institutionen för data- och informationsteknik (GU)
Språk en
Länkar dx.doi.org/10.1145/2639490.2639507
Ämneskategorier Data- och informationsvetenskap

Sammanfattning

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.

Sidansvarig: Webbredaktion|Sidan uppdaterades: 2012-09-11
Dela:

På Göteborgs universitet använder vi kakor (cookies) för att webbplatsen ska fungera på ett bra sätt för dig. Genom att surfa vidare godkänner du att vi använder kakor.  Vad är kakor?