Title :
Software component quality prediction using KNN and Fuzzy logic
Author :
Hribar, Lovre ; Duka, Denis
Author_Institution :
Ericsson Nikola Tesla d.d. Croatia, Split, Croatia
Abstract :
Prediction of product quality within software engineering, preventive and corrective actions within the various project phases are constantly improved over the past decades. Practitioners and software companies were using various methods, different approaches and best practices in software development projects. Nevertheless, the issue of quality is pushing software companies to constantly invest in efforts to produce enough quality products that will arrive in time, with good enough quality to the customer. However, the quality is not for free, it has a price that is required at the time you notice about her. In this paper fuzzy logic and KNN classification method approaches are presented to predict Weibull distribution parameters shape, slope and the total number of faults in the system based on the software components individual contribution. Since the Weibull distribution is one of the most widely used probability distributions in the reliability engineering, predicting of its characteristics early in the software lifecycle might be useful input for the planning and control of verification activities.
Keywords :
Weibull distribution; costing; fault diagnosis; formal verification; fuzzy logic; learning (artificial intelligence); object-oriented programming; software development management; software fault tolerance; software quality; software reliability; KNN classification method; Weibull distribution parameter; fault diagnosis; fuzzy logic method; probability distribution; reliability engineering; software company; software component quality prediction; software development project; software engineering; verification planning; Decision support systems; Fiber reinforced plastics; Fuzzy logic; Software quality;
Conference_Titel :
MIPRO, 2010 Proceedings of the 33rd International Convention
Conference_Location :
Opatija
Print_ISBN :
978-1-4244-7763-0