Open AccessFuzzy-Provenance Architecture for Effort Metric Data Quality Assessment
Author(s) -
Rita Cristina Galarraga Berardi,
Duncan D. Ruiz
Publication year - 2009
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbqs.2009.15500
Subject(s) - computer science , metric (unit) , software quality , software metric , quality (philosophy) , data mining , software , fuzzy logic , software engineering , product metric , software architecture , reliability engineering , data science , software development , artificial intelligence , engineering , programming language , mathematics , philosophy , operations management , epistemology , metric space , mathematical analysis
Software companies rely on stored metric data in order to track and manage their projects, through analyzing, monitoring and estimating software metrics. If managers cannot believe the metrics data, the product that is being developed is fated to fail. Currently, the assessment of software effort is subjective and derived mainly through managers’ assumptions, which is fundamentally an error-prone process. We present an architecture for assessing data quality of software effort metric based on data provenance associated with a mechanism of logical inference (fuzzy logic). The contribution is to provide an assessment to search evident reasons for a low quality in order to ensure that the metrics can be used with sufficient reliability.