Monitoring service quality: methods and solutions to implement a managerial dash-board to improve software development

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📝 Original Info

  • Title: Monitoring service quality: methods and solutions to implement a managerial dash-board to improve software development
  • ArXiv ID: 1404.7558
  • Date: 2014-05-01
  • Authors: Researchers from original ArXiv paper

📝 Abstract

The software used for running and handling the inter-bank network framework provides services with extremely strict uptime (above 99.98 percent) and quality requirements, thus tools to trace and manage changes as well as metrics to measure process quality are essential. Having conducted a two year long campaign of data collection and activity monitoring it has been possible to analyze a huge amount of process data from which many aggregated indicators were derived, selected and evaluated for providing a managerial dash-board to monitor software development.

💡 Deep Analysis

Deep Dive into Monitoring service quality: methods and solutions to implement a managerial dash-board to improve software development.

The software used for running and handling the inter-bank network framework provides services with extremely strict uptime (above 99.98 percent) and quality requirements, thus tools to trace and manage changes as well as metrics to measure process quality are essential. Having conducted a two year long campaign of data collection and activity monitoring it has been possible to analyze a huge amount of process data from which many aggregated indicators were derived, selected and evaluated for providing a managerial dash-board to monitor software development.

📄 Full Content

The software used for running and handling the inter-bank network framework provides services with extremely strict uptime (above 99.98 percent) and quality requirements, thus tools to trace and manage changes as well as metrics to measure process quality are essential. Having conducted a two year long campaign of data collection and activity monitoring it has been possible to analyze a huge amount of process data from which many aggregated indicators were derived, selected and evaluated for providing a managerial dash-board to monitor software development.

Reference

This content is AI-processed based on ArXiv data.

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