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Issue No.04 - July-Aug. (2013 vol.30)
pp: 64-71
Jacek Czerwonka , Microsoft
Nachiappan Nagappan , Microsoft Research
Wolfram Schulte , Microsoft
Brendan Murphy , Microsoft Research
The scale and speed of today's software development efforts impose unprecedented constraints on the pace and quality of decisions made during planning, implementation, and postrelease maintenance and support for software. Decisions during the planning process include level of staffing and choosing a development model given the scope of a project and timelines. Tracking progress, course correcting, and identifying and mitigating risks are key in the development phase, as are monitoring aspects of and improving overall customer satisfaction in the maintenance and support phase. Availability of relevant data can greatly increase both the speed and likelihood of making a decision that leads to a successful software system. This article outlines the process Microsoft has gone through developing CODEMINE--a software development data analytics platform for collecting and analyzing engineering process data—its constraints, and pivotal organizational and technical choices.
Software development, Analytical models, Software quality, Data models, Data analysis, Computer bugs, Computer architecture, Software architecture, and software analytics, code quality, metrics, mining, reliability, software repositories
Jacek Czerwonka, Nachiappan Nagappan, Wolfram Schulte, Brendan Murphy, "CODEMINE: Building a Software Development Data Analytics Platform at Microsoft", IEEE Software, vol.30, no. 4, pp. 64-71, July-Aug. 2013, doi:10.1109/MS.2013.68
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