Continuous GQM: An automated framework for the Goal-Question-Metric paradigm

Measurement is an important aspect of Software Engineering as it is the foundation of predictable and controllable software project execution. Measurement is essential for assessing actual project progress, establishing baselines and validating the effects of improvement or controlling actions. The work performed in this thesis is based on Hackystat, a fully automated measurement framework for software engineering processes and products. Hackystat is designed to unobtrusively measure a wide range of metrics relevant to software development and collect them in a centralized data repository. Unfortunately, it is not easy to interpret, analyze and visualize the vast data collected by Hackystat in such way that it can effectively be used for software project control. A potential solution to that problem is to integrate Hackystat with the GQM (Goal / Question / Metric) Paradigm, a popular approach for goal-oriented, systematic definition of measurement programs for software-engineering processes and products. This integration should allow the goal-oriented use of the metric data collected by Hackystat and increase it’s usefulness for project control. During the course of this work, this extension to Hackystat which is later called hackyCGQM is implemented. As a result, hackyCGQM enables Hackystat to be used as a Software Project Control Center (SPCC) by providing purposeful high-level representations of the measurement data. Another interesting side-effect of the combination of Hackystat and hackyCGQM is that this system is able to perform fully automated measurement and analysis cycles. This leads to the development of cGQM, a specialized method for fully automated, GQM based measurement programs. As a summary, hackyCGQM seeks to implement a completely automated GQMbased measurement framework. This high degree of automation is made possible by limiting the implemented measurement programs to metrics which can be measured automatically, thus sacrificing the ability to use arbitrary metrics