Inferring change effort from configuration management databases

In this paper we describe a methodology and algorithm for historical analysis of the effort necessary for developers to make changes to software. The algorithm identifies factors which have historically increased the difficulty of changes. This methodology has implications for research into cost drivers. As an example of a research finding, we find that a system under study was “decaying” in that changes grew more difficult to implement at a rate of 20% per year. We also quantify the difference in costs between changes that fix faults and additions of new functionality: fixes require 80% more effort after accounting for size. Since our methodology adds no overhead to the development process, we also envision it being used as a project management tool: for example, developers can identify code modules which have grown more difficult to change than previously, and can match changes to developers with appropriate expertise. The methodology uses data from a change management system, supported by monthly time sheet data if available. The method´s performance does not degrade much when the quality of the time sheet data is limited. We validate our results using a survey of the developers under study: the change efforts resulting from the algorithm match the developers´ opinions. Our methodology includes a technique based on the jackknife to determine factors that contribute significantly to change effort