Abstract:
This paper is an attempt to understand the processes by which software ages. We de ne code to be aged or decayed if its structure makes it unnecessarily difficult to understand or change, and we measure the extent of decay by counting the number of faults in code in a period of time. Using change management data from a very large, long-lived software system, we explore the extent to which measurements from the change history are successful in predicting the distribution over modules of these incidences of faults. In general, process measures based on the change history are more useful in predicting fault rates than product metrics of the code: for instance, the number of times code has been changed is a better indication of how many faults it will contain than is its length. We also compare the fault rates of code of various ages, finding that if a module is on the average a year older than an otherwise similar module, the older module will have roughly a third fewer faults. Our most successful model measures the fault potential of a module as the sum of contributions from all of the times the module has been changed, with large, recent changes receiving the most weight.
Keywords:
Fault potential, code decay, change manage- ment data, metrics, statistical analysis, generalized linear models.
tr80.pdf