Name
Abstract | ||
|---|---|---|
This paper is an attempt to understand the processes by which software ages. We define 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. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/32.859533 | IEEE Trans. Software Eng. |
Keywords | Field | DocType |
change history,software change history,long-lived software system,recent change,older module,fault rate,similar module,fewer fault,times code,predicting fault incidence,fault potential,change management data,software maintenance,software systems,history,statistical analysis,software measurement,time measurement,length measurement,change management,aging,indexing terms,predictive models,degradation,software fault tolerance,management of change,software metrics,metrics,generalized linear models,general linear model | Change management,Computer science,Software fault tolerance,Real-time computing,Software system,Software,Process Measures,Software metric,Software maintenance,Product metric,Reliability engineering | Journal |
Volume | Issue | ISSN |
26 | 7 | 0098-5589 |
Citations | PageRank | References |
407 | 25.68 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Todd L. Graves | 1 | 707 | 53.09 |
| Alan F. Karr | 2 | 1005 | 76.93 |
| J. S. Marron | 3 | 407 | 25.68 |
| Harvey Siy | 4 | 581 | 44.51 |