کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
464744 | 697404 | 2013 | 16 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Predicting aging-related bugs using software complexity metrics Predicting aging-related bugs using software complexity metrics](/preview/png/464744.png)
Long-running software systems tend to show degraded performance and an increased failure occurrence rate. This problem, known as Software Aging, which is typically related to the runtime accumulation of error conditions, is caused by the activation of the so-called Aging-Related Bugs (ARBs). This paper aims to predict the location of Aging-Related Bugs in complex software systems, so as to aid their identification during testing. First, we carried out a bug data analysis on three large software projects in order to collect data about ARBs. Then, a set of software complexity metrics were selected and extracted from the three projects. Finally, by using such metrics as predictor variables and machine learning algorithms, we built fault prediction models that can be used to predict which source code files are more prone to Aging-Related Bugs.
Journal: Performance Evaluation - Volume 70, Issue 3, March 2013, Pages 163–178