Welcome!
To use the personalized features of this site, please log in or register.
If you have forgotten your username or password, we can help.
|
 |
The impact of software evolution and reuse on software quality
| |
|
Peer Reviewed ArticlesThe impact of software evolution and reuse on software quality Taghi M. Khoshgoftaar1  , Edward B. Allen2, Kalai S. Kalaichelvan3 and Nishith Goel4 | (1) | Dept. of Computer Science and Engineering, Florida Atlantic University, 33431 Boca Raton, Florida, USA |
| (2) | Dept. of Computer Science and Engineering, Florida Atlantic University, 33431 Boca Raton, Florida, USA |
| (3) | Nortel Technology, P.O. Box 3511 Station C, K1Y 4H7 Ottawa, Ontario, Canada |
| (4) | Nortel Technology, P.O. Box 3511 Station C, K1Y 4H7 Ottawa, Ontario, Canada |
Abstract This paper presents a case study of a software project in the maintenance phase. The case study was based on a sample of modules, representing about 1.3 million lines of code, from a very large telecommunications system. Software quality models were developed to predict the number of faults expected from the coding through operations phases. Since modules from the prior release were often reused to develop a new release, one model incorporated reuse data as additional independent variables. We compare this model's performance to a similar model without reuse data. Software quality models often have product metrics as the only input data for predicting quality. There is an implicit assumption that all the modules have had a similar development history, so that product attributes are the primary drivers of different quality levels. Reuse of software as components and software evolution do not fit this assumption very well, and consequently, traditional models for such environments may not have adequate accuracy. Focusing on the software maintenance phase, this study demonstrated that reuse data can significantly improve the predictive accuracy of software quality models. Keywords software maintenance - software reuse - software quality models - software metrics - principal components analysis - multiple linear regression
Fulltext Preview (Small, Large)
 References secured to subscribers.
|
|
|
|
|
|