Name
Abstract | ||
|---|---|---|
In general, estimating software effort using a Use Case Point (UCP) size requires the use of productivity as a second prediction factor. However, there are three drawbacks to this approach: (1) there is no clear procedure for predicting productivity in the early stages, (2) the use of fixed or limited productivity ratios does not allow research to reflect the realities of the software industry, and (3) productivity from historical data is often challenging. The new UCP datasets now available allow us to perform further empirical investigations of the productivity variable in order to estimate the UCP effort. Accordingly, four different prediction models based on productivity were used. The results showed that learning productivity from historical data is more efficient than using classical approaches that rely on default or limited productivity values. In addition, predicting productivity from historical environmental factors is not often accurate. From here we conclude that productivity is an effective factor for estimating the software effort based on the UCP in the presence and absence of previous historical data. Moreover, productivity measurement should be flexible and adjustable when historical data is available. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.scico.2022.102819 | Science of Computer Programming |
Keywords | DocType | Volume |
Use Case Points,Software productivity,Software effort estimation,Software size measures,Regression to mean | Journal | 219 |
ISSN | Citations | PageRank |
0167-6423 | 1 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Azzeh | 1 | 2 | 1.08 |
| Ali Bou Nassif | 2 | 1 | 0.37 |
| Yousef Elsheikh | 3 | 1 | 0.37 |
| Lefteris Angelis | 4 | 1296 | 82.51 |