| Title |
Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects
|
|---|---|
| Published in |
PLOS ONE, November 2012
|
| DOI | 10.1371/journal.pone.0050531 |
| Pubmed ID | |
| Authors |
Arturo Chavoya, Cuauhtemoc Lopez-Martin, Irma R. Andalon-Garcia, M. E. Meda-Campaña |
| Abstract |
Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Iran, Islamic Republic of | 1 | 5% |
| Unknown | 18 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 7 | 37% |
| Student > Doctoral Student | 2 | 11% |
| Student > Postgraduate | 2 | 11% |
| Professor | 2 | 11% |
| Lecturer | 1 | 5% |
| Other | 4 | 21% |
| Unknown | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 42% |
| Engineering | 4 | 21% |
| Environmental Science | 1 | 5% |
| Business, Management and Accounting | 1 | 5% |
| Physics and Astronomy | 1 | 5% |
| Other | 3 | 16% |
| Unknown | 1 | 5% |