Di Martino, Sergio (2009) Measures and Techniques for Effort Estimation of Web Applications: an empirical study based on a single-company dataset. [Pubblicazione in rivista scientifica]Full text not available from this repository.
|Item Type:||Pubblicazione in rivista scientifica|
|Title:||Measures and Techniques for Effort Estimation of Web Applications: an empirical study based on a single-company dataset|
|Autor/s:||S. Di Martino, F. Ferrucci, C. Gravino, E. Mendes|
|Number of Pages:||29|
|Journal or Publication Title:||JOURNAL OF WEB ENGINEERING|
|Page Range:||pp. 154-181|
|Number of Pages:||29|
|Uncontrolled Keywords:||Web applications, Size measures, Effort estimation, Empirical validation|
|Date Deposited:||21 Oct 2010 06:57|
|Last Modified:||30 Apr 2014 19:43|
Effort estimation is a key management activity which goes on throughout a software project being fundamental for accurate project planning and for allocating resources adequately. Thus, it is important to identify techniques and measures that can support such project management activity during the development of Web applications. To this aim, empirical investigations should be performed using data coming from the industrial world. To address this issue, this paper reports on an empirical study based on data from 15 Web applications developed by an Italian software company. The objective of the study was two-fold. The first goal was to verify whether or not some size measures were good indicators of the effort spent to develop the Web applications taken into account. The second goal was to compare the effectiveness of some techniques to establish the relationships between the employed size measures and the development effort of the Web applications. The measures were organized in two sets, where the first one included some length measures while the second one consisted of the nine components which are used to estimate the Web Objects measure. The techniques taken into account were Stepwise Regression, Case-Based Reasoning, and Regression Tree. The results indicated that both the sets of size measures were good indicators of the effort for the analyzed dataset. Furthermore, the analysis also revealed that the first set presented significantly superior performance than the second set when using Stepwise Regression. No significant differences between the two sets of size measures were highlighted when using Case-Based Reasoning and Regression Tree.
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