Details

Autor(en) / Beteiligte

• Azzeh, Mohammad
• Nassif, Ali Bou
• Elsheikh, Yousef
• Angelis, Lefteris

Titel

On the value of project productivity for early effort estimation

Ist Teil von

• Science of computer programming, 2022-07, Vol.219, p.102819, Article 102819

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2022

Link zum Volltext

Quelle

ScienceDirect

Beschreibungen/Notizen

• 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. •Using different productivity ratios for each project seems more effective than using a static ratio for all projects.•The different productivity ratios should be learned through a robust model from a historical dataset.•Karner and S&W models may work well for educational projects but not for industrial projects.•The use of homogeneous datasets allows a slight improvement in the productivity and effort estimation for UCP models.