• RICARDO BRITTO Department of Software Engineering, Blekinge Institute of Technology Karlskrona, 371 49, Sweden
  • MUHAMMAD USMAN Department of Software Engineering, Blekinge Institute of Technology Karlskrona, 371 49, Sweden
  • EMILIA MENDES Department of Computer Science and Engineering, Blekinge Institute of Technology Karlskrona, 371 49, Sweden


Web effort predictors, Taxonomy, Knowledge Classification, Web Engineering


Web engineering as a field has emerged to address challenges associated with developingWeb applications. It is known that the development of Web applications differs from the development of non-Web applications, especially regarding some aspects such asWeb size metrics. The classification of existing Web engineering knowledge would be beneficial for both practitioners and researchers in many different ways, such as finding research gaps and supporting decision making. In the context of Web effort estimation, a taxonomy was proposed to classify the existing size metrics, and more recently a systematic literature review was conducted to identify aspects related to Web resource/effort estimation. However, there is no study that classifiesWeb predictors (both size metrics and cost drivers). The main objective of this study is to organize the body of knowledge onWeb effort predictors by designing and using a taxonomy, aiming at supporting both research and practice inWeb effort estimation. To design our taxonomy, we used a recently proposed taxonomy design method. As input, we used the results of a previously conducted systematic literature review (updated in this study), an existing taxonomy of Web size metrics and expert knowledge. We identified 165 unique Web effort predictors from a final set of 98 primary studies; they were used as one of the basis to design our hierarchical taxonomy. The taxonomy has three levels, organized into 13 categories. We demonstrated the utility of the taxonomy and body of knowledge by using examples. The proposed taxonomy can be beneficial in the following ways: i) It can help to identify research gaps and some literature of interest and ii) it can support the selection of predictors for Web effort estimation. We also intend to extend the taxonomy presented to also include effort estimation techniques and accuracy metrics.


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