A TAXONOMY OF WEB EFFORT PREDICTORS
Keywords: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.
D. Azhar, E. Mendes, and P. Riddle. A systematic review of web resource estimation. In Proceedings of the
th International Conference on Predictive Models in Software Engineering - PROMISE ’12, pages 49–58,
S. Bayona-Or´e, J. A. Calvo-Manzano, G. Cuevas, and T. San-Feliu. Critical success factors taxonomy for
software process deployment. Software Quality Control, 22(1):21–48, Mar. 2014.
P. Bourque and R. E. Farley, editors. Guide to the Software Engineering Body of Knowledge v3. IEEE
Computer Society, 2013.
A. Ginige and S. Murugesan. Web engineering: An introduction. MultiMedia, IEEE, 8(1):14–18, 2001.
R. L. Glass and I. Vessey. Contemporary application-domain taxonomies. Software, IEEE, 12(4):63–76, Jul
B. H. Kwasnik. The role of classification in knowledge representation and discovery. Library Trends,
C. Linnaeus. System of nature through the three kingdoms of nature, according to classes, orders, genera
and species, with characters, differences, synonyms, places (in Latin). Laurentius Salvius, -, 10th edition,
A. McDonald and R.Welland. Web engineering in practice. In Proceedings of the fourth WWW10 Workshop
on Web Engineering, pages 21–30, 2001.
E. Mendes. Cost Estimation Techniques for Web Projects. IGI Publishing, 2007.
E. Mendes. Predicting web development effort using a bayesian network. In Proceedings of the 11th International
Conference on Evaluation and Assessment in Software Engineering, EASE’07, pages 83–93. British
Computer Society, 2007.
E. Mendes, S. Counsell, and N. Mosley. Towards a taxonomy of hypermedia and web application size
metrics. In D. Lowe and M. Gaedke, editors, Web Engineering, volume 3579 of Lecture Notes in Computer
Science, pages 110–123. Springer Berlin Heidelberg, 2005.
E. Mendes, N. Mosley, and S. Counsell. Investigating web size metrics for early web cost estimation. J. Syst.
Softw., 77(2):157–172, Aug. 2005.
E. Mendes, N. Mosley, and S. Counsell. The need for web engineering: An introduction. In E. Mendes and
N. Mosley, editors, Web Engineering, pages 1–27. Springer Berlin Heidelberg, 2006.
T. E. Moffitt. Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy.
Psychological Review, 100(4):674–701, Oct 1993.
K. Moløkken-Østvold and M. Jørgensen. Group processes in software effort estimation. Empirical Software
Engineering, 9(4):315–334, 2004.
J. Moses and J. Clifford. Learning how to improve effort estimation in small software development companies.
In Proceedings of the 24th Annual International on Computer Software and Applications Conference
(COMPSAC), pages 522–527. IEEE, 2000.
D. J. Reifer. Web development: estimating quick-to-market software. IEEE software, 17(6):57–64, 2000.
D. Smite, C. Wohlin, Z. Galvina, and R. Prikladnicki. An empirically based terminology and taxonomy for
global software engineering. Empirical Software Engineering, 19(1):105–153, 2014.
P. Umbers and G. Miles. Resource estimation for web applications. In Proceedings of the 10th International
Symposium Software Metrics, METRICS ’04, pages 370–381. IEEE Computer Society, 2004.
M. Usman, R. Britto, J. B¨orstler, and E. Mendes. Taxonomies in software engineering: A systematic mapping
study and a revised taxonomy development method. Information and Software Technology, 85:43 – 59, 2017.
S. Vegas, N. Juristo, and V. Basili. Maturing software engineering knowledge through classifications: A case
study on unit testing techniques. Software Engineering, IEEE Transactions on, 35(4):551–565, July 2009.
G. R. Wheaton. Development of a taxonomy of human performance: A review of classificatory systems
relating to tasks and performance. Technical report, American Institute for Research,Washington DC, 1968.
C. Wohlin. Writing for synthesis of evidence in empirical software engineering. In Proceedings of the 8th
ACM/IEEE International Symposium on Empirical Software Engineering and Measurement - ESEM ’14,
pages 46:1–46:4, New York, NY, USA, 2014. ACM.
C. Wohlin and A. Aurum. Towards a decision-making structure for selecting a research design in empirical
software engineering. Empirical Software Engineering, pages 1–29, 2014.