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Supporting Analogy-based Effort Estimation with the Use of Ontologies

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper concerns effort estimation of software development projects, in particular, at the level of product delivery stages. It proposes a new approach to model project data to support expert-supervised analogy-based effort estimation. The data is modeled using Semantic Web technologies, such as Resource Description Framework (RDF) and Ontology Language for the Web (OWL). Moreover, in the paper, we define a method of supervised case-based reasoning. The method enables to search for similar projects’ tasks at different levels of abstraction. For instance, instead of searching for a task performed by a specific person, one could look for tasks performed by people with similar capabilities. The proposed method relies on ontology that defines the core concepts and relationships. However, it is possible to introduce new classes and relationships, without the need of altering the search mechanisms. Finally, we implemented a prototype tool that was used to preliminary validate the proposed approach. We observed that the proposed approach could potentially help experts in estimating non-trivial tasks that are often underestimated.
Rocznik
Strony
53--64
Opis fizyczny
Bibliogr. 24 poz., tab., wykr.
Twórcy
autor
  • Faculty of Computing, Institute of Computing Science, Poznan University of Technology
autor
  • Faculty of Computing, Institute of Computing Science, Poznan University of Technology
Bibliografia
  • [1] P. R. Hill, Practical Software Project Estimation: A Toolkit for Estimating Software Development Effort & Duration. McGraw-Hill, 2011.
  • [2] B. Boehm, B. Clark, E. Horowitz, C. Westland, R. Madachy, and R. Selby, “Cost models for future software life cycle processes: COCOMO 2.0,” Annals of Software Engineering, Vol. 1, No. 1, 1995, pp. 57–94.
  • [3] F.Walkerden and R. Jeffery, “An empirical study of analogy-based software effort estimation,” Empirical Software Engineering, Vol. 4, No. 2, 1999, pp. 135–158.
  • [4] M. Shepperd, C. Schofield, and B. Kitchenham, “Effort estimation using analogy,” in Proceedings of the 18th International Conference on Software Engineering, Berlin, 1996. IEEE, 1996, pp. 170–178.
  • [5] T. Mukhopadhyay, S. Vicinanza, and M. Prietula, “Examining the feasibility of a case-based reasoning model for software effort estimation,” MIS Quarterly, Vol. 16, No. 2, 1992, pp. 155–171.
  • [6] K. Schwaber and M. Beedle, Agile software development with Scrum. Prentice Hall, 2002.
  • [7] K. Beck and C. Andres, Extreme programming explained: embrace change. Addison-Wesley Professional, 2004.
  • [8] J. Grenning, “Planning poker or how to avoid analysis paralysis while release planning,” 2002.
  • [9] K. Moløkken-Østvold, N. C. Haugen, and H. C. Benestad, “Using planning poker for combining expert estimates in software projects,” Journal of Systems and Software, Vol. 81, No. 12, 2008, pp. 2106–2117.
  • [10] V. Mahnic, “A case study on agile estimating and planning using scrum,” Electronics and Electrical Engineering, Vol. 111, No. 5, 2011, pp. 123–128.
  • [11] V. Mahnic and T. Hovelja, “On using planning poker for estimating user stories,” Journal of Systems and Software, Vol. 85, No. 9, 2012, pp. 2086–2095.
  • [12] M. Jorgensen, B. Boehm, and S. Rifkin, “Software development effort estimation: Formal models or expert judgment?” Software, IEEE, Vol. 26, No. 2, March 2009, pp. 14–19.
  • [13] R. Popli and N. Chauhan, “Cost and effort estimation in agile software development,” in Optimization, Reliabilty, and Information Technology (ICROIT), 2014 International Conference on. IEEE, 2014, pp. 57–61.
  • [14] J. Hebeler, M. Fisher, R. Blace, and A. Perez-Lopez, Semantic web programming. John Wiley & Sons, 2011.
  • [15] J. Nawrocki, L. Olek, M. Jasinski, B. Paliswiat, B. Walter, B. Pietrzak, and P. Godek, “Balancing agility and discipline with xprince,” in Rapid integration of software engineering techniques. Springer, 2006, pp. 266–277.
  • [16] O. of Government Commerce, Managing Successful Projects with PRINCE2. TSO, 2009.
  • [17] T. Foss, E. Stensrud, B. Kitchenham, and I. Myrtveit, “A simulation study of the model evaluation criterion mmre,” Software Engineering, IEEE Transactions on, Vol. 29, No. 11, 2003, pp. 985–995.
  • [18] M. Jørgensen, “A critique of how we measure and interpret the accuracy of software development effort estimation,” in First International Workshop on Software Productivity Analysis and Cost Estimation. Information Processing Society of Japan, Nagoya. Citeseer, 2007.
  • [19] B. A. Kitchenham, L. M. Pickard, S. G. Mac-Donell, and M. J. Shepperd, “What accuracy statistics really measure [software estimation],” in Software, IEE Proceedings-, Vol. 148, No. 3. IET, 2001, pp. 81–85.
  • [20] M. Shepperd, M. Cartwright, and G. Kadoda, “On building prediction systems for software engineers,” Empirical Software Engineering, Vol. 5, No. 3, 2000, pp. 175–182.
  • [21] M. Azzeh, D. Neagu, and P. I. Cowling, “Analogy-based software effort estimation using fuzzy numbers,” Journal of Systems and Software, Vol. 84, No. 2, 2011, pp. 270–284.
  • [22] P. Hearty, N. Fenton, D. Marquez, and M. Neil, “Predicting project velocity in xp using a learning dynamic bayesian network model,” Software Engineering, IEEE Transactions on, Vol. 35, No. 1, 2009, pp. 124–137.
  • [23] E. Miranda, P. Bourque, and A. Abran, “Sizing user stories using paired comparisons,” Information and Software Technology, Vol. 51, No. 9, 2009, pp. 1327–1337.
  • [24] K. Hamdan, H. El Khatib, J. Moses, and P. Smith, “A software cost ontology system for assisting estimation of software project effort for use with case-based reasoning,” in Innovations in Information Technology, 2006. IEEE, 2006, pp. 1–5.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a802b535-520b-4506-932c-090215f41e49
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