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Improving the identification of hedonic quality in user requirements: a second controlled experiment

  • RE 2017
  • Published:
Requirements Engineering Aims and scope Submit manuscript

Abstract

Systematically engineering a good user experience (UX) into a computer-based system under development demands that the user requirements of the system reflect all needs, including emotional, of all stakeholders. User requirements address two different types of qualities: pragmatic qualities (PQs), that address system functionality and usability, and hedonic qualities (HQs) that address the stakeholder’s psychological well-being. Studies show that users tend to describe such satisfying UXes mainly with PQs and that some users seem to believe that they are describing an HQ when they are actually describing a PQ. The problem is to see if classification of any user requirement as PQ-related or HQ-related is difficult, and if so, why. We conducted two controlled experiments involving the same twelve requirements-engineering and UX professionals, hereinafter called “analysts.” The first experiment, which had the twelve analysts classifying each of 105 user requirements as PQ-related or HQ-related, shows that neither (1) an analyst’s involvement in the project from which the requirements came nor (2) the analyst’s use of a detailed model of the qualities in addition to the standard definitions of “PQ” and “HQ” has a positive effect on the consistency of the analyst’s classification with that of others. The second experiment, which had the twelve analysts classifying each of a set of 50 user requirements, derived from the 105 of the first experiment, showed that difficulties seem to be caused both by the analyst’s lacking skill in applying the definitions of “PQ” and “HQ” and by poorly written user requirement specifications. The first experiment revealed that classification of user requirements is a lot harder than initially assumed. The second experiment provided evidence that the difficulties can be mitigated by the combination of (1) training analysts in applying the definitions of “PQ” and “HQ” and (2) casting user requirement specifications in a new template that forces provision of the information needed for reliable classification. The experiment shows also that neither training analysts nor casting user requirement specifications in the new template, by itself, mitigates the difficulty in classifying user requirements.

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Notes

  1. For example, an expensive product communicates the value of the product’s user’s being rich to other people.

  2. In a hypothesis name, “A” means “alternative,” “N” means “null,” “E” means “concerning ease of classification,” and “C” means “concerning consistency of the classifications.”

  3. (1) ease of use and (2) utility.

  4. (1) enablement of personal development, (2) identification, (3) symbolism, (4) attachment, (5) aesthetics, (6) luxuriousness, (7) trust, (8) physical comfort, and (9) freedom from risk.

  5. There is no CorrN variable, because correctness can be evaluated for only templated, pragmatic or hedonic, and ambiguous user requirements.

  6. Wohlin et al. [32] define mono-operation bias as “If the experiment includes a single independent variable, case, subject or treatment, the experiment may under-represent the construct and thus not give the full picture of the theory. For example, if an inspection experiment is conducted with a single document as object, the cause construct is underrepresented.”

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Acknowledgements

The authors thank this paper’s anonymous reviewers for RE’17 and for this special issue of REJ, Sebastian Adam, Joerg Doerr, and Andreas Jedlitschka for their comments on earlier drafts of this paper. Daniel Berry’s work was supported in part by a Canadian NSERC grant NSERC-RGPIN227055-15.

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Authors and Affiliations

  1. Department of Computer Science, Technical University of Kaiserslautern, Gottlieb-Daimler-Strasse 42, Building 47, 67663, Kaiserslautern, Germany

    Andreas Maier

  2. Fraunhofer Institute for Experimental Software Engineering IESE, Fraunhofer-Platz 1, 67663, Kaiserslautern, Germany

    Andreas Maier

  3. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada

    Daniel M. Berry

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Correspondence to Andreas Maier or Daniel M. Berry.

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Maier, A., Berry, D.M. Improving the identification of hedonic quality in user requirements: a second controlled experiment. Requirements Eng 23, 401–424 (2018). https://doi.org/10.1007/s00766-018-0290-5

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