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A Method to Transform Automatically Extracted Product Features into Inputs for Kano-Like Models

  • Conference paper
  • First Online:
Product-Focused Software Process Improvement (PROFES 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10611))

Abstract

Background: In the context of a larger research project, we plan to automatically extract user needs (i.e., functional requirements) from online open sources and classify them using the principles of the Kano model. In this paper, we present a two-step method for automatically transforming feature related text extracted from online open sources into inputs for Kano-like models. Goal: The problem we are facing is how to transform requirements and related sentiments extracted from raw texts collected from an online open source into the input format required by our Kano-like models. To solve this problem, we need a method that transforms requirements and related sentiments into a format that corresponds to answers that would be given to either the functional or dysfunctional question of the Kano method on a specific requirement. Method: We propose a method consisting of two steps. In the first step, we apply machine learning methods to decide whether a text line extracted from an online open source corresponds to an answer of the functional or dysfunctional question asked in the Kano method. In the second step, we use a dictionary-based method to classify the sentiment of each statement such that we can assign an answer value to each text line previously classified as functional or dysfunctional. We implemented our method in the R language. We evaluate the accuracy of the proposed method using simulation. Result: Based on the simulation results, we found the overall accuracy of our method is 65%. We also found that data sources such as app store reviews are better suited to our analysis than question/answer sources such as Stack Overflow. Conclusion: The method we proposed can be used to automatically transform feature-related text into inputs for Kano-like models but performance improvements are needed.

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Notes

  1. 1.

    O = One-dimensional Quality, A = Attractive Quality, M = Must-be Quality, I = Indifferent Quality, R = Reverse Quality.

  2. 2.

    Accuracy = (TF + TD)/(TF + FF + FD + TD).

  3. 3.

    FPV = TF/(TF + FF).

  4. 4.

    DPV = TD/(FD + TD).

  5. 5.

    https://figshare.com/s/d13b6f16738190d7b935.

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Acknowledgement

The research was supported by the institutional research grant IUT20-55 of the Estonian Research Council. In addition, Huishi Yin was funded by the European Regional Development Fund for Higher Education.

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Authors and Affiliations

  1. Institute of Computer Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia

    Huishi Yin & Dietmar Pfahl

Authors
  1. Huishi Yin
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  2. Dietmar Pfahl
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Correspondence to Huishi Yin .

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Editors and Affiliations

  1. University of Innsbruck, Innsbruck, Austria

    Michael Felderer

  2. Technical University Munich, Garching, Germany

    Daniel Méndez Fernández

  3. Brunel University London, Uxbridge, United Kingdom

    Burak Turhan

  4. Pontifical Catholic University of Rio de, Rio de Janeiro, Brazil

    Marcos Kalinowski

  5. University College London, London, United Kingdom

    Federica Sarro

  6. Vienna University of Technology, Vienna, Austria

    Dietmar Winkler

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Yin, H., Pfahl, D. (2017). A Method to Transform Automatically Extracted Product Features into Inputs for Kano-Like Models. In: Felderer, M., Méndez Fernández, D., Turhan, B., Kalinowski, M., Sarro, F., Winkler, D. (eds) Product-Focused Software Process Improvement. PROFES 2017. Lecture Notes in Computer Science(), vol 10611. Springer, Cham. https://doi.org/10.1007/978-3-319-69926-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-69926-4_17

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