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Automatic recommendation to omitted steps in use case specification

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Abstract

Completeness is one of the key attributes for a high-quality software requirements specification. Although incomplete requirements frequently occur in the requirements specification, it is rarely discovered. This turns out to be one of the major causes of software project failure. In order to handle this issue, this paper proposes an automatic approach to recommending omitted steps in a use case-based requirements specification. First, we automatically extract diverse scenario patterns by using the verb clustering algorithm and scenario flow graphs. Based on the scenario patterns, our approach detects omitted steps of user’s scenarios by the pattern matching algorithm and automatically recommends appropriate steps for the omitted parts. For validation of our approach, we have developed tool support, named ScenarioAmigo, and collected 231 use case specifications composing of 1874 scenario steps from 12 academic or proprietary projects. We first carried out the preliminary study to decide appropriate thresholds and weights. Then, we conducted three experiments as a quantitative performance evaluation. First, the cross-validation for the collected scenarios shows the 76% precision and 80% recall. Second, the comparison of recall of ScenarioAmigo to that of human experts obtained the 20% higher score. As the last experiment, we compared the result of ScenarioAmigo and human experts in terms of severity of each scenario and found that our approach could recommend normal as well as important scenarios, compared to the human experts.

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Notes

  1. We have applied the Stanford Parser (https://nlp.stanford.edu/software/lex-parser.shtml), which is one of the most popular and precise natural language parser.

  2. Note that appropriate thresholds and weights that we mention here and the remaining part in Sect. 3 are obtained in the preliminary study of Sect. 5 by sensitivity analysis.

  3. In Algorithm 1, we considered that two verbs are synonyms when the similarity measured by Eq. 1 is over 0.2. See the line 9 in Algorithm 1.

  4. Based on sensitivity analysis with several functions (e.g., Linear, Sigmoid, Binary Step, Gaussian) that can change the data range from 0 to 1, we recognize that the sigmoid function produces the highest performance.

  5. The entire sets of the candidate scenario pattern indexes are {0,1,2,3}, {0,1,2,3,4}, {0,1,2,3,4,5}, {0,1,2,3,4,5,6}, {1,2,3,4}, {1,2,3,4,5}, {1,2,3,4,5,6}, {2,3,4,5}, {2,3,4,5,6} and {3,4,5,6}.

  6. The source code of ScenarioAmigo is open at the Github with the URL—https://github.com/maniara/ScenarioAmigo.

  7. We used the rand() function of MySQL to select the sample scenarios.

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Acknowledgements

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2016-0-00313, IITP-2017-0-01628) supervised by the IITP (Institute for Information & communications Technology Promotion).

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Authors and Affiliations

  1. Finotek Co., Ltd., Seoul, Republic of Korea

    Deokyoon Ko

  2. Department of Software Engineering, CAIIT, Chonbuk National University, 567 Baekje-daero, deokjin-gu, Jeonju-si, Jeollabuk-do, 54896, Republic of Korea

    Suntae Kim

  3. Department of Computer Science and Engineering, Sogang University, Seoul, Republic of Korea

    Sooyong Park

Authors
  1. Deokyoon Ko
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  2. Suntae Kim
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  3. Sooyong Park
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Corresponding author

Correspondence to Suntae Kim.

Appendices

Appendix 1: List of scenario patterns

See Tables 8, 9, 10 and 11.

Table 8 List I of scenario patterns identified by our approach
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Table 9 List II of scenario patterns identified by our approach
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Table 10 List III of scenario patterns identified by our approach
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Table 11 List IV of scenario patterns identified by our approach
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Appendix 2: List of verbs in verb synonym dictionary

See Table 12.

Table 12 List of verbs in verb synonym dictionary
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Ko, D., Kim, S. & Park, S. Automatic recommendation to omitted steps in use case specification. Requirements Eng 24, 431–458 (2019). https://doi.org/10.1007/s00766-018-0288-z

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