Using semantic roles to improve text classification in the requirements domain

Abstract

Engineering activities often produce considerable documentation as a by-product of the development process. Due to their complexity, technical analysts can benefit from text processing techniques able to identify concepts of interest and analyze deficiencies of the documents in an automated fashion. In practice, text sentences from the documentation are usually transformed to a vector space model, which is suitable for traditional machine learning classifiers. However, such transformations suffer from problems of synonyms and ambiguity that cause classification mistakes. For alleviating these problems, there has been a growing interest in the semantic enrichment of text. Unfortunately, using general-purpose thesaurus and encyclopedias to enrich technical documents belonging to a given domain (e.g. requirements engineering) often introduces noise and does not improve classification. In this work, we aim at boosting text classification by exploiting information about semantic roles. We have explored this approach when building a multi-label classifier for identifying special concepts, called domain actions, in textual software requirements. After evaluating various combinations of semantic roles and text classification algorithms, we found that this kind of semantically-enriched data leads to improvements of up to 18% in both precision and recall, when compared to non-enriched data. Our enrichment strategy based on semantic roles also allowed classifiers to reach acceptable accuracy levels with small training sets. Moreover, semantic roles outperformed Wikipedia- and WordNET-based enrichments, which failed to boost requirements classification with several techniques. These results drove the development of two requirements tools, which we successfully applied in the processing of textual use cases.

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Notes

  1. 1.

    For simplicity, a supervised approach is considered.

  2. 2.

    The original requirements documents of the case studies can be downloaded from: http://www.alejandrorago.com.ar/files/assets/dataset-Source.zip.

  3. 3.

    http://www.comp.lancs.ac.uk/~greenwop/tao/.

  4. 4.

    http://sce.uhcl.edu/helm/RUP_course_example/courseregistrationproject/indexcourse.htm.

  5. 5.

    http://sce.uhcl.edu/helm/rationalunifiedprocess/examples/csports/.

  6. 6.

    The complete dataset can be found at http://www.alejandrorago.com.ar/files/assets/dataset-DomainActions.zip.

  7. 7.

    https://code.google.com/p/mate-tools/.

  8. 8.

    http://verbs.colorado.edu/~mpalmer/projects/ace.html.

  9. 9.

    http://www.cs.waikato.ac.nz/ml/weka/.

  10. 10.

    http://mulan.sourceforge.net/index.html.

  11. 11.

    https://code.google.com/p/reassistant/.

  12. 12.

    http://ucrefactoring.googlecode.com/.

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Acknowledgements

This work was partially supported by ANPCyT (Argentina) through PICT Project 2015 No. 2565. The authors are grateful to the doctoral students that helped to manually tag the sentences of the case-studies with DAs. The authors would like to make a special mention to Paula Frade, Miguel Ruival, German Attanasio and Rodrigo Gonzalez for testing the DA classifier and helping us to make adjustments to the implementation. The authors also thank the anonymous reviewers for their feedback that helped to improve the quality of the manuscript.

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Correspondence to Alejandro Rago or Claudia Marcos or J. Andres Diaz-Pace.

Appendix: Description of domain actions

Appendix: Description of domain actions

  • Process: Represents interactions that involve CPU-demanding activities (of a system).

    • Verification: Covers interactions associated from checks of user input, validation of stored information and consistency of data.

    • Calculation: Covers interactions associated to the analysis of information and the synthesis of new results.

    • Communication: Covers all types of interaction with subsystems or foreign software/hardware.

      • Internal: Groups interactions linked to data sharing with subsystems.

      • External: Groups interactions linked to data sharing with other systems.

  • Data: Represents interactions that involve data-related activities, such as persistence and caches operations.

    • Read: Covers interactions associated to retrieval of data.

      • Single: Groups retrieval interactions of single values, often linked to parameters and object representations.

      • Multiple: Groups retrieval interactions of many tuples of information, often materialized as a complex query.

    • Write: Covers interactions associated to the storage of data, by either adding, modifying or removing.

      • Create: Groups interactions aimed at incorporating new information to the system.

      • Update: Groups interactions aimed at altering pre-existing information in the system.

      • Delete: Groups interactions aimed at removing information from the system.

  • Use Case: Represents interactions commonly used in use case scenarios to manage the execution flow.

    • Begin: Groups interactions frequently used to denote the start of a use case flow.

    • End: Groups interactions frequently used to denote the end of a use case flow.

    • Control: Groups interactions to denote the jump from one use case step to another.

  • Input/Output: Represents interactions that involve the communication between the system described and human actors (or other systems).

    • Input: Covers interactions associated to the feeding of information to the system.

      • Entry: Groups interactions related to feeding in data via physical/virtual interface.

      • Selection: Groups interactions related to choosing data from a list of options.

    • Output: Covers interactions associated to the delivery of information to end users.

      • Display: Groups interactions related to the presentation of data on a physical/virtual display.

      • Notification: Groups interactions about status changes or warning messages.

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Rago, A., Marcos, C. & Diaz-Pace, J.A. Using semantic roles to improve text classification in the requirements domain. Lang Resources & Evaluation 52, 801–837 (2018). https://doi.org/10.1007/s10579-017-9406-7

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Keywords

  • Text classification
  • Natural language processing
  • Knowledge representation
  • Semantic enrichment
  • Use case specification