Advertisement

Supporting Agile Software Development by Natural Language Processing

  • Barbara Plank
  • Thomas Sauer
  • Ina Schaefer
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 379)

Abstract

Agile software development puts more emphasis on working programs than on documentation. However, this may cause complications from the management perspective when an overview of the progress achieved within a project needs to be provided. In this paper, we outline the potential for applying natural language processing (NLP) in order to support agile development. We point out that using NLP, the artifacts created during agile software development activities can be traced back to the requirements expressed in user stories. This allows determining how far the project has progressed in terms of realized requirements.

Keywords

Agile Software Development Project Management Machine Learning Natural Language Processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ambriola, V., Gervasi, V.: On the systematic analysis of natural language requirements with circe. Autom. Softw. Eng. 13(1), 107–167 (2006)CrossRefGoogle Scholar
  2. 2.
    Beck, K.: Test Driven Development By Example. Addison-Wesley (2002)Google Scholar
  3. 3.
    Cleland-Huang, J., Settimi, R., Romanova, E.: Best practices for automated traceability. Computer 40(6), 27–35 (2007)CrossRefGoogle Scholar
  4. 4.
    Cohn, M.: User Stories Applied for Agile Software Development. Addison-Wesley (2004)Google Scholar
  5. 5.
    Collins, M., Duffy, N.: Convolution kernels for natural language. In: Proceedings of NIPS (2001)Google Scholar
  6. 6.
    Johnson, P.M., Kou, H., Paulding, M., Zhang, Q., Kagawa, A., Yamashita, T.: Improving software development management through software project telemetry. IEEE Software 22(4), 76–85 (2005)CrossRefGoogle Scholar
  7. 7.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing. Prentice Hall Series in Artificial Intelligence. Prentice Hall (2008)Google Scholar
  8. 8.
    Landhäußer, M., Genaid, A.: Connecting user stories and code for test development. In: Proc. of the 3rd International Workshop on Recommendation Systems for Software Engineering (RSSE 2012), pp. 33–37 (2012)Google Scholar
  9. 9.
    Madani, N., Guerrouj, L., Di Penta, M., Gueheneuc, Y., Antoniol, G.: Recognizing words from source code identifiers using speech recognition techniques. In: 2010 14th European Conference on Software Maintenance and Reengineering (CSMR), pp. 68–77 (March 2010)Google Scholar
  10. 10.
    Manning, C., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press (2008)Google Scholar
  11. 11.
    Moschitti, A.: A study on convolution kernels for shallow semantic parsing. In: Proceedings of the 42nd Meeting of the ACL, Barcelona, Spain (2004)Google Scholar
  12. 12.
    Ratanotayanon, S., Sim, S.E., Gallardo-Valencia, R.: Supporting program comprehension in agile with links to user stories. In: AGILE Conference, pp. 26–32. IEEE Computer Society (2009)Google Scholar
  13. 13.
    Sawyer, P., Rayson, P., Garside, R.: Revere: Support for requirements synthesis from documents. Information Systems Frontiers 4(3), 343–353 (2002)CrossRefGoogle Scholar
  14. 14.
    Schwaber, K., Beedle, M.: Agile Software Development with Scrum. Prentice Hall (2001)Google Scholar
  15. 15.
    Winkler, S., von Pilgrim, J.: A survey of traceability in requirements engineering and model-driven development. Software and Systems Modeling 9, 529–565 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Barbara Plank
    • 1
  • Thomas Sauer
    • 2
  • Ina Schaefer
    • 3
  1. 1.University of TrentoItaly
  2. 2.rjm business solutions GmbHLampertheimGermany
  3. 3.Technische Universität BraunschweigGermany

Personalised recommendations