Introducing Collaboration for Locating Features in Models: Approach and Industrial Evaluation

  • Francisca Pérez
  • Ana C. Marcén
  • Raúl Lapeña
  • Carlos Cetina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10573)

Abstract

Feature Location (FL) is one of the most important tasks in software maintenance and evolution. However, current works on FL neglected the collaboration of different domain experts. This collaboration is especially important in long-living industrial domains where a single domain expert may lack the required knowledge to fully locate a feature, so the collaboration among different domain experts could alleviate this lack of knowledge. In this work, we address collaboration among different domain experts by automatically reformulating their feature descriptions. With our approach, we extend existing FL approaches based on Information Retrieval and Linguistic rules to locate features in models. We evaluate our approach in a real-world case study from our industrial partner, which is a worldwide leader in train manufacturing. We analyze the impact of our approach in terms of recall, precision, and F-Measure. Moreover, we perform a statistical analysis to show that the impact of the results is significant. Our results show that our approach for collaboration boosts the quality of the results of FL.

Keywords

Collaborative information retrieval Feature location Query expansion Model driven engineering 

Notes

Acknowledgements

This work has been partially supported by the Ministry of Economy and Competitiveness (MINECO) through the Spanish National R+D+i Plan and ERDF funds under the project Model-Driven Variability Extraction for Software Product Line Adoption (TIN2015-64397-R).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Francisca Pérez
    • 1
  • Ana C. Marcén
    • 1
  • Raúl Lapeña
    • 1
  • Carlos Cetina
    • 1
  1. 1.SVIT Research GroupUniversidad San JorgeZaragozaSpain

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