Decision support for the software product line domain engineering lifecycle

Ebrahim Bagheri, Faezeh Ensan, Dragan Gasevic

Research output: Contribution to journalArticleResearchpeer-review

37 Citations (Scopus)


Software product line engineering is a paradigm that advocates the reusability of software engineering assets and the rapid development of new applications for a target domain. These objectives are achieved by capturing the commonalities and variabilities between the applications of the target domain and through the development of comprehensive and variability-covering feature models. The feature models developed within the software product line development process need to cover the relevant features and aspects of the target domain. In other words, the feature models should be elaborate representations of the feature space of that domain. Given that feature models, i.e., software product line feature models, are developed mostly by domain analysts by sifting through domain documentation, corporate records and transcribed interviews, the process is a cumbersome and error-prone one. In this paper, we propose a decision support platform that assists domain analysts throughout the domain engineering lifecycle by: (1) automatically performing natural language processing tasks over domain documents and identifying important information for the domain analysts such as the features and integrity constraints that exist in the domain documents; (2) providing a collaboration platform around the domain documents such that multiple domain analysts can collaborate with each other during the process using a Wiki; (3) formulating semantic links between domain terminology with external widely used ontologies such as WordNet in order to disambiguate the terms used in domain documents; and (4) developing traceability links between the unstructured information available in the domain documents and their formal counterparts within the formal feature model representations. Results obtained from our controlled experimentations show that the decision support platform is effective in increasing the performance of the domain analysts during the domain engineering lifecycle in terms of both the coverage and accuracy measures.

Original languageEnglish
Pages (from-to)335-377
Number of pages43
JournalAutomated Software Engineering
Issue number3
Publication statusPublished - 1 Sept 2012
Externally publishedYes


  • Domain engineering
  • Feature models
  • NLP model inference
  • Software product lines

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