Reference : An Active Learning Approach for Improving the Accuracy of Automated Domain Model Extr...
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/37054
An Active Learning Approach for Improving the Accuracy of Automated Domain Model Extraction
English
Arora, Chetan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; SES Networks > Systems Engineering]
Sabetzadeh, Mehrdad mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Nejati, Shiva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Feb-2019
ACM Transactions on Software Engineering and Methodology
Association for Computing Machinery (ACM)
28
1
Yes
International
1049-331X
United States
[en] Requirements Engineering ; Active Learning ; Natural-language Requirements ; Domain Modeling ; Case Study Research
[en] Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural language processing (NLP) have made it possible to automatically extract from requirements most of the information that is relevant to domain model construction. However, alongside the relevant information, NLP extracts from requirements a significant amount of information that is superfluous, i.e., not relevant to the domain model. Our objective in this article is to develop automated assistance for filtering the superfluous information extracted by NLP during domain model extraction. To this end, we devise an active-learning-based approach that iteratively learns from analysts’ feedback over the relevance and superfluousness of the extracted domain model elements, and uses this feedback to provide recommendations for filtering superfluous elements. We empirically evaluate our approach over three industrial case studies. Our results indicate that, once trained, our approach automatically detects an average of ≈ 45% of the superfluous elements with a precision of ≈ 96%. Since precision is very high, the automatic recommendations made by our approach are trustworthy. Consequently, analysts can dispose of a considerable fraction – nearly half – of the superfluous elements with minimal manual work. The results are particularly promising, as they should be considered in light of the non-negligible subjectivity that is inherently tied to the notion of relevance.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Fonds National de la Recherche - FnR ; European Research Council
http://hdl.handle.net/10993/37054
10.1145/3293454
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems
FnR ; FNR11601446 - Reconciling Natural-Language Requirements and Model-Based Specification for Effective Development of Critical Infrastructure Systems (RECONCIS)

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