New Approaches to the Identification of Dependencies between Requirements

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Abstract

There is a high demand for intelligent decision support systems which assist stakeholders in requirements engineering tasks. Examples of such tasks are the elicitation of requirements, release planning, and the identification of requirement-dependencies. In particular, the detection of dependencies between requirements is a major challenge for stakeholders. In this paper, we present two content-based recommendation approaches which automatically detect and recommend such dependencies. The first approach identifies potential dependencies between requirements which are defined on a textual level by exploiting document classification techniques (based on Linear SVM, Naive Bayes, Random Forest, and k-Nearest Neighbors). This approach uses two different feature types (TF-IDF features vs. probabilistic features). The second recommendation approach is based on Latent Semantic Analysis and defines the baseline for the evaluation with a real-world data set. The evaluation shows that the recommendation approach based on Random Forest using probabilistic features achieves the best prediction quality of all approaches (F1: 0.89).
Original languageEnglish
Title of host publication 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)
PublisherInstitute of Electrical and Electronics Engineers
Pages1265-1270
Number of pages6
DOIs
Publication statusPublished - 2019
Event 2019 IEEE 31st International Conference on Tools with Artificial Intelligence - Portland, United States
Duration: 4 Nov 20196 Nov 2019

Conference

Conference 2019 IEEE 31st International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2019
CountryUnited States
CityPortland
Period4/11/196/11/19

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  • Cite this

    Samer, R., Stettinger, M., Atas, M., Felfernig, A., Ruhe, G., & Deshpande, G. (2019). New Approaches to the Identification of Dependencies between Requirements. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1265-1270). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICTAI.2019.00-91