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).
Originalsprache | englisch |
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Titel | 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers |
Seiten | 1265-1270 |
Seitenumfang | 6 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2019 |
Veranstaltung | 31st IEEE International Conference on Tools with Artificial Intelligence : ICTAI 2019 - Portland, USA / Vereinigte Staaten Dauer: 4 Nov. 2019 → 6 Nov. 2019 |
Konferenz
Konferenz | 31st IEEE International Conference on Tools with Artificial Intelligence |
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Kurztitel | ICTAI 2019 |
Land/Gebiet | USA / Vereinigte Staaten |
Ort | Portland |
Zeitraum | 4/11/19 → 6/11/19 |