- Tytuł:
- Mining Non-Functional Requirements using Machine Learning Techniques
- Autorzy:
-
Jindal, Rajni
Malhotra, Ruchika
Jain, Abha
Bansal, Ankita - Powiązania:
- https://bibliotekanauki.pl/articles/2060908.pdf
- Data publikacji:
- 2021
- Wydawca:
- Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
- Tematy:
-
requirement engineering
text mining
non-functional requirements
machine learning
receiver operating characteristics - Opis:
- Background: Non-Functional Requirements (NFR) have a direct impact on the architecture of the system, thus it is essential to identify NFRs in the initial phases of software development. Aim: The work is based on extraction of relevant keywords from NFR descriptions by employing text mining steps and thereafter classifying these descriptions into one of the nine types of NFRs. Method: For each NFR type, keywords are extracted from a set of pre-categorized specifications using Information-Gain measure. Then models using 8 Machine Learning (ML) techniques are developed for classification of NFR descriptions. A set of 15 projects (containing 326 NFR descriptions) developed by MS students at DePaul University are used to evaluate the models. Results: The study analyzes the performance of ML models in terms of classification and misclassification rate to determine the best model for predicting each type NFR descriptions. The Naïve Bayes model has performed best in predicting “maintainability” and “availability” type of NFRs. Conclusion: The NFR descriptions should be analyzed and mapped into their corresponding NFR types during the initial phases. The authors conducted cost benefit analysis to appreciate the advantage of using the proposed models.
- Źródło:
-
e-Informatica Software Engineering Journal; 2021, 15, 1; 85--114
1897-7979 - Pojawia się w:
- e-Informatica Software Engineering Journal
- Dostawca treści:
- Biblioteka Nauki
Artykuł