Szczegóły publikacji
Opis bibliograficzny
Empirical assessment of machine learning techniques for software requirements risk prediction / Rashid Naseem, Zain Shaukat, Muhammad Irfan, Muhammad Arif Shah, Arshad Ahmad, Fazal Muhammad, Adam GŁOWACZ, Larisa Dunai, Jose Antonino-Daviu, Adel Sulaiman // Electronics [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2079-9292. — 2021 — vol. 10 iss. 2 art. no. 168, s. 1–19. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 17–19, Abstr. — Publikacja dostępna online od: 2021-01-14
Autorzy (10)
- Naseem Rashid
- Shaukat Zain
- Irfan Muhammad
- Arif Shah Muhammad
- Ahmad Arshad
- Muhammad Fazal
- AGHGłowacz Adam
- Dunai Larisa
- Antonino-Daviu Jose Alfonso
- Sulaiman Adel
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 132142 |
|---|---|
| Data dodania do BaDAP | 2021-01-16 |
| Tekst źródłowy | URL |
| DOI | 10.3390/electronics10020168 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Electronics |
Abstract
Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results.
