Siahaan, D.; Darnoto, B.R.P. A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model. Informatics2022, 9, 87.
Siahaan, D.; Darnoto, B.R.P. A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model. Informatics 2022, 9, 87.
Siahaan, D.; Darnoto, B.R.P. A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model. Informatics2022, 9, 87.
Siahaan, D.; Darnoto, B.R.P. A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model. Informatics 2022, 9, 87.
Abstract
Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
