A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model

Daniel Siahaan*, Brian Rizqi Paradisiaca Darnoto

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 an 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.

Original languageEnglish
Article number87
Issue number4
Publication statusPublished - Dec 2022


  • angle-based outlier detection
  • irrelevant software requirements
  • multiphilda
  • noise
  • percentile-based outlier detection


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