Examining the Performance of Mamba Tab for Software Defect Prediction

Software Defect Prediction (SDP) is crucial for ensuring the quality of software systems. While traditional and emerging transformer-based models are well researched in SDP, recent advancement of state space model - Mamba, has gained popularity in various domains. This research explores the potential of state space model in the SDP domain for efficiently extracting effective representations from data. Inspired by Mamba Tab's lightweight, scalable, and generalizable nature, we experimented to evaluate its performance in the context of SDP. Our experiment involved several datasets and compared Mamba Tab with traditional machine learning, state-of-the-art deep learning, and transformer - based models. The experimental results demonstrate that Mamba Tab outperforms other baseline models across most key metrics and time complexity analysis, further confirming its efficiency. Cohen's d effect size analysis strengthens this advantage, showing large and medium effect sizes for Mamba Tab on these metrics. These findings highlight Mamba Tab's effectiveness, efficiency and generalizability, in the context of SDP.