Cross-Project Transfer Learning for Reliability Prediction

While ML approaches to reliability estimation like those by Farooq & Chishti (2024) show promise, they typically require substantial historical failure data from the specific project being analyzed. This creates a chicken-and-egg problem: you need reliability data to predict reliability, but new projects have no history. This research proposes using transfer learning to address this limitation. By training models on large datasets of completed projects (like the Apache data used by Saraf et al., 2021), then fine-tuning on minimal data from new projects, we could provide meaningful reliability estimates much earlier in the development lifecycle. The novelty lies in specifically addressing the "cold start" problem in software reliability prediction, which none of the current ML approaches seem to tackle directly. This differs from traditional SRGM selection approaches like Garg et al.'s (2022) CODAS-E method by not just selecting the best model, but actively transferring learning across projects. The approach could also incorporate the bathtub-shaped failure patterns identified by Nafreen & Fiondella (2021) as transferable features across project domains.

References:

1. A Family of Software Reliability Models with Bathtub-Shaped Fault Detection Rate. Maskura Nafreen, L. Fiondella (2021). International Journal of Reliability, Quality and Safety Engineering (IJRQSE).
2. Modelling reliability growth for multi‐version open source software considering varied testing and debugging factors. I. Saraf, Javaid Iqbal, A. Shrivastava, Shozab Khurshid (2021). Quality and Reliability Engineering International.
3. Decision Support System for Optimal Selection of Software Reliability Growth Models Using a Hybrid Approach. R. Garg, Supriya Raheja, R. Garg (2022). IEEE Transactions on Reliability.
4. Software Reliability Growth Testing Effort Function Model Dependent on Machine Learning and Neural Network Algorithm. Refath Farooq, M. Chishti (2024). 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS).