A Bayesian network framework for fusing spectrum-based fault localization and forward slicing
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[EMBARGOED UNTIL 12/01/2026] Fault localization techniques based on spectrum analysis (SBFL), such as Ochiai, Tarantula, D*, and Barinel, have achieved state-of-the-art performance in many benchmarks, yet their effectiveness can degrade in complex, multi-fault programs where coverage information alone is insufficient. Program slicing, in contrast, provides precise structural dependencies but often lacks statistical fault evidence. In this thesis, a generalizable probabilistic fusion framework is presented that integrates SBFL metrics with forward slicing through Bayesian networks. Unlike prior hybrids that combine these techniques via fixed heuristics, this approach models the probabilistic relationships between suspiciousness scores and slicing reachability, enabling principled evidence integration. This method is evaluated on multi-fault scenarios using Defects4J and an extended dataset of combined-bug projects, demonstrating consistent improvements over SBFL baselines. The results show that this framework not only boosts accuracy in traditional settings but also substantially improves localization in multi-fault contexts, suggesting a viable path toward more robust and adaptable fault localization in practice.
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M.S.