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Fault localization is essential to software maintenance tasks such as testing and automated program repair. Many fault localization techniques have been developed, the most common of which are spectrum-based. Most techniques have been designed for traditional programming paradigms that map passing and failing test cases to lines or branches of code, hence specialized programming paradigms which utilize different code abstractions may fail to localize well. In this paper, we study fault localization in the context of a class of programs, molecular programs. Recent research has designed automated testing and repair frameworks for these programs but has ignored the importance of fault localization. As we demonstrate, using existing spectrum-based approaches may not provide much information. Instead we propose a novel approach, Traceback, that leverages temporal trace data. In an empirical study on a set of 89 faulty program variants, we demonstrate that Traceback provides between a 32-90\% improvement in localization over reaction-based mapping, a direct translation of spectrum-based localization. We see little difference in parameter tuning of Traceback when all tests, or only code-based (invariant) tests are used, however the best depth and weight parameters vary when using specification based tests, which can be either functional or metamorphic. Overall, invariant-based tests provide the best localization results (either alone or in combination with others), followed by metamorphic and then functional tests. 

The use of non-traditional computing devices is growing rapidly. One paradigm of interest is chemical reaction networks (CRNs) which can model and use chemical interactions for computation. These CRNs are used to develop programs at the nanoscale for applications such as intelligent drug delivery. In practice, these programs are developed in simulation environments, and then compiled into physical systems. A challenge when designing CRNs for computation is the lack of techniques to verify and validate correctness. In this work, we adapt software testing and repair techniques for use in this domain. In initial work, we designed a testing framework to handle the challenges presented by CRN programs; this includes distributed computation and stochastic behavior. We extended this framework to implement automated program repair of CRN models and automated test generation via program invariants. For future work, we will develop a notion of fault localization for these programs, develop a theory of mutation generation, and address issues regarding flakiness present in this computing paradigm.