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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. 

Chemical reaction networks (CRNs) are an important tool for molecular programming. This field is rapidly expanding our ability to deploy computer programs into biological systems for various applications. However, CRNs are also difficult to work with due to their massively parallel nature, leading to the need for higher-level languages that allow for more straightforward computation with CRNs. Recently, research has been conducted into various higher-level languages for deterministic CRNs but modeling CRN parallelism, managing error accumulation, and finding natural CRN representations are ongoing challenges. We introduce Reactamole, a higher-level language for deterministic CRNs that utilizes the functional reactive programming (FRP) paradigm to represent CRNs as a reactive dataflow network. Reactamole equates a CRN with a functional reactive program, implementing the key primitives of the FRP paradigm directly as CRNs. The functional nature of Reactamole makes reasoning about molecular programs easier, and its strong static typing allows us to ensure that a CRN is well-formed by virtue of being well-typed. In this paper, we describe the design of Reactamole and how we use CRNs to represent the common datatypes and operations found in FRP. We demonstrate the potential of this functional reactive approach to molecular programming by giving an extended example where a CRN is constructed using FRP to modulate and demodulate an amplitude-modulated signal. We also show how Reactamole can be used to specify abstract CRNs whose structure depends on the reactions and species of its input, allowing users to specify more general CRN behaviors. 

Chemical reaction networks (CRNs) are an important tool for molecular programming, a field that is rapidly expanding our ability to deploy computer programs into biological systems for a variety of applications. However, CRNs are also difficult to work with due to their massively parallel nature, leading to the need for higher-level languages that allow for easier computation with CRNs. Recently, research has been conducted into a variety of higher-level languages for deterministic CRNs but modeling CRN parallelism, managing error accumulation, and finding natural CRN representations are ongoing challenges. We introduce Reactamole, a higher-level language for deterministic CRNs that utilizes the functional reactive programming (FRP) paradigm to represent CRNs as a reactive dataflow network. Reactamole equates a CRN with a functional reactive program, implementing the key primitives of the FRP paradigm directly as CRNs. The functional nature of Reactamole makes reasoning about molecular programs easier, and its strong static typing allows us to ensure that a CRN is well-formed by virtue of being well-typed. In this paper, we describe the design of Reactamole and how we use CRNs to represent the common datatypes and operations found in FRP. We also demonstrate the potential of this functional reactive approach to molecular programming by giving an extended example where a CRN is constructed using FRP to modulate and demodulate an amplitude modulated signal.