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Computational tools have become increasingly important to the advancement of biological research. Despite the existence of data-sharing principles such as FAIR, there has been little attention paid to ensuring that computational tools provide a platform to compare experimental results and data across different studies. Tools often are lightly documented, non-extensible, and provide different levels of accuracy of their representation. This lack of standardization in biological research reduces the potential power for new insight and discovery and makes it hard for biologists to experiment, compare, and trust results between different studies. In this poster we present our experience using four tools that perform flux balance analysis, on a set of different metabolic models. We frame our work around a proposed principle, akin to FAIR, aimed at bioinformatics tools. We call this CORE (Comparable, Open, Reliable and Extensible), and find the biggest challenges to be with comparability between tools. We also find that while the tools are all open source, without a deep understanding of the code base, they have insufficient openness. We needed to reach out to developers to resolve many of our questions, and we were still left with unexpected behaviors. We present lessons learned as a path to future improvement using the CORE principles. 

Small uncrewed aerial systems, sUAS, provide an invaluable resource for performing a variety of surveillance, search, and delivery tasks in remote or hostile terrains which may not be accessible by other means. Due to the critical role sUAS play in these situations, it is vital that they are well configured in order to ensure a safe and stable flight. However, it is not uncommon for mistakes to occur in configuration and calibration, leading to failures or incomplete missions. To address this problem, we propose a set of self-adaptive mechanisms and implement them into a self-adaptive framework, CICADA, for Controller Instability-preventing Configuration Aware Drone Adaptation. CICADA dynamically detects unstable drone behavior during flight and adapts to mitigate this threat. We have built a prototype of CICADA using a popular open source sUAS simulator and experimented with a large number of different configurations. Experimental results show that CICADA’s adaptations reduce controller instability and enable the sUAS to recover from a significant number of poor configurations. In cases where we cannot complete the intended mission, invoking alternative adaptations may still help by allowing the vehicle to loiter or land safely in place, avoiding potentially catastrophic crashes. 

Configurable software makes up most of the software in use today. Configurability, i.e., the ability of software to be customized without additional programming, is pervasive, and due to the criticality of problems caused by misconfiguration, it has been an active topic researched by investigators in multiple, diverse areas. This broad reach of configurability means that much of the literature and latest results are dispersed, and researchers may not be collaborating or be aware of similar problems and solutions in other domains. We argue that this lack of a common ground leads to a missed opportunity for synergy between research domains and the synthesis of efforts to tackle configurability problems. In short, configurability cuts across software as a whole and needs to be treated as a first class programming element. To provide a foundation for addressing these concerns we make suggestions on how to bring the communities together and propose a common model of configurability and a platform, ACCORD, to facilitate collaboration among researchers and practitioners. 

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. 

Information leaks in software can unintentionally reveal private data, yet they are hard to detect and fix. Although several methods have been proposed to detect leakage, such as static verification-based approaches, they require specialist knowledge, and are time-consuming. Recently, we introduced HyperGI, a dynamic, hypertest-based approach that can detect and produce potential fixes for hyperproperty violations. In particular, we focused on violations of the noninterference property, as it results in information flow leakage. Our instantiation of HyperGI was able to detect and reduce leakage in three small programs. Its fitness function tried to balance information leakage and program correctness but, as we pointed out, there may be tradeoffs between keeping program semantics and reducing information leakage that require developer decisions. In this work we ask if it is possible to automatically detect and repair information leakage in more realistic programs without requiring specialist knowledge. We instantiate a multi-objective version of HyperGI in a tool, called LeakReducer, which explicitly encodes the tradeoff between program correctness and information leakage. We apply LeakReducer to six leaky programs, including the well-known Heartbleed bug. LeakReducer is able to detect leakage in all, in contrast to state-of-the-art fuzzers, detecting leakage in only two programs. Moreover, LeakReducer is able to reduce leakage in all subjects, with comparable results to previous work, while scaling to much larger software. 

Maintaining confidential information control in software is a persistent security problem where failure means secrets can be revealed via program behaviors. Information flow control techniques traditionally have been based on static or symbolic analyses — limited in scalability and specialized to particular languages. When programs do leak secrets there are no approaches to automatically repair them unless the leak causes a functional test to fail. We present our vision for HyperGI, a genetic improvement framework that detects, localizes and repairs information leakage. Key elements of HyperGI include (1) the use of two orthogonal test suites, (2) a dynamic leak detection approach which estimates and localizes potential leaks, and (3) a repair component that produces a candidate patch using genetic improvement. We demonstrate the successful use of HyperGI on several programs with no failing functional test cases. We manually examine the resulting patches and identify trade-offs and future directions for fully realizing our vision.