Self-adaptive software systems monitor their state and then adapt when certain conditions are met, guided by a global utility function. In prior work we developed algorithms and conducted a post-hoc analysis demonstrating the possibility of adapting to software failures by judiciously changing configurations. In this paper we present the REFRACT framework that realizes this idea in practice by building on the self-adaptive Rainbow architecture. REFRACT extends Rainbow with new components and algorithms targeting failure avoidance. We use REFRACT in a case study running four independently executing Firefox clients with 36 passing test cases and 7 seeded faults. The study show that workarounds for all but one of the seeded faults are found and the one that is not found never fails -it is guarded from failing by a related workaround. Moreover, REFRACT finds workarounds for eight configuration-related unseeded failures from tests that were expected to pass (and did under the default configuration). Finally, the data show that when a failure and its workaround are found, configuration guards prevent the failure from appearing again. In a simulation lasting 24 hours we see over 150 guard activations and no failures with workarounds remaining beyond 16 hours.

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Software product line engineering is a best practice for managing reuse in families of software systems. In this work, we explore the use of product line engineering in the emerging programming domain of synthetic biology. In synthetic biology, living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse these programs. As a first step towards this goal, we perform a domain engineering case study that leverages an open-source repository of more than 45,000 reusable DNA parts. We are able to identify features and their related artifacts, all of which can be composed to make different programs. We demonstrate that we can successfully build feature models representing families for two commonly engineered functions. We then analyze an existing synthetic biology case study and demonstrate how product line engineering can be beneficial in this domain. CCS CONCEPTS • Software and its engineering → Software product lines; • Applied computing → Systems biology.

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