Language Learning for Verification of Configuration Files
Abstract : Software failures resulting from configuration errors have become commonplace as modern software systems grow increasingly large and more complex. The lack of language constructs in configuration files, such as types and specifications, has directed the focus of a configuration file verification towards building post-failure error diagnosis tools. In addition, the existing tools are generally language specific, requiring the user to define the language model and explicit rules to check. In this talk, we propose a framework which analyzes datasets of configuration files and derives rules for building a language model from the given dataset. The resulting language model can be used to verify new configuration files and detect errors in them. We will discuss the implementation, ConfigC, of this framework - as well as the underlying model and how it might be extended in the future.
Bio: Mark Santolucito is a Computer Science PhD student at Yale University, where he is studying programming languages with Ruzica Piskac. Mark originally started under the supervision of Paul Hudak, working on interactive computer music in and Functional Reactive Programming (FRP). Mark is now working with Ruzica Piskac on various forms of program synthesis. Recently, their work has focused on synthesis of FRP programs from logical specifications. Mark also graduated Cum Laude from Amherst College with a BA in both Computer Science and Music, where he was awarded the Best Computer Science Thesis Award in addition to the Lerner Piano Prize upon graduation.
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