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Register a new userRoosterize: Suggesting Lemma Names for Coq Verification Projects Using Deep Learning
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Added by
Pengyu Nie
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Naming conventions are an important concern in large verification projects using proof assistants, such as Coq. In particular, lemma names are used by proof engineers to effectively understand and modify Coq code. However, providing accurate and informative lemma names is a complex task, which is currently often carried out manually. Even when lemma naming is automated using rule-based tools, generated names may fail to adhere to important conventions not specified explicitly. We demonstrate a toolchain, dubbed Roosterize, which automatically suggests lemma names in Coq projects. Roosterize leverages a neural network model trained on existing Coq code, thus avoiding manual specification of naming conventions. To allow proof engineers to conveniently access suggestions from Roosterize during Coq project development, we integrated the toolchain into the popular Visual Studio Code editor. Our evaluation shows that Roosterize substantially outperforms strong baselines for suggesting lemma names and is useful in practice. The demo video for Roosterize can be viewed at: https://youtu.be/HZ5ac7Q14rc.
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We implement extraction of Coq programs to functional languages based on MetaCoqs certified erasure. We extend the MetaCoq erasure output language with typing information and use it as an intermediate representation, which we call $lambda^T_square$. We complement the extraction functionality with a full pipeline that includes several standard transformations (eta-expansion, inlining, etc) implemented in a proof-generating manner along with a verified optimisation pass removing unused arguments. We prove the pass correct wrt. a conventional call-by-value operational semantics of functional languages. From the optimised $lambda^T_square$ representation, we obtain code in two functional smart contract languages (Liquidity and CameLIGO), the functional language Elm, and a subset of the multi-paradigm language for systems programming Rust. Rust is currently gaining popularity as a language for smart contracts, and we demonstrate how our extraction can be used to extract smart contract code for the Concordium network. The development is done in the context of the ConCert framework that enables smart contract verification. We contribute with two verified real-world smart contracts (boardroom voting and escrow), which we use, among other examples, to exemplify the applicability of the pipeline. In addition, we develop a verified web application and extract it to fully functional Elm code. In total, this gives us a way to write dependently typed programs in Coq, verify, and then extract them to several target languages while retaining a small trusted computing base of only MetaCoq and the pretty-printers into these languages.
This paper is concerned with the form of typed name binding used by the FreshML family of languages. Its characteristic feature is that a name binding is represented by an abstract (name,value)-pair that may only be deconstructed via the generation of fresh bound names. The paper proves a new result about what operations on names can co-exist with this construct. In FreshML the only observation one can make of names is to test whether or not they are equal. This restricted amount of observation was thought necessary to ensure that there is no observable difference between alpha-equivalent name binders. Yet from an algorithmic point of view it would be desirable to allow other operations and relations on names, such as a total ordering. This paper shows that, contrary to expectations, one may add not just ordering, but almost any relation or numerical function on names without disturbing the fundamental correctness result about this form of typed name binding (that object-level alpha-equivalence precisely corresponds to contextual equivalence at the programming meta-level), so long as one takes the state of dynamically created names into account.
There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved. A recent study [1] presented a time contrastive learning (TCL) concept to explore the non-stationarity of brain signals for classification of brain states. Speech signals have similar non-stationarity property, and TCL further has the advantage of having no need for labeled data. We therefore present a TCL based BN feature extraction method. The method uniformly partitions each speech utterance in a training dataset into a predefined number of multi-frame segments. Each segment in an utterance corresponds to one class, and class labels are shared across utterances. DNNs are then trained to discriminate all speech frames among the classes to exploit the temporal structure of speech. In addition, we propose a segment-based unsupervised clustering algorithm to re-assign class labels to the segments. TD-SV experiments were conducted on the RedDots challenge database. The TCL-DNNs were trained using speech data of fixed pass-phrases that were excluded from the TD-SV evaluation set, so the learned features can be considered phrase-independent. We compare the performance of the proposed TCL bottleneck (BN) feature with those of short-time cepstral features and BN features extracted from DNNs discriminating speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels and boundaries are generated by three different automatic speech recognition (ASR) systems. Experimental results show that the proposed TCL-BN outperforms cepstral features and speaker+pass-phrase discriminant BN features, and its performance is on par with those of ASR derived BN features. Moreover,....
A quantum circuit is a computational unit that transforms an input quantum state to an output one. A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by it. However, when the number of qubits increases, the matrix dimension grows exponentially and the computation becomes intractable. In this paper, we propose a symbolic approach to reasoning about quantum circuits. It is based on a small set of laws involving some basic manipulations on vectors and matrices. This symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq, as demonstrated with some typical examples.
The Message Passing Interface specification (MPI) defines a portable message-passing API used to program parallel computers. MPI programs manifest a number of challenges on what concerns correctness: sent and expected values in communications may not match, resulting in incorrect computations possibly leading to crashes; and programs may deadlock resulting in wasted resources. Existing tools are not completely satisfactory: model-checking does not scale with the number of processes; testing techniques wastes resources and are highly dependent on the quality of the test set. As an alternative, we present a prototype for a type-based approach to programming and verifying MPI like programs against protocols. Protocols are written in a dependent type language designed so as to capture the most common primitives in MPI, incorporating, in addition, a form of primitive recursion and collective choice. Protocols are then translated into Why3, a deductive software verification tool. Source code, in turn, is written in WhyML, the language of the Why3 platform, and checked against the protocol. Programs that pass verification are guaranteed to be communication safe and free from deadlocks. We verified several parallel programs from textbooks using our approach, and report on the outcome.
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