The Three Pillars of Machine Programming

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are:(i) intention,(ii) invention, and (iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software

Bidirectional Type Checking for Relational Properties

Relational type systems have been designed for several applications including information flow, differential privacy, and cost analysis. In order to achieve the best results, these systems often use relational refinements and relational effects to maximally exploit the similarity in the structure of the two programs being compared. Relational type systems are appealing for relational properties because they deliver simpler and more precise verification than what could be derived from typing the two programs separately. However, relational type systems do not yet achieve the practical appeal of their non-relational counterpart, in part because of the lack of a general foundations for implementing them. In this paper, we take a step in this direction by developing bidirectional relational type checking for systems with relational refinements and effects. Our approach achieves the benefits of bidirectional type checking, in a relational setting. In particular, it significantly reduces the need for typing annotations through the combination of type checking and type inference. In order to highlight the foundational nature of our approach, we develop bidirectional versions of several relational type systems which incrementally combine many different components needed for expressive relational analysis.Comment: 14 page

Cerberus-BMC: A Principled Reference Semantics and Exploration Tool for Concurrent and Sequential C

C remains central to our infrastructure, making verification of C code an essential and much-researched topic, but the semantics of C is remarkably complex, and important aspects of it are still unsettled, leaving programmers and verification tool builders on shaky ground. This paper describes a tool, Cerberus-BMC, that for the first time provides a principled reference semantics that simultaneously supports (1) a choice of concurrency memory model (including substantial fragments of the C11, RC11, and Linux kernel memory models), (2) a modern memory object model, and (3) a well-validated thread-local semantics for a large fragment of the language. The tool should be useful for C programmers, compiler writers, verification tool builders, and members of the C/C++ standards committees

Extending Intel-x86 consistency and persistency: formalising the semantics of Intel-x86 memory types and non-temporal stores

Existing semantic formalisations of the Intel-x86 architecture cover only a small fragment of its available features that are relevant for the consistency semantics of multi-threaded programs as well as the persistency semantics of programs interfacing with non-volatile memory. We extend these formalisations to cover: (1) non-temporal writes, which provide higher performance and are used to ensure that updates are flushed to memory; (2) reads and writes to other Intel-x86 memory types, namely uncacheable, write-combined, and write-through; as well as (3) the interaction between these features. We develop our formal model in both operational and declarative styles, and prove that the two characterisations are equivalent. We have empirically validated our formalisation of the consistency semantics of these additional features and their subtle interactions by extensive testing on different Intel-x86 implementations

Robustness Against Transactional Causal Consistency

Distributed storage systems and databases are widely used by various types of applications. Transactional access to these storage systems is an important abstraction allowing application programmers to consider blocks of actions (i.e., transactions) as executing atomically. For performance reasons, the consistency models implemented by modern databases are weaker than the standard serializability model, which corresponds to the atomicity abstraction of transactions executing over a sequentially consistent memory. Causal consistency for instance is one such model that is widely used in practice. In this paper, we investigate application-specific relationships between several variations of causal consistency and we address the issue of verifying automatically if a given transactional program is robust against causal consistency, i.e., all its behaviors when executed over an arbitrary causally consistent database are serializable. We show that programs without write-write races have the same set of behaviors under all these variations, and we show that checking robustness is polynomial time reducible to a state reachability problem in transactional programs over a sequentially consistent shared memory. A surprising corollary of the latter result is that causal consistency variations which admit incomparable sets of behaviors admit comparable sets of robust programs. This reduction also opens the door to leveraging existing methods and tools for the verification of concurrent programs (assuming sequential consistency) for reasoning about programs running over causally consistent databases. Furthermore, it allows to establish that the problem of checking robustness is decidable when the programs executed at different sites are finite-state