A formal soundness proof of region-based memory management for object-oriented paradigm.

Region-based memory management has been proposed as a viable alternative to garbage collection for real-time applications and embedded software. In our previous work we have developed a region type inference algorithm that provides an automatic compile-time region-based memory management for object-oriented paradigm. In this work we present a formal soundness proof of the region type system that is the target of our region inference. More precisely, we prove that the object-oriented programs accepted by our region type system achieve region-based memory management in a safe way. That means, the regions follow a stack-of-regions discipline and regions deallocation never create dangling references in the store and on the program stack. Our contribution is to provide a simple syntactic proof that is based on induction and follows the standard steps of a type safety proof. In contrast the previous safety proofs provided for other region type systems employ quite elaborate techniques

Array operators using multiple dispatch: a design methodology for array implementations in dynamic languages

Arrays are such a rich and fundamental data type that they tend to be built into a language, either in the compiler or in a large low-level library. Defining this functionality at the user level instead provides greater flexibility for application domains not envisioned by the language designer. Only a few languages, such as C++ and Haskell, provide the necessary power to define $n$-dimensional arrays, but these systems rely on compile-time abstraction, sacrificing some flexibility. In contrast, dynamic languages make it straightforward for the user to define any behavior they might want, but at the possible expense of performance. As part of the Julia language project, we have developed an approach that yields a novel trade-off between flexibility and compile-time analysis. The core abstraction we use is multiple dispatch. We have come to believe that while multiple dispatch has not been especially popular in most kinds of programming, technical computing is its killer application. By expressing key functions such as array indexing using multi-method signatures, a surprising range of behaviors can be obtained, in a way that is both relatively easy to write and amenable to compiler analysis. The compact factoring of concerns provided by these methods makes it easier for user-defined types to behave consistently with types in the standard library.Comment: 6 pages, 2 figures, workshop paper for the ARRAY '14 workshop, June 11, 2014, Edinburgh, United Kingdo

Scenic: A Language for Scenario Specification and Scene Generation

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.Comment: 41 pages, 36 figures. Full version of a PLDI 2019 paper (extending UC Berkeley EECS Department Tech Report No. UCB/EECS-2018-8

Aspect-Oriented Programming

Aspect-oriented programming is a promising idea that can improve the quality of software by reduce the problem of code tangling and improving the separation of concerns. At ECOOP'97, the first AOP workshop brought together a number of researchers interested in aspect-orientation. At ECOOP'98, during the second AOP workshop the participants reported on progress in some research topics and raised more issues that were further discussed. \ud \ud This year, the ideas and concepts of AOP have been spread and adopted more widely, and, accordingly, the workshop received many submissions covering areas from design and application of aspects to design and implementation of aspect languages

Modular and composable extensions to smalltalk using composition filters

Current and future trends in computer science require extensions to Smalltalk. Rather than arguing for particular language mechanisms to deal with specific requirements, in this position paper we want to make a case for two requirements that Smalltalk extensions should fulfill. The first is that the extensions must be integrated with Smalltalk without violating its basic object model. The second requirement is that extensions should allow for defining objects that are still adaptable, extensible and reusable, and in particular do not cause inheritance anomalies. We propose the composition filters model as a framework for language extensions that fulfills these criteria. Its applicability to solving various modeling problems is briefly illustrated