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Register a new userConcept-oriented programming: from classes to concepts and from inheritance to inclusion
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Added by
Alexandr Savinov
Publication date
2015
fields
Informatics Engineering
and research's language is
English
Authors
Alexandr Savinov
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For the past several decades, programmers have been modeling things in the world with trees using hierarchies of classes and object-oriented programming (OOP) languages. In this paper, we describe a novel approach to programming, called concept-oriented programming (COP), which generalizes classes and inheritance by introducing concepts and inclusion, respectively.
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Object-oriented programming (OOP) is aimed at describing the structure and behaviour of objects by hiding the mechanism of their representation and access in primitive references. In this article we describe an approach, called concept-oriented programming (COP), which focuses on modelling references assuming that they also possess application-specific structure and behaviour accounting for a great deal or even most of the overall program complexity. References in COP are completely legalized and get the same status as objects while the functions are distributed among both objects and references. In order to support this design we introduce a new programming construct, called concept, which generalizes conventional classes and concept inclusion relation generalizing class inheritance. The main advantage of COP is that it allows programmers to describe two sides of any program: explicitly used functions of objects and intermediate functionality of references having cross-cutting nature and executed implicitly behind the scenes during object access.
Class invariants -- consistency constraints preserved by every operation on objects of a given type -- are fundamental to building and understanding object-oriented programs. They should also be a key help in verifying them, but turn out instead to raise major verification challenges which have prompted a significant literature with, until now, no widely accepted solution. The present work introduces a general proof rule meant to address invariant-related issues and allow verification tools benefit from invariants. It first clarifies the notion of invariant and identify the three problems: callbacks, furtive access and reference leak. As an example, the 2016 Ethereum DAO bug, in which $50 million were stolen, resulted from a callback invalidating an invariant. The discussion starts with a Simple Model and an associated proof rule, demonstrating its soundness. It then removes one by one the three assumptions of the Simple Model, each removal bringing up one of the three issues, and introduces the corresponding adaptation to the proof rule. The final version of the rule can tackle tricky examples, including challenge problems listed in the literature.
I present a new approach to teaching a graduate-level programming languages course focused on using systems programming ideas and languages like WebAssembly and Rust to motivate PL theory. Drawing on students prior experience with low-level languages, the course shows how type systems and PL theory are used to avoid tricky real-world errors that students encounter in practice. I reflect on the curricular design and lessons learned from two years of teaching at Stanford, showing that integrating systems ideas can provide students a more grounded and enjoyable education in programming languages. The curriculum, course notes, and assignments are freely available: http://cs242.stanford.edu/f18/
Context: Embedded Domain-Specific Languages (EDSLs) are a common and widely used approach to DSLs in various languages, including Haskell and Scala. There are two main implementation techniques for EDSLs: shallow embeddings and deep embeddings. Inquiry: Shallow embeddings are quite simple, but they have been criticized in the past for being quite limited in terms of modularity and reuse. In particular, it is often argued that supporting multiple DSL interpretations in shallow embeddings is difficult. Approach: This paper argues that shallow EDSLs and Object-Oriented Programming (OOP) are closely related. Gibbons and Wu already discussed the relationship between shallow EDSLs and procedural abstraction, while Cook discussed the connection between procedural abstraction and OOP. We make the transitive step in this paper by connecting shallow EDSLs directly to OOP via procedural abstraction. The knowledge about this relationship enables us to improve on implementation techniques for EDSLs. Knowledge: This paper argues that common OOP mechanisms (including inheritance, subtyping, and type-refinement) increase the modularity and reuse of shallow EDSLs when compared to classical procedural abstraction by enabling a simple way to express multiple, possibly dependent, interpretations. Grounding: We make our arguments by using Gibbons and Wus examples, where procedural abstraction is used in Haskell to model a simple shallow EDSL. We recode that EDSL in Scala and with an improved OO-inspired Haskell encoding. We further illustrate our approach with a case study on refactoring a deep external SQL query processor to make it more modular, shallow, and embedded. Importance: This work is important for two reasons. Firstly, from an intellectual point of view, this work establishes the connection between shallow embeddings and OOP, which enables a better understanding of both concepts. Secondly, this work illustrates programming techniques that can be used to improve the modularity and reuse of shallow EDSLs.
This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image captions. We use multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives. The word detector outputs serve as conditional inputs to a maximum-entropy language model. The language model learns from a set of over 400,000 image descriptions to capture the statistics of word usage. We capture global semantics by re-ranking caption candidates using sentence-level features and a deep multimodal similarity model. Our system is state-of-the-art on the official Microsoft COCO benchmark, producing a BLEU-4 score of 29.1%. When human judges compare the system captions to ones written by other people on our held-out test set, the system captions have equal or better quality 34% of the time.
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