Staattisesti tyypitetyt ohjelmointikielet JavaScript-ekosysteemissä: tyyppijärjestelmien näkökulma

JavaScript is a ubiquitous programming language with usage in web, mobile applications and server software. The status of the language as the de-facto programming language of the web has made the language ecosystem advanced with a great number of userspace libraries and major companies working on efficient runtime systems. The core language, however, has numerous known difficulties caused by the initial design and persisted by the requirements for backwards-compatibility. In the last decade, a number of programming languages have chosen JavaScript as the compile target of the language. Type theory and its application, programming language type systems, is an essential area of study in the design of programming languages. Every high-level programming language features a type system that greatly influences the ways of designing and implementing programs in the language. This thesis examines a group of selected statically-typed programming languages that compile to JavaScript. The core topics of research in this thesis are the motivation for new JS-compiled languages, the type system design of the languages, and the future direction of the JavaScript ecosystem based on the current trends and parallels to other programming ecosystems. The results of the work include identifying several trends in type systems for the JS ecosystem and the web. These include unsound yet convenient partially inferred type systems for object-oriented and multi-paradigm programming and fully inferred extended Hindley-Milner type systems for primarily functional programming languages. Additionally, different options for the advancement of the programming ecosystem, including type annotations, inference of dynamically typed languages and new compile targets, are explored. Finally, based on the design choices of the languages researched, we provide several recommendations for safe and productive statically typed programming in the JavaScript ecosystem.JavaScript on laajalti käytetty ohjelmointikieli, jonka käyttö ulottuu web- ja mobiilisovelluksiin sekä palvelinohjelmistoon. Kielen asema web-kehityksen de-facto-ohjelmointikielenä on luonut sen ympärille laajan ohjelmistoekosysteemin, joka kattaa suuren määrän ohjelmistokirjastoja sekä tehokkaita ajoympäristöjä. Itse kieli aiheuttaa tästä huolimatta vaikeuksia alkuperäisten suunnitteluvirheiden ja vaaditun taaksepäinyhteensopivuuden vuoksi. Viimeisen vuosikymmenen aikana useampi ohjelmointikieli on alkanut käyttää JavaScriptia käännöskohteenaan. Tyyppiteoria ja sen sovellus, ohjelmointikielten tyyppijärjestelmät, on tärkeä tutkimusala liittyen ohjelmointikielten suunnitteluun. Tyyppijärjestelmä on osa jokaista korkean tason ohjelmoinikieltä ja vaikuttaa täten suuresti itse ohjelmointikielen muihin ominaisuuksiin ja käyttöön. Tämä tutkimus käsittelee joukkoa staattisesti tyypitettyjä ohjelmointikieliä, jotka kääntyvät JavaScript-koodiksi. Tutkimuksen ytimessä ovat uusien kielten kehityksen motiivit, kielten tyyppijärjestelmien suunnittelu ja ominaisuudet sekä JavaScript-ekosysteemin mahdolliset tulevaisuuden suunnat. Työn tuloksena tunnistamme useita trendejä tyyppijärjestelmien suunnittelussa JavaScript-ekosysteemiin. Näihin kuuluu käytännölliset, mutta teoriassa epäturvalliset tyyppijärjestelmät olio- ja moniparadigmaohjelmoinkieliin sekä funktionaalisten ohjelmointikielien Hindley-Milner-pohjaiset tyyppijärjestelmät, joissa muuttujien tyypit pystytään täysin päättelemään ilman ohjelman kirjoittajan annotaatioita. Lisäksi nostamme esiin useita tulevaisuuden suuntia, jotka voisivat viedä JS-ekosysteemiä eteenpäin. Näihin kuuluvat tyyppiannotaatiot, dynaamisten kielten tyyppi-inferenssi ja uudet käännöskohteet web-ekosysteemiin. Lopuksi annamme tutkimuksen perusteella suosituksia ominaisuuksista ja suunnitteluratkaisuista, jotka voisivat mahdollistaa tehokkaan ja turvallisen ohjelmistokehityksen JavaScript-ekosysteemissä tulevaisuudessa

Subtyping with Generics: A Unified Approach

Reusable software increases programmers\u27 productivity and reduces repetitive code and software bugs. Variance is a key programming language mechanism for writing reusable software. Variance is concerned with the interplay of parametric polymorphism (i.e., templates, generics) and subtype (inclusion) polymorphism. Parametric polymorphism enables programmers to write abstract types and is known to enhance the readability, maintainability, and reliability of programs. Subtyping promotes software reuse by allowing code to be applied to a larger set of terms. Integrating parametric and subtype polymorphism while maintaining type safety is a difficult problem. Existing variance mechanisms enable greater subtyping between parametric types, but they suffer from severe deficiencies. They are unable to express several common type abstractions. They can cause a proliferation of types and redundant code. They are difficult for programmers to use due to its inherent complexity. This dissertation aims to improve variance mechanisms in programming languages supporting parametric polymorphism. To address the shortcomings of current mechanisms, I will combine two popular approaches, definition-site variance and use-site variance, in a single programming language. I have developed formal languages or calculi for reasoning about variance. The calculi are example languages supporting both notions of definition-site and use-site variance. They enable stating precise properties that can be proved rigorously. The VarLang calculus demonstrates fundamental issues in variance from a language neutral perspective. The VarJ calculus illustrates realistic complications by modeling a mainstream programming language, Java. VarJ not only supports both notions of use-site and definition-site variance but also language features with complex interactions with variance such as F-bounded polymorphism and wildcard capture. A mapping from Java to VarLang was implemented in software that infers definition-site variance for Java. Large, standard Java libraries (e.g. Oracle\u27s JDK 1.6) were analyzed using the software to compute metrics measuring the benefits of adding definition-site variance to Java, which only supports use-site variance. Applying this technique to six Java generic libraries shows that 21-47% (depending on the library) of generic definitions are inferred to have single-variance; 7-29% of method signatures can be relaxed through this inference, and up to 100% of existing wildcard annotations are unnecessary and can be elided. Although the VarJ calculus proposes how to extend Java with definition-site variance, no mainstream language currently supports both definition-site and use-site variance. To assist programmers with utilizing both notions with existing technology, I developed a refactoring tool that refactors Java code by inferring definition-site variance and adding wildcard annotations. This tool is practical and immediately applicable: It assumes no changes to the Java type system, while taking into account all its intricacies. This system allows users to select declarations (variables, method parameters, return types, etc.) to generalize and considers declarations not declared in available source code. I evaluated our technique on six Java generic libraries. I found that 34% of available declarations of variant type signatures can be generalized-i.e., relaxed with more general wildcard types. On average, 146 other declarations need to be updated when a declaration is generalized, showing that this refactoring would be too tedious and error-prone to perform manually. The result of applying this refactoring is a more general interface that supports greater software reuse

A Type System for Julia

The Julia programming language was designed to fill the needs of scientific computing by combining the benefits of productivity and performance languages. Julia allows users to write untyped scripts easily without needing to worry about many implementation details, as do other productivity languages. If one just wants to get the work done-regardless of how efficient or general the program might be, such a paradigm is ideal. Simultaneously, Julia also allows library developers to write efficient generic code that can run as fast as implementations in performance languages such as C or Fortran. This combination of user-facing ease and library developer-facing performance has proven quite attractive, and the language has increasing adoption. With adoption comes combinatorial challenges to correctness. Multiple dispatch -- Julia's key mechanism for abstraction -- allows many libraries to compose "out of the box." However, it creates bugs where one library's requirements do not match what another provides. Typing could address this at the cost of Julia's flexibility for scripting. I developed a "best of both worlds" solution: gradual typing for Julia. My system forms the core of a gradual type system for Julia, laying the foundation for improving the correctness of Julia programs while not getting in the way of script writers. My framework allows methods to be individually typed or untyped, allowing users to write untyped code that interacts with typed library code and vice versa. Typed methods then get a soundness guarantee that is robust in the presence of both dynamically typed code and dynamically generated definitions. I additionally describe protocols, a mechanism for typing abstraction over concrete implementation that accommodates one common pattern in Julia libraries, and describe its implementation into my typed Julia framework.Comment: PhD thesi

The Essence of Dependent Object Types

Focusing on path-dependent types, the paper develops foundations for Scala from first principles. Starting from a simple calculus D-<: of dependent functions, it adds records, intersections and recursion to arrive at DOT, a calculus for dependent object types. The paper shows an encoding of System F with subtyping in D-<: and demonstrates the expressiveness of DOT by modeling a range of Scala constructs in it

Hidden Type Variables and Conditional Extension for More Expressive Generic Programs

Generic object-oriented programming languages combine parametric polymorphism and nominal subtype polymorphism, thereby providing better data abstraction, greater code reuse, and fewer run-time errors. However, most generic object-oriented languages provide a straightforward combination of the two kinds of polymorphism, which prevents the expression of advanced type relationships. Furthermore, most generic object-oriented languages have a type-erasure semantics: instantiations of type parameters are not available at run time, and thus may not be used by type-dependent operations. This dissertation shows that two features, which allow the expression of many advanced type relationships, can be added to a generic object-oriented programming language without type erasure: 1. type variables that are not parameters of the class that declares them, and 2. extension that is dependent on the satisfiability of one or more constraints. We refer to the first feature as hidden type variables and the second feature as conditional extension. Hidden type variables allow: covariance and contravariance without variance annotations or special type arguments such as wildcards; a single type to extend, and inherit methods from, infinitely many instantiations of another type; a limited capacity to augment the set of superclasses after that class is defined; and the omission of redundant type arguments. Conditional extension allows the properties of a collection type to be dependent on the properties of its element type. This dissertation describes the semantics and implementation of hidden type variables and conditional extension. A sound type system is presented. In addition, a sound and terminating type checking algorithm is presented. Although designed for the Fortress programming language, hidden type variables and conditional extension can be incorporated into other generic object-oriented languages. Many of the same problems would arise, and solutions analogous to those we present would apply

Julia Subtyping: A Rational Reconstruction

OOPSLA, Article 113International audienceProgramming languages that support multiple dispatch rely on an expressive notion of subtyping to specify method applicability. In these languages, type annotations on method declarations are used to select, out of a potentially large set of methods, the one that is most appropriate for a particular tuple of arguments. Julia is a language for scientific computing built around multiple dispatch and an expressive subtyping relation. This paper provides the first formal definition of Julia's subtype relation and motivates its design. We validate our specification empirically with an implementation of our definition that we compare against the existing Julia implementation on a collection of real-world programs. Our subtype implementation differs on 122 subtype tests out of 6,014,476. The first 120 differences are due to a bug in Julia that was fixed once reported; the remaining 2 are under discussion

A Robust Unified Graph Model Based on Molecular Data Binning for Subtype Discovery in High-dimensional Spaces

Machine learning (ML) is a subfield of artificial intelligence (AI) that has already revolutionised the world around us. It is a widely employed process for discovering patterns and groups within datasets. It has a wide range of applications including disease subtyping, which aims to discover intrinsic subtypes of disease in large-scale unlabelled data. Whilst the groups discovered in multi-view high-dimensional data by ML algorithms are promising, their capacity to identify pertinent and meaningful groups is limited by the presence of data variability and outliers. Since outlier values represent potential but unlikely outcomes, they are statistically and philosophically fascinating. Therefore, the primary aim of this thesis was to propose a robust approach that discovers meaningful groups while considering the presence of data variability and outliers in the data. To achieve this aim, a novel robust approach (ROMDEX) was developed that utilised the proposed intermediate graph models (IMGs) for robust computation of proximity between observations in the data. Finally, a robust multi-view graph-based clustering approach was developed based on ROMDEX that improved the discovery of meaningful groups that were hidden behind the noise in the data. The proposed approach was validated on real-world, and synthetic data for disease subtyping. Additionally, the stability of the approach was assessed by evaluating its performance across different levels of noise in clustering data. The results were evaluated through Kaplan-Meier survival time analysis for disease subtyping. Also, the concordance index (CI) and normalised mutual information (NMI) are used to evaluate the predictive ability of the proposed clustering model. Additionally, the accuracy, Kappa statistic and rand index are computed to evaluate the clustering stability against various levels of Gaussian noise. The proposed approach outperformed the existing state-of-the-art approaches MRGC, PINS, SNF, Consensus Clustering, and Icluster+ on these datasets. The findings for all datasets were outstanding, demonstrating the predictive ability of the proposed unsupervised graph-based clustering approach

A Theory of Higher-Order Subtyping with Type Intervals (Extended Version)

The calculus of Dependent Object Types (DOT) has enabled a more principled and robust implementation of Scala, but its support for type-level computation has proven insufficient. As a remedy, we propose $F^\omega_{..}$, a rigorous theoretical foundation for Scala's higher-kinded types. $F^\omega_{..}$ extends $F^\omega_{<:}$ with interval kinds, which afford a unified treatment of important type- and kind-level abstraction mechanisms found in Scala, such as bounded quantification, bounded operator abstractions, translucent type definitions and first-class subtyping constraints. The result is a flexible and general theory of higher-order subtyping. We prove type and kind safety of $F^\omega_{..}$, as well as weak normalization of types and undecidability of subtyping. All our proofs are mechanized in Agda using a fully syntactic approach based on hereditary substitution.Comment: 73 pages; to be presented at the 26th ACM SIGPLAN International Conference on Functional Programming (ICFP 2021), 22-27 August 202

Data mining scenarios for the discovery of subtypes and the comparison of algorithms

A data mining scenario is a logical sequence of steps to infer patterns from data. In this thesis, we present two scenarios. Our first scenario aims to identify homogeneous subtypes in data. It was applied to clinical research on Osteoarthritis (OA) and Parkinson’s disease (PD) and in drug discovery. Thus, because OA and PD are characterized by clinical heterogeneity, a more sensitive classification of the cohort of patients may contribute to the search for the underlying diseases mechanism. In drug discovery, subtyping may improve the understanding of the similarity (and distance) between different phenotypic effects as induced by drugs and chemicals. Our second scenario aims to compare text classification algorithms. First, we show that common classifiers achieve comparable performance on most problems. Second, tightly constrained SVM solutions are high performers. In that situation, most training documents are bounded support vectors, SVM reduces to a nearest mean classifier and no training is necessary, which raises a question on SVM merits in sparse bag of words feature spaces. Also, SVM is shown to suffer from performance deterioration for particular combinations of training set size/number of features. This relate to outlying documents of distinct classes overlapping in the feature space.LEI Universiteit LeidenAlgorithm