Featherweight Generic Confinement

Existing approaches to object encapsulation either rely on ad hoc syntactic restrictions or require the use of specialised type systems. Syntactic restrictions are difficult to scale and to prove correct, while specialised type systems require extensive changes to programming languages. We demonstrate that confinement can be enforced cheaply in Featherweight Generic Java, with no essential change to the underlying language or type system. This result demonstrates that polymorphic type parameters can simultaneously act as ownership parameters and should facilitate the adoption of confinement and ownership type systems in general-purpose programming languages

Generic Ownership Types for Java and the Collections Framework

Generic programming has turned out very useful in the development of reusable software. With the Java programming language, genericity is not only meant for reusability, but also for type-safety. Java generics constrain a container object (e.g., list, hash table) to store objects of a pre-specified data type. Nevertheless, safe programming with aliasing (multiple pointers in a program may point to the same object) is still a concern in object-oriented programming language research. A pointing object can mutate the state of the aliased object, reflecting the changes to all of the other pointers (aka aliases) thus affecting their behaviour. As programs grow larger and more complex, such changes in behaviour can be undesirable and difficult to detect and reason about. With respect to container objects, the iterator pattern critically violates encapsulation, allowing aliases to the state (and thereof the components) of its container. Object ownership is one of the well-researched paradigms in the area of alias management. Ownership types support hierarchical object encapsulation rather than the traditional class-level encapsulation. This thesis introduces an extension of Java 6 with support for ownership types as supplementary generic types. That is, Java generics are extended with the ability of carrying ownership information. This extension provides generic ownership support for all of Java; that is, all major language features are addressed so that programs can safely manage and express their aliasing properties. The resulting language is expressive enough to support common programming idioms, with little programming and runtime overhead. We evaluated the programmability of the language by refactoring a major (the most essential) portion of the Java Collections Framework. We also evaluated the performance impact of our refactoring by conducting a small micro-benchmark study to measure the performance time overhead the refactored collections may impose

Object Cloning for Ownership Systems

Modern object-oriented programming languages frequently need the ability to clone, duplicate, and copy objects. The usual approaches taken by languages are rudimentary, primarily because these approaches operate with little understanding of the object being cloned. Deep cloning naively copies every object that has a reachable reference path from the object being cloned, even if the objects being copied have no innate relationship with that object. For more sophisticated cloning operations, languages usually only provide the capacity for programmers to define their own cloning operations for specific objects, and with no help from the type system. Sheep cloning is an automated operation that clones objects by leveraging information about those objects’ structures, which the programmer imparts into their programs with ownership types. Ownership types are a language mechanism that defines an owner for every object in the program. Ownership types create a hierarchical structure for the heap. In this thesis, we construct an extensible formal model for an object-oriented language with ownership types (Core), and use it to explore different formalisms of sheep cloning. We formalise three distinct operational semantics of sheep cloning, and for each approach we include proofs or proof outlines where appropriate, and provide a comparative analysis of each model’s benefits. Our main contribution is the descripSC formal model of sheep cloning and its proof of type soundness. The second contribution of this thesis is the formalism of Mojo-jojo, a multiple ownership system that includes existential quantification over types and context parameters, along with a constraint system for context parameters. We prove type soundness for Mojo-jojo. Multiple ownership is a mechanism which allows objects to have more than one owner. Context parameters in Mojo-jojo can use binary operators such as: intersection, union, and disjointness

Advanced flow-based type systems for object-oriented languages

Ph.DDOCTOR OF PHILOSOPH

Programming Languages and Systems

This open access book constitutes the proceedings of the 29th European Symposium on Programming, ESOP 2020, which was planned to take place in Dublin, Ireland, in April 2020, as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The actual ETAPS 2020 meeting was postponed due to the Corona pandemic. The papers deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Existential Types for Variance - Java Wildcards and Ownership Types

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Runtime monitoring of security SLAs for big data pipelines: design implementation and evaluation of a framework for monitoring security SLAs in big data pipelines with the assistance of run-time code instrumentation

The Big Data processing ecosystem has been constantly growing in recent years. This has been significantly reinforced by the advent of cloud computing platforms where Big Data analytics can be offered on an as-a-service basis. The ease with which users can leverage the capabilities of Big Data processing frameworks in the cloud has made them a popular solution with low up-front expenditure and a flexible deployment model. In spite of their cost benefits and flexibility of use, Big Data services in cloud platforms present us with an array of new challenges compared to traditional web services especially in the domain of data security and privacy. Their distributed nature makes them more dynamic with regards to deployment and execution but at the same time it exacerbates challenges related to data and operation security since both data and operations are shared across multiple nodes. Inevitably, distributing data and operations on multiple nodes leads to an increase in the attack surface. Given the need for systems that react fast and produce results as quickly as possible, more emphasis has been placed on performance and less so on security. Having said that, as the use of cloud computing is becoming more widespread, concerns with regards to non-functional properties such as data security are becoming more pronounced for the users. Runtime security monitoring is a mechanism that can be employed to alleviate some of the issues that emerge with respect to the activity of security monitoring for Big Data analytics services that are outsourced in the cloud. In this thesis we make the case for a monitoring framework where monitoring events are collected and evaluated against a set of monitoring rules that describe monitorable security properties of the system. The framework that we put forward can be used to assess the level of security of Big Data analytics pipelines at runtime. For our proof of concept we examine three security properties namely the service response time, the location of execution of service operations and the integrity of the intermediate data produced during the service execution