Region-based memory management offers several important potential advantages over garbage collection, including real-time performance, better data locality, and more efficient use of limited memory. Researchers have advocated the use of regions for functional, imperative, and object-oriented languages. Lexically scoped regions are now a core feature of the Real-Time Specification for Java (RTSJ) [5].Recent research in region-based programming for Java has focused on region checking, which requires manual effort to augment the program with region annotations. In this paper, we propose an automatic region inference system for a core subset of Java. To provide an inference method that is both precise and practical, we support classes and methods that are region-polymorphic, with regionpolymorphic recursion for methods. One challenging aspect is to ensure region safety in the presence of features such as class subtyping, method overriding, and downcast operations. Our region inference rules can handle these object-oriented features safely without creating dangling references.

In the current work, we investigate the benefits of immutability guarantees for allowing more flexible handling of aliasing, as well as more precise and concise specifications. Our approach supports finer levels of control that can mark data structures as being immutable through the use of immutability annotations. By using such annotations to encode immutability guarantees, we expect to obtain better specifications that can more accurately describe the intentions, as well as prohibitions, of the method. Ultimately, our goal is improving the precision of the verification process, as well as making the specifications more readable, more precise and as an enforceable program documentation. We have designed and implemented a new entailment procedure to formally and automatically reason about immutability enhanced specifications. We have also formalised the soundness for our new procedure through an operational semantics with mutability assertions on the heap. Lastly, we have carried out a set of experiments to both validate and affirm the utility of our current proposal on immutability enhanced specification mechanism.

Many program optimizations and analyses, such as arraybounds checking, termination analysis, depend on knowing the size of a function's input and output. However, size information can be difficult to compute. Firstly, accurate size computation requires detecting a size relation between different inputs of a function. Secondly, size information may also be contained inside a collection (data structure with multiple elements). In this paper, we introduce some techniques to derive universal and existential size properties over collections of elements of recursive data structures. We shall show how a mixed constraint system could support the enhanced size type, and highlight examples where collection analysis are useful.

Large functional programs are often constructed by decomposing each big task into smaller tasks which can be performed by simpler functions. This hierarchical style of developing programs has been found to improve programmers' productivity because smaller functions are easier to construct and reuse. However, programs written in this way tend to be less efficient. Unnecessary intermediate data structures may be created. More function invocations may be required. To reduce such performance penalties, Wadler proposed a transformation algorithm, called deforestation , which could automatically fuse certain composed expressions together in order to eliminate intermediate tree-like data structures. However, his technique is only applicable to a subset of first-order expressions. This paper will generalise the deforestation technique to make it applicable to all first-order and higher-order functional programs. Our generalisation is made possible by the adoption of a model for safe fusion which views each function as a producer and its parameters as consumers. Through this model, static program properties are proposed to classify producers and consumers as either safe or unsafe. This classification is used to identify sub-terms that can be safely fused/eliminated. We present the generalised transformation algorithm as a set of syntax-directed rewrite rules, illustrate it with examples, and provide an outline of its termination proof.