Exploiting Type Hints in Method Argument Names to Improve Lightweight Type Inference

Dynamically typed languages allow developers to write more expressive source code, but their lack of static information about types of variables increases the complexity of a program. Static type information about types of variables facilitates program comprehension and maintenance. Simple type inference algorithms suffer from the problem of false positives or negatives, thus complex approaches are required to avoid this problem. We propose a simple heuristic that uses easily accessible run-time information about the usage of each class as a receiver type for a message send. This frequency serves as a proxy for the likelihood that a run-time type of the variable is that class and it is used to promote the correct type towards the top of the list of possible types for a variable. Our evaluation of a proof-of-concept prototype implemented in Pharo Smalltalk shows that our heuristic is reasonably precise to detect correct types on average in 65.92% to 82.83% of cases.

A secure software demands effective techniques for vulnerability detection during its development cycle. The practice of detecting security flaws before the deployment phase eliminates the risks that vulnerabilities may impose for the company. Static analysis and dynamic analysis techniques offer two complimentary approaches for checking vulnerabilities. Static analysis involves the scanning of source code or binary eliminating the need of executing it. This approach is fast and has no run time overhead. However, static analysis are quite imprecise and generate huge false positives and false negatives. On the other hand, dynamic analysis involves the running of the software. The problem of false positives and negatives is less in case of dynamic analysis because they analyze by running the test cases. But this approach requires large number of test cases to ensure a certain confidence level in detecting security bugs. This paper describes a methodology which integrates the two approaches in a complimentary manner. It adopts the strengths of the two and eliminates their weaknesses. We are currently dealing with buffer overflow vulnerability with pointer aliasing. However the idea can be extended to other vulnerabilities also for e.g memory related errors, race conditions( Time of Check to Time to Use vulnerability), dangling pointer vulnerability, integer errors etc.

Multiple dispatch is a feature provided by some programming languages, in which the actual method to be executed is dynamically determined by the runtime types of its arguments. Although some programming languages such as CLOS, Xtend and Dylan provide this feature, many widespread languages such as Java, C# and C++ only support single dispatch by means of dynamic binding. Therefore, different implementation techniques are commonly used to obtain multiple dispatch from within these single-dispatch languages. We propose a new approach to obtain multiple dispatch in single-dispatch hybrid static and dynamic typing languages. Using C# as an example, the proposed approach is compared with the existing ones, considering factors such as software maintainability and readability, code size, parameter generalization, compile-time type checking, and runtime performance. The example implementation of the hybrid statically and dynamically typed approach in .Net has shown a runtime performance 1.26 times lower than the statically typed approach, and 6.27 times higher than the dynamically type one.

Logically, two broad types of mnemonic errors are possible when adult psychotherapy or hypnosis patients reflect on whether they were sexually abused or not as a child. They may believe that they were not abused when in fact they were (false negative error), or they may believe they were abused when in fact they were not (false positive error). The author briefly reviews the empirical evidence for the occurrence of each of these types of errors, and illustrates each with a clinical case. Further, in considering the incidence, importance, and clinical implications of these errors, the author contends that clinical efficacy in no way assures that a false negative or a false positive has been avoided. A plea is made for theorists and researchers to acknowledge that both categories of errors can occur and to conduct future clinical and laboratory research accordingly.

Gradual typing lets programmers evolve their dynamically typed programs by gradually adding explicit type annotations, which confer benefits like improved performance and fewer run-time failures. However, we argue that such evolution often requires a giant leap, and that type inference can offer a crucial missing step. If omitted type annotations are interpreted as unknown types, rather than the dynamic type, then static types can often be inferred, thereby removing unnecessary assumptions of the dynamic type. The remaining assumptions of the dynamic type may then be removed by either reasoning outside the static type system, or restructuring the code. We present a type inference algorithm that can improve the performance of existing gradually typed programs without introducing any new run-time failures. To account for dynamic typing, types that flow in to an unknown type are treated in a fundamentally different manner than types that flow out. Furthermore, in the interests of backward-compatibility, an escape analysis is conducted to decide which types are safe to infer. We have implemented our algorithm for ActionScript, and evaluated it on the SunSpider and V8 benchmark suites. We demonstrate that our algorithm can improve the performance of unannotated programs as well as recover most of the type annotations in annotated programs.

Many dynamically-typed languages (including JavaScript, Ruby, Python or Racket) support first-class classes, or related concepts such as first-class traits and/or mixins. In those languages classes are first-class values and, like any other values, they can be passed as an argument, or returned from a function. Furthermore first-class classes support dynamic inheritance: i.e. they can inherit from other classes at runtime, enabling programmers to abstract over the inheritance hierarchy. In contrast, type system limitations prevent most statically-typed languages from having first-class classes and dynamic inheritance. This paper shows the design of SEDEL: a polymorphic statically-typed language with first-class traits, supporting dynamic inheritance as well as conventional OO features such as dynamic dispatching and abstract methods. To address the challenges of type-checking first-class traits, SEDEL employs a type system based on the recent work on disjoint intersection types and disjoint polymorphism. The novelty of SEDEL over core disjoint intersection calculi are source level features for practical OO programming, including first-class traits with dynamic inheritance, dynamic dispatching and abstract methods. Inspired by Cook and Palsberg's work on the denotational semantics for inheritance, we show how to design a source language that can be elaborated into Alpuim et al.'s F_{i} (a core polymorphic calculus with records supporting disjoint polymorphism). We illustrate the applicability of SEDEL with several example uses for first-class traits, and a case study that modularizes programming language interpreters using a highly modular form of visitors.

Hindley and Milner's type system, in its purest form, allows what one might call full type inference. Even when a program contains no explicit type information, its very structure gives rise to a conjunction of type equations (or, more generally, to a constraint) whose resolution allows reconstructing the type of every variable and of every sub-expression. Actual programming languages based on this type system, such as Standard ML, Objective Caml, and Haskell, do allow users to provide explicit type information via optional type annotations. Yet, this extra information does not make type inference any easier: it simply gives rise to extra equations, leading to a more specific constraint.There are extensions of Hindley and Milner's type system where full type inference becomes undecidable or intractable. Then, it is common to require explicit type information, via mandatory type annotations. Polymorphic recursion is a simple and well-known instance of this phenomenon. This talk presents two more elaborate instances, commonly known as arbitrary-rank polymorphism and generalized algebraic data types.In both cases, type inference is made tractable thanks to mandatory type annotations. There is a twist, though: these required annotations are often numerous and redundant. So, it seems desirable to make them optional again, and to set up a distinct mechanism to guess some of the elided annotations. This mechanism, referred to as elaboration, can be heuristic. It must be predictable. In the two cases considered in this talk, it is a form of local type inference.This approach leads to a so-called stratified type inference system, where type information is propagated first in a local, ad hoc manner during elaboration, then in a possibly nonlocal, but well-specified manner during constraint solving. This appears to be one of the less displeasing ways of describing type inference systems that are able to exploit optional type annotations to infer better (or different) types.

Dynamic languages such as Javascript are the de-facto standard for web applications. However, generating efficient code for dynamically-typed languages is a challenge, because it requires frequent dynamic type checks. Our analysis has shown that some programs spend upwards of 20% of dynamic instructions doing type checks, and 12.9% on average. In this paper we propose Checked Load, a low-complexity architectural extension that replaces software-based, dynamic type checking. Checked Load is comprised of four new ISA instructions that provide flexible and automatic type checks for memory operations, and whose implementation requires minimal hardware changes. We also propose hardware support for dynamic type prediction to reduce the cost of failed type checks. We show how to use Checked Load in the Nitro JavaScript just-in-time compiler (used in the Safari 5 browser). Speedups on a typical mobile processor range up to 44.6% (with a mean of 11.2%) in popular JavaScript benchmarks. While we have focused our work on JavaScript, Checked Load is sufficiently general to support other dynamically-typed languages, such as Python or Ruby.

Although dynamically typed languages allow developers to be more productive in writing source code, their lack of in- formation about types of variables is one of the main obstacles during program comprehension. Static type information helps developers to decrease software maintenance time. Inference of types of variables requires complex algorithms to avoid false positives or negatives. Their main aim is to shorten the list of possible types for a variable. We have developed a couple of cheap heuristics that use easily accessible information about the presence of each class in the available source code to promote the correct type towards the top of the list of possible types for a variable. Our evaluation of a proof-of-concept prototype, implemented in Pharo Smalltalk, shows that both for domain- specific types and standard libraries these heuristics tend to work well. The evaluated heuristics prove to be reasonably precise, promoting the correct types of a variable towards the top of the list in 50.67% up to 89.09% of cases on average, depending on the applied heuristic. The heuristic that has proved to be the best was compared with one existing type inference algorithm and the best heuristic yields significantly better results with less effort.

Applications written in dynamically typed scripting languages are increasingly popular for Web software development. Even on the server side, programmers are using dynamically typed scripting languages such as Ruby and Python to build complex applications quickly. As the number and complexity of dynamically typed scripting language applications grows, optimizing their performance is becoming important. Some of the best performing compilers and optimizers for dynamically typed scripting languages are developed entirely from scratch and target a specific language. This approach is not scalable, given the variety of dynamically typed scripting languages, and the effort involved in developing and maintaining separate infrastructures for each. In this paper, we evaluate the feasibility of adapting and extending an existing production-quality method-based Just-In-Time (JIT) compiler for a language with dynamic types. Our goal is to identify the challenges and shortcomings with the current infrastructure, and to propose and evaluate runtime techniques and optimizations that can be incorporated into a common optimization infrastructure for static and dynamic languages. We discuss three extensions to the compiler to support dynamically typed languages: (1) simplification of control flow graphs, (2) mapping of memory locations to stack-allocated variables, and (3) reduction of runtime overhead using language semantics. We also propose four new optimizations for Python in (2) and (3). These extensions are effective in reduction of compiler working memory and improvement of runtime performance. We present a detailed performance evaluation of our approach for Python, finding an overall improvement of 1.69x on average (up to 2.74x) over our JIT compiler without any optimization for dynamically typed languages and Python.

Deriving a complete type inference for Hindley–Milner and vector sizes using expansion

Numerous behavioral procedures have been described in the literature which are intended to evaluate the presence and/or degree of altered brain function. Determination of the efficacy of these procedures is difficult enough in general, but becomes considerably more complicated when applied to the aged in particular. The purpose of this chapter is to consider some of the clinical issues including: the meaning of “organic brain dysfunction” that the behavioral procedures are supposed to measure, the problems of false positives and negatives, the circumstances under which the clinical examination is likely to occur, and the common problems of differential diagnosis encountered. There will be a review of the psychometric and clinical evaluation procedures commonly employed and, finally, measures which are most likely to be clinically useful will be recommended and described in detail.

Type feedback and type inference are two common methods used to optimize dynamic languages such as JavaScript. Each of these methods has its own strengths and weaknesses, and we propose that each can benefit from the other if combined in the right way. We explore the interdependency between these two methods and propose two novel ways to combine them in order to significantly increase their aggregate benefit and decrease their aggregate overhead. In our proposed strategy, an initial type inference pass is applied that can reduce type feedback overhead by enabling more intelligent placement of profiling hooks. This initial type inference pass is novel in the literature. After profiling, a final type inference pass uses the type information from profiling to generate efficient code. While this second pass is not novel, we significantly improve its effectiveness in a novel way by feeding the type inference pass information about the function signature, i.e., the types of the function's arguments for aspecific function invocation. Our results show significant speedups when using these low-overhead strategies, ranging from 1.2x to 4x over an implementation that does not perform type feedback or type inference based optimizations. Our experiments are carried out across a wide range of traditional benchmarks and realistic web applications. The results also show an average reduction of 23.5% in the size of the profiled data for these benchmarks.

Type information is a crucial information to support object-oriented reengineering. In a dynamically typed language like Smalltalk standard static type inference is a complex and heavily computational task. In this paper, we report how we use message passing control and compiler extension to support dynamic inference type in Smalltalk.

Gradual typing allows programs to enjoy the benefits of both static typing and dynamic typing. While it is often desirable to migrate a program from more dynamically typed to more statically typed or vice versa, gradual typing itself does not provide a way to facilitate this migration. This places the burden on programmers who have to manually add or remove type annotations. Besides the general challenge of adding type annotations to dynamically typed code, there are subtle interactions between these annotations in gradually typed code that exacerbate the situation. For example, to migrate a program to be as static as possible, in general, all possible combinations of adding or removing type annotations from parameters must be tried out and compared. In this paper, we address this problem by developing migrational typing , which efficiently types all possible ways of replacing dynamic types with fully static types for a gradually typed program. The typing result supports automatically migrating a program to be as static as possible or introducing the least number of dynamic types necessary to remove a type error. The approach can be extended to support user-defined criteria about which annotations to modify. We have implemented migrational typing and evaluated it on large programs. The results show that migrational typing scales linearly with the size of the program and takes only 2–4 times longer than plain gradual typing.