E P L S | These abstracts are for talks at this event. NEPLS is a venue for ongoing research, so the abstract and supplemental material associated with each talk is necessarily temporal. The work presented here may be in a state of flux. In all cases, please consult the authors' Web pages for up-to-date information. Please don't refer to these pages as a definitive source.
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It has been noted that many typed lambda; calculi are both weakly normalizing and strongly normalizing. This has lead to the statement of the Barendregt-Geuvers-Klop conjecture. The conjecture is concerned with so-called pure type systems which include the well-known typed lambda calculi like the simply-typed lambda calculus, polymorphism (system F), and dependent types. The conjecture states that if all terms in a pure type system are weakly normalizing, then all terms are strongly normalizing. By definition, we say that all pure type systems are uniformly normalizing. The literature contains many proofs of instances of this conjecture. Most of these proofs depend on the so-called Conservation Theorem for Lambda I (Lambda I is the lambda calculus with the restriction that an abstraction variable always appears in the body of an abstraction). The Conservation Theorem can be stated in two equivalent forms: one form is that Lambda I has conservation, i.e., that single-step reductions of a term preserve infinite reduction paths from the term; the other form is that Lambda I is uniformly normalizing. However, in more general settings, conservation for an arbitrary set of terms only implies uniform normalization if the set of terms is closed under reduction. In particular, when introducing reductions erasing subterms the two notions are not necessarily the same. This work studies how to get uniform normalization for subsets of terms in the presence of erasing reductions. This is first done for a subset of the pure lambda calculus. Though a new result, this is mainly a quick warm up for the main result: that simply-typed lambda calculus extended with pairs and projections is uniformly normalizing. This is achieved using a CPS-translation to map lambda terms into a uniformly normalizing subset. Effectively, the proof uses a normalizing strategy to semi-decide strong normalization of a term. ## Speaker's SupplementThis talk will only present the main ideas. The person interested in the gory details can consult the following sources: - Peter Møller Neergaard and Morten Heine Sørensen, ``Conservation and Uniform Normalization in Lambda Calculi with Erasing Reductions'', accepted for Information and Computation
- Peter Møller Neergaard, ``Weak and Strong Normalization, K-redexes, and First-Order Logic''
Raw trace data, or even raw structural or semantic data for a realistic system, is too large to be used directly for software understanding or visualization. Instead, one must analyze this data to summarize it, to find patterns, or to highlight potential problems or areas of interest. To address this, we have developed two general purpose analysis facilities. The first uses a program database and allows arbitrary OQL or procedureal queries to retrieve and organize the appropriate information. The second is a framework for doing arbitrary analyses of program trace data. This facility has let us implement a wide variety of different analyses including: * Interval analysis where the overall call graph is summarized by breaking the execution into intervals and looking at either what classes were being executed or what types of memory was being allocated or freed during each of those intervals. * Path analysis where the dynamic call graph is modeled as a directed acyclic graph and the behavior of each node of the graph is encoded directly, as a finite state machine, or using a context-free grammar. * Lock analysis where we generate all sequences of nested locks that occur in the program to allow these to be checked for potential deadlock situations. * Performance analysis where we collect performance data for sets of k-level calls. When k=1 this yields a simple profile; when k=2 it gives information about who calls whom; when k>2 it provides more detailed information. * Class analysis where we gather and encode information about how each class is being used by looking at the sequence of method calls for each object of the class. The analysis can either include or ignore nested calls and the information can be encoded with path analysis. * Thread path analysis where we encode the sequence of calls and returns performed by each thread, grouping similar threads together, to get an understanding of what the different threads are doing. * Sampled traces where we generate what looks like a full call trace but only includes those calls that are in effect at some sampling interval. * Allocation analysis where we look at the number and size of objects that are allocated, either individually or grouped by class, along with information from the garbage collector. * Race analysis where we look for either individual objects or classes that are used by multiple threads at the same time. * Trace analysis where we can generate full information about all or a portion of the trace. The facility also provides a visual front end and an XML-oriented back end to let these analyses be combined in interesting ways.
Functional reactive programming (FRP) is a declarative programming language that combines a notion of continuous, time-varying behaviors with a notion of discrete, event-based reactivity. In previous work we investigated key theoretical properties of FRP but did not formally address the issue of cost. But one of our goals is to use FRP in real-time applications, for which it is crucial that the cost of running programs be known before hand. Ideally, this cost should also be fixed. To address these concerns, we present Real-Time FRP (RT-FRP), a statically-typed language where the cost of each execution step is bounded by a constant, the space needed for running a program is also constant, and well-formed programs are provably deadlock free. These three properties guarantee that RT-FRP can be executed in real-time and in constant space, making it a suitable model for programming real-time applications and embedded systems. The syntax of RT-FRP is developed as a restricted subset of that of FRP. The exact restrictions are described using concepts inspired by the notion of tail recursion. Most but not all of our existing programs fall in this restricted subset. We deal with the balance through a mechanism to integrate RT-FRP with an external language. This is joint work with Zhanyong Wan and Walid Taha.
Despite Java's popularity, several practical limitations imposed by the language's type system have become increasingly apparent in recent years. A particularly glaring omission is the lack of a generic mechanism. As a result, many recent projects have extended Java to support polymorphism in the style of C++ templates or Ada generics. One project, GJ, adds F-Bounded parametric polymorphism to Java via a homogeneous translation (such that only one compiled class is produced for each source class), and produces bytecode that is compatible with the standard Java Virtual Machine. However while GJ's simple translation based on erasure allows for maximum interaction with existing Java code, the new parameterized types that it supports do not operate consistently with Java's semantics for lightweight reflection (i.e., checked type-casts and instanceof operations). We present Rupiah, a languaged based on features adapted from LOOM (a provably type-safe language), and implemented by a translation based on GJ's. However its translation differs from GJ's in that it harnesses Java's built in reflection to store information about parameterized types. The resulting bytecode correctly executes checked cast, instanceof, and array creation expressions because it can access the necessary type information at run-time. We also add a ThisType construct, which solves many of the problems that arise when binary methods are mixed with inheritance, and we replace subtyping with a different relation, matching. Finally, we add exact types, an inheritable virtual constructor mechanism: ThisClass, and compiler features to allow separate compilation of Rupiah source files. These features are implemented in a modified javac compiler. Bytecode emitted by our compiler runs on any Java 1.2 VM. Thus, our project contributes a complete implementation of an extension of Java with a more expressive type system that maintains a close fit with existing Java semantics and philosophies.
Advances in automated verification (model checking) of hardware systems has inspired a wealth of research into using these techniques to analyze software. Unfortunately, most existing modular model checking techniques betray their hardware roots: they assume that modules compose in parallel. In contrast, layered software systems, which have proven successful in many domains, are really quasi-sequential compositions of parallel compositions. Most such systems demand and inspire new modular verification techniques. This talk presents algorithms that exploit a layered (or feature-based) architecture to drive compositional verification. Our technique can verify large classes of properties locally within a layer; we also characterize when a global state space construction is unavoidable. This work is motivated by our efforts to verify a military fire simulation and support software system called FSATS. ## Speaker's SupplementThis talk is based on a paper by Fisler and Krishnamurthi to be presented at the European Software Engineering Conference / Foundations of Software Engineering (ESEC/FSE), 2001.
Polytypic programming is a paradigm for expressing algorithms parametrized by types. Examples of such algorithms are equality tests, traversals and pretty printers. Each polytypic instance can be obtained by instantiating a template algorithm with (the structure of) a datatype. I will present an introduction to functional polytypic programming explaining why it is useful and what it can express.
Persistent models of computation include the notion of a "state", or "memory", whose contents is not changed during the interaction of the computing device with its environment. In objects, the values of the attributes are persistent; in processes, it is the process state. We consider models of computation for object-oriented and concurrent computation, showing the equivalence of approaches based on transition systems and on Turing Machines extended with persistence. We also show the greater expressiveness of "persistent" vs. "amnesic" computation for these models. |

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