Search

Instruction subsets are heuristics that can reduce the size of the inductive programming search space by tens of orders of magnitude. Comprising many overlapping subsets of different sizes, they serve as predictions of the instructions required to code a solution for any problem. Currently, this approach employs a single, large family of subsets meaning that some problems can search thousands of subsets before a solution is found. In this paper we introduce the use of test case type signatures as hyper-heuristics to select one of many, smaller families of instruction subsets. The type signature for any set of test cases maps directly to a single family and smaller families mean that fewer subsets need to be considered for most problems. Having many families also permits subsets to be reordered to better reflect their relative occurrence in human code - again reducing the search space size for many problems. Overall the new approach can further reduce the size of the inductive programming search space by between 1 and 3 orders of magnitude, depending on the type signature. Larger and more consistent reductions are possible through the use of more sophisticated type systems. The potential use of additional test case features as hyper-heuristics and some other possible future work is also briefly discussed., Comment: 14 pages, 3 figures. Accepted for 20th IFIP WG 12.5 International Conference, AIAI 2024 Corfu, Greece, June 27-30, 2024

Recent advancements in Large Language Models (LLMs) and their utilization in code generation tasks have significantly reshaped the field of software development. Despite the remarkable efficacy of code completion solutions in mainstream programming languages, their performance lags when applied to less ubiquitous formats such as OpenAPI definitions. This study evaluates the OpenAPI completion performance of GitHub Copilot, a prevalent commercial code completion tool, and proposes a set of task-specific optimizations leveraging Meta's open-source model Code Llama. A semantics-aware OpenAPI completion benchmark proposed in this research is used to perform a series of experiments through which the impact of various prompt-engineering and fine-tuning techniques on the Code Llama model's performance is analyzed. The fine-tuned Code Llama model reaches a peak correctness improvement of 55.2% over GitHub Copilot despite utilizing 25 times fewer parameters than the commercial solution's underlying Codex model. Additionally, this research proposes an enhancement to a widely used code infilling training technique, addressing the issue of underperformance when the model is prompted with context sizes smaller than those used during training. The dataset, the benchmark, and the model fine-tuning code are made publicly available., Comment: Update: a better quality and readability of figures, better explanation of code infilling and document splitting in training, some text polishing, making it more compact

We present an update on the current architecture of the Zoea knowledge-based, Composable Inductive Programming system. The Zoea compiler is built using a modern variant of the black-board architecture. Zoea integrates a large number of knowledge sources that encode different aspects of programming language and software development expertise. We describe the use of synthetic test cases as a ubiquitous form of knowledge and hypothesis representation that sup-ports a variety of reasoning strategies. Some future plans are also outlined., Comment: 14 pages, 3 figures

Despite the significant advances achieved in Artificial Neural Networks (ANNs), their design process remains notoriously tedious, depending primarily on intuition, experience and trial-and-error. This human-dependent process is often time-consuming and prone to errors. Furthermore, the models are generally bound to their training contexts, with no considerations to their surrounding environments. Continual adaptiveness and automation of neural networks is of paramount importance to several domains where model accessibility is limited after deployment (e.g IoT devices, self-driving vehicles, etc.). Additionally, even accessible models require frequent maintenance post-deployment to overcome issues such as Concept/Data Drift, which can be cumbersome and restrictive. By leveraging and combining approaches from Neural Architecture Search (NAS) and Continual Learning (CL), more robust and adaptive agents can be developed. This study conducts the first extensive review on the intersection between NAS and CL, formalizing the prospective Continually-Adaptive Neural Networks (CANNs) paradigm and outlining research directions for lifelong autonomous ANNs.

Conflict-free replicated data types (CRDTs) are a promising tool for designing scalable, coordination-free distributed systems. However, constructing correct CRDTs is difficult, posing a challenge for even seasoned developers. As a result, CRDT development is still largely the domain of academics, with new designs often awaiting peer review and a manual proof of correctness. In this paper, we present Katara, a program synthesis-based system that takes sequential data type implementations and automatically synthesizes verified CRDT designs from them. Key to this process is a new formal definition of CRDT correctness that combines a reference sequential type with a lightweight ordering constraint that resolves conflicts between non-commutative operations. Our process follows the tradition of work in verified lifting, including an encoding of correctness into SMT logic using synthesized inductive invariants and hand-crafted grammars for the CRDT state and runtime. Katara is able to automatically synthesize CRDTs for a wide variety of scenarios, from reproducing classic CRDTs to synthesizing novel designs based on specifications in existing literature. Crucially, our synthesized CRDTs are fully, automatically verified, eliminating entire classes of common errors and reducing the process of producing a new CRDT from a painstaking paper proof of correctness to a lightweight specification.

Computer-aided diagnosis tools have experienced rapid growth and development in recent years. Among all, deep learning is the most sophisticated and popular tool. In this paper, researchers propose a novel deep learning model and apply it to COVID-19 diagnosis. Our model uses the tool of fractional calculus, which has the potential to improve the performance of gradient methods. To this end, the researcher proposes a fractional-order gradient method for the back-propagation of convolutional neural networks based on the Caputo definition. However, if only the first term of the infinite series of the Caputo definition is used to approximate the fractional-order derivative, the length of the memory is truncated. Therefore, the fractional-order gradient (FGD) method with a fixed memory step and an adjustable number of terms is used to update the weights of the layers. Experiments were performed on the COVIDx dataset to demonstrate fast convergence, good accuracy, and the ability to bypass the local optimal point. We also compared the performance of the developed fractional-order neural networks and Integer-order neural networks. The results confirmed the effectiveness of our proposed model in the diagnosis of COVID-19., Comment: 9 pages, 6 figuers

It is believed that mechanisms including intermediate values enable composable inductive programming (CIP) to be used to produce software of any size. We present the results of a study that investigated the relationships between program size, the number of intermediate values and the number of test cases used to specify programs using CIP. In the study 96,000 programs of various sizes were randomly generated, decomposed into fragments and transformed into test cases. The test cases were then used to regenerate new versions of the original programs using Zoea. The results show linear relationships between the number of intermediate values and regenerated program size, and between the number of test cases and regenerated program size within the size range studied. In addition, as program size increases there is increasing scope for trading off the number of test cases against the number of intermediate values and vice versa., Comment: 8 pages, 9 figures

We present Zoea Visual which is a visual programming language based on the Zoea composable inductive programming language. Zoea Visual allows users to create software directly from a specification that resembles a set of functional test cases. Programming with Zoea Visual involves the definition of a data flow model of test case inputs, optional intermediate values, and outputs. Data elements are represented visually and can be combined to create structures of any complexity. Data flows between elements provide additional information that allows the Zoea compiler to generate larger programs in less time. This paper includes an overview of the language. The benefits of the approach and some possible future enhancements are also discussed., Comment: 10 pages, 4 figures

Composable inductive programming as implemented in the Zoea programming language is a simple declarative approach to software development. At the language level it is evident that Zoea is significantly simpler than all mainstream languages. However, until now we have only had anecdotal evidence that software produced with Zoea is also simpler than equivalent software produced with conventional languages. This paper presents the results of a quantitative comparison of the software complexity of equivalent code implemented in Zoea and also in a conventional programming language. The study uses a varied set of programming tasks from a popular programming language chrestomathy. Results are presented for relative program complexity using two established metrics and also for relative program size. It was found that Zoea programs are approximately 50% the complexity of equivalent programs in a conventional language and on average equal in size. The results suggest that current programming languages (as opposed to software requirements) are the largest contributor to software complexity and that significant complexity could be avoided through an inductive programming approach., Comment: 8 pages, 8 figures

The suitability of cloud computing has been studied by several authors to run scientific applications. However, the unpredictable performance fluctuations in these environments hinders the migration of scientific applications to cloud providers. To mitigate these effects, this work presents RUPER-LB, a load balancer for loosely-coupled iterative parallel applications that runs on infrastructures with disparate computing capabilities. The results obtained with a real world simulation software, show the suitability of RUPER-LB to adapt this kind of applications to execution environments with variable performance and highlight the convenience of its adoption., Comment: 13 pages, 9 figures, Added download link previously blinded due double blinded review process

Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties. Many commercial transactions use smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties. Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks which may enable attackers to steal valuable assets of involving parties. There is therefore a need to apply analysis and automated repair techniques to detect and repair bugs in smart contracts before being deployed. In this work, we present the first general-purpose automated smart contract repair approach that is also gas-aware. Our repair method is search-based and searches among mutations of the buggy contract. Our method also considers the gas usage of the candidate patches by leveraging our novel notion of gas dominance relationship. We have made our smart contract repair tool SCRepair available open-source, for investigation by the wider community., Comment: 32 pages. ACM Transactions on Software Engineering and Methodology (TOSEM), 2020

Security vulnerabilities are among the most critical software defects in existence. As such, they require patches that are correct and quickly deployed. This motivates an automatic patch generation method that emphasizes both soundness and wide applicability. To address this challenge, we propose Senx, which uses three novel patch generation techniques to create patches for out-of-bounds read/write vulnerabilities. Senx uses symbolic execution to extract expressions from the source code of a target application to synthesize patches. To reduce the runtime overhead of patches, it uses loop cloning and access range analysis to analyze loops involved in these vulnerabilities and elevate patches outside of loops. For vulnerabilities that span multiple functions, Senx uses expression translation to translate expressions and place them in a function scope where all values are available to create the patch. This enables Senx to patch vulnerabilities with complex loops and interprocedural dependencies that previous semantics-based patch generation systems cannot handle. We have implemented a prototype using this approach. Our evaluation shows that the patches generated by Senx successfully fix 76% of 42 real-world vulnerabilities from 11 applications including various tools or libraries for manipulating graphics/media files, a programming language interpreter, a relational database engine, a collection of programming tools for creating and managing binary programs, and a collection of basic file, shell, and text manipulation tools. All patches that Senx produces are sound, and Senx correctly aborts patch generations in cases where its analysis will fall short.

Considerable delays often exist between the discovery of a vulnerability and the issue of a patch. One way to mitigate this window of vulnerability is to use a configuration workaround, which prevents the vulnerable code from being executed at the cost of some lost functionality -- but only if one is available. Since program configurations are not specifically designed to mitigate software vulnerabilities, we find that they only cover 25.2% of vulnerabilities. To minimize patch delay vulnerabilities and address the limitations of configuration workarounds, we propose Security Workarounds for Rapid Response (SWRRs), which are designed to neutralize security vulnerabilities in a timely, secure, and unobtrusive manner. Similar to configuration workarounds, SWRRs neutralize vulnerabilities by preventing vulnerable code from being executed at the cost of some lost functionality. However, the key difference is that SWRRs use existing error-handling code within programs, which enables them to be mechanically inserted with minimal knowledge of the program and minimal developer effort. This allows SWRRs to achieve high coverage while still being fast and easy to deploy. We have designed and implemented Talos, a system that mechanically instruments SWRRs into a given program, and evaluate it on five popular Linux server programs. We run exploits against 11 real-world software vulnerabilities and show that SWRRs neutralize the vulnerabilities in all cases. Quantitative measurements on 320 SWRRs indicate that SWRRs instrumented by Talos can neutralize 75.1% of all potential vulnerabilities and incur a loss of functionality similar to configuration workarounds in 71.3% of those cases. Our overall conclusion is that automatically generated SWRRs can safely mitigate 2.1x more vulnerabilities, while only incurring a loss of functionality comparable to that of traditional configuration workarounds., Comment: Published in Proceedings of the 37th IEEE Symposium on Security and Privacy (Oakland 2016)

Sparse tiling is a technique to fuse loops that access common data, thus increasing data locality. Unlike traditional loop fusion or blocking, the loops may have different iteration spaces and access shared datasets through indirect memory accesses, such as A[map[i]] -- hence the name "sparse". One notable example of such loops arises in discontinuous-Galerkin finite element methods, because of the computation of numerical integrals over different domains (e.g., cells, facets). The major challenge with sparse tiling is implementation -- not only is it cumbersome to understand and synthesize, but it is also onerous to maintain and generalize, as it requires a complete rewrite of the bulk of the numerical computation. In this article, we propose an approach to extend the applicability of sparse tiling based on raising the level of abstraction. Through a sequence of compiler passes, the mathematical specification of a problem is progressively lowered, and eventually sparse-tiled C for-loops are generated. Besides automation, we advance the state-of-the-art by introducing: a revisited, more efficient sparse tiling algorithm; support for distributed-memory parallelism; a range of fine-grained optimizations for increased run-time performance; implementation in a publicly-available library, SLOPE; and an in-depth study of the performance impact in Seigen, a real-world elastic wave equation solver for seismological problems, which shows speed-ups up to 1.28x on a platform consisting of 896 Intel Broadwell cores., Comment: 29 pages including supplementary materials and references

Programming by Optimization tools perform automatic software configuration according to the specification supplied by a software developer. Developers specify design spaces for program components, and the onerous task of determining which configuration best suits a given use case is determined using automated analysis tools and optimization heuristics. However, in current approaches to Programming by Optimization, design space specification and exploration relies on external configuration algorithms, executable wrappers and fragile, preprocessed programming language extensions. Here we show that the architectural pattern of Dependency Injection provides a superior alternative to the traditional Programming by Optimization pipeline. We demonstrate that configuration tools based on Dependency Injection fit naturally into the software development process, while requiring less overhead than current wrapper-based mechanisms. Furthermore, the structural correspondence between Dependency Injection and context-free grammars yields a new class of evolutionary metaheuristics for automated algorithm configuration. We found that the new heuristics significantly outperform existing configuration algorithms on many problems of interest (in one case by two orders of magnitude). We anticipate that these developments will make Programming by Optimization immediately applicable to a large number of enterprise software projects.

Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula $\varphi$ in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in SyGuS-IF, a language that is built on top of SMT-LIB. The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to facilitate, bring together and accelerate research and development of efficient solvers for SyGuS by providing a platform for evaluating different synthesis techniques on a comprehensive set of benchmarks. In this year's competition we added a new track devoted to programming by examples. This track consisted of two categories, one using the theory of bit-vectors and one using the theory of strings. This paper presents and analyses the results of SyGuS-Comp'16., Comment: In Proceedings SYNT 2016, arXiv:1611.07178. arXiv admin note: text overlap with arXiv:1602.01170

"Natural Language," whether spoken and attended to by humans, or processed and generated by computers, requires networked structures that reflect creative processes in semantic, syntactic, phonetic, linguistic, social, emotional, and cultural modules. Being able to produce novel and useful behavior following repeated practice gets to the root of both artificial intelligence and human language. This paper investigates the modalities involved in language-like applications that computers -- and programmers -- engage with, and aims to fine tune the questions we ask to better account for context, self-awareness, and embodiment., Comment: 6 pages, including 1 figure and 3 tables; accepted for presentation at IJCAI2016 Workshop on Language Sense on Computers

DASH is a library of distributed data structures and algorithms designed for running the applications on modern HPC architectures, composed of hierarchical network interconnections and stratified memory. DASH implements a PGAS (partitioned global address space) model in the form of C++ templates, built on top of DART -- a run-time system with an abstracted tier above existing one-sided communication libraries. In order to facilitate the application development process for exploiting the hierarchical organization of HPC machines, DART allows to reorder the placement of the computational units. In this paper we present an automatic, hierarchical units mapping technique (using a similar approach to the Hilbert curve transformation) to reorder the placement of DART units on the Cray XC40 machine Hazel Hen at HLRS. To evaluate the performance of new units mapping which takes into the account the topology of allocated compute nodes, we perform latency benchmark for a 3D stencil code. The technique of units mapping is generic and can be be adopted in other DART communication substrates and on other hardware platforms. Furthermore, high--level features of DASH are presented, enabling more complex automatic transformations and optimizations in the future., Comment: 10 pages, 8 figures, Program Transformation for Programmability in Heterogeneous Architectures (PROHA) Workshop, held in conjunction with the International Symposium on Code Generation and Optimization (CGO) 2016

A combination of hierarchical tree-like data structures and data access patterns from fast multipole methods and hierarchical low-rank approximation of linear operators from H-matrix methods appears to form an algorithmic path forward for efficient implementation of many linear algebraic operations of scientific computing at the exascale. The combination provides asymptotically optimal computational and communication complexity and applicability to large classes of operators that commonly arise in scientific computing applications. A convergence of the mathematical theories of the fast multipole and H-matrix methods has been underway for over a decade. We recap this mathematical unification and describe implementation aspects of a hybrid of these two compelling hierarchical algorithms on hierarchical distributed-shared memory architectures, which are likely to be the first to reach the exascale. We present a new communication complexity estimate for fast multipole methods on such architectures. We also show how the data structures and access patterns of H-matrices for low-rank operators map onto those of fast multipole, leading to an algebraically generalized form of fast multipole that compromises none of its architecturally ideal properties.

Fast multipole methods (FMM) on distributed mem- ory have traditionally used a bulk-synchronous model of com- municating the local essential tree (LET) and overlapping it with computation of the local data. This could be perceived as an extreme case of data aggregation, where the whole LET is communicated at once. Charm++ allows a much finer control over the granularity of communication, and has a asynchronous execution model that fits well with the structure of our FMM code. Unlike previous work on asynchronous fast N-body methods such as ChaNGa and PEPC, the present work performs a direct comparison against the traditional bulk-synchronous approach and the asynchronous approach using Charm++. Furthermore, the serial performance of our FMM code is over an order of magnitude better than these previous codes, so it is much more challenging to hide the overhead of Charm++.

Exascale systems are predicted to have approximately one billion cores, assuming Gigahertz cores. Limitations on affordable network topologies for distributed memory systems of such massive scale bring new challenges to the current parallel programing model. Currently, there are many efforts to evaluate the hardware and software bottlenecks of exascale designs. There is therefore an urgent need to model application performance and to understand what changes need to be made to ensure extrapolated scalability. The fast multipole method (FMM) was originally developed for accelerating N-body problems in astrophysics and molecular dynamics, but has recently been extended to a wider range of problems, including preconditioners for sparse linear solvers. It's high arithmetic intensity combined with its linear complexity and asynchronous communication patterns makes it a promising algorithm for exascale systems. In this paper, we discuss the challenges for FMM on current parallel computers and future exascale architectures, with a focus on inter-node communication. We develop a performance model that considers the communication patterns of the FMM, and observe a good match between our model and the actual communication time, when latency, bandwidth, network topology, and multi-core penalties are all taken into account. To our knowledge, this is the first formal characterization of inter-node communication in FMM, which validates the model against actual measurements of communication time.

The present work attempts to integrate the independent efforts in the fast N-body community to create the fastest N-body library for many-core and heterogenous architectures. Focus is placed on low accuracy optimizations, in response to the recent interest to use FMM as a preconditioner for sparse linear solvers. A direct comparison with other state-of-the-art fast N-body codes demonstrates that orders of magnitude increase in performance can be achieved by careful selection of the optimal algorithm and low-level optimization of the code. The current N-body solver uses a fast multipole method with an efficient strategy for finding the list of cell-cell interactions by a dual tree traversal. A task-based threading model is used to maximize thread-level parallelism and intra-node load-balancing. In order to extract the full potential of the SIMD units on the latest CPUs, the inner kernels are optimized using AVX instructions. Our code -- exaFMM -- is an order of magnitude faster than the current state-of-the-art FMM codes, which are themselves an order of magnitude faster than the average FMM code.

This paper presents a new approach to select events of interest to a user in a social media setting where events are generated by the activities of the user's friends through their mobile devices. We argue that given the unique requirements of the social media setting, the problem is best viewed as an inductive learning problem, where the goal is to first generalize from the users' expressed "likes" and "dislikes" of specific events, then to produce a program that can be manipulated by the system and distributed to the collection devices to collect only data of interest. The key contribution of this paper is a new algorithm that combines existing machine learning techniques with new program synthesis technology to learn users' preferences. We show that when compared with the more standard approaches, our new algorithm provides up to order-of-magnitude reductions in model training time, and significantly higher prediction accuracies for our target application. The approach also improves on standard machine learning techniques in that it produces clear programs that can be manipulated to optimize data collection and filtering.

Fast multipole methods have O(N) complexity, are compute bound, and require very little synchronization, which makes them a favorable algorithm on next-generation supercomputers. Their most common application is to accelerate N-body problems, but they can also be used to solve boundary integral equations. When the particle distribution is irregular and the tree structure is adaptive, load-balancing becomes a non-trivial question. A common strategy for load-balancing FMMs is to use the work load from the previous step as weights to statically repartition the next step. The authors discuss in the paper another approach based on data-driven execution to efficiently tackle this challenging load-balancing problem. The core idea consists of breaking the most time-consuming stages of the FMMs into smaller tasks. The algorithm can then be represented as a Directed Acyclic Graph (DAG) where nodes represent tasks, and edges represent dependencies among them. The execution of the algorithm is performed by asynchronously scheduling the tasks using the QUARK runtime environment, in a way such that data dependencies are not violated for numerical correctness purposes. This asynchronous scheduling results in an out-of-order execution. The performance results of the data-driven FMM execution outperform the previous strategy and show linear speedup on a quad-socket quad-core Intel Xeon system.

The authors' "metatools" are a collection of tools for generic programming. This includes generating Java sources from mathematically well-founded specifications, as well as the creation of strictly typed document object models for XML encoded texts. In this context, almost every computer-internal structure is treated as a "model", and every computation is a kind of model transformation. This concept differs significantly from "classical model transformation" executed by specialized tools and languages. Therefore it seemed promising to the organizers of the TTC 2011, as well as to the authors, to apply metatools to one of the challenges, namely to the "compiler optimization task". This is a report on the resulting experiences., Comment: In Proceedings TTC 2011, arXiv:1111.4407

With the current hybridization of treecodes and FMMs, combined with auto-tuning capabilities on heterogeneous architectures, the flexibility of fast N-body methods has been greatly enhanced. These features are a requirement to developing a black-box software library for fast N-body algorithms on heterogeneous systems, which is our immediate goal.

We propose a realizability interpretation of a system for quantifier free arithmetic which is equivalent to the fragment of classical arithmetic without "nested" quantifiers, called here EM1-arithmetic. We interpret classical proofs as interactive learning strategies, namely as processes going through several stages of knowledge and learning by interacting with the "environment" and with each other. We give a categorical presentation of the interpretation through the construction of two suitable monads.

High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that support this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success., Comment: Submitted to Parallel Computing, Elsevier

I will present my implementation 'n-units' of physical units into C++ programs. It allows the compiler to check for dimensional consistency., Comment: submitted to "Computing in Science and Engineering"

We study here schedulers for a class of rules that naturally arise in the context of rule-based constraint programming. We systematically derive a scheduler for them from a generic iteration algorithm of [Apt 2000]. We apply this study to so-called membership rules of [Apt and Monfroy 2001]. This leads to an implementation that yields a considerably better performance for these rules than their execution as standard CHR rules. Finally, we show how redundant rules can be identified and how appropriately reduced sets of rules can be computed., Comment: 25 pages, to appear in the journal "Theory and Practice of Logic Programming"

We propose a set of transformation rules for constraint logic programs with negation. We assume that every program is locally stratified and, thus, it has a unique perfect model. We give sufficient conditions which ensure that the proposed set of transformation rules preserves the perfect model of the programs. Our rules extend in some respects the rules for logic programs and constraint logic programs already considered in the literature and, in particular, they include a rule for unfolding a clause with respect to a negative literal., Comment: To appear in: M. Bruynooghe, K.-K. Lau (Eds.) Program Development in Computational Logic, Lecture Notes in Computer Science, Springer

Program specialization is a program transformation methodology which improves program efficiency by exploiting the information about the input data which are available at compile time. We show that current techniques for program specialization based on partial evaluation do not perform well on nondeterministic logic programs. We then consider a set of transformation rules which extend the ones used for partial evaluation, and we propose a strategy for guiding the application of these extended rules so to derive very efficient specialized programs. The efficiency improvements which sometimes are exponential, are due to the reduction of nondeterminism and to the fact that the computations which are performed by the initial programs in different branches of the computation trees, are performed by the specialized programs within single branches. In order to reduce nondeterminism we also make use of mode information for guiding the unfolding process. To exemplify our technique, we show that we can automatically derive very efficient matching programs and parsers for regular languages. The derivations we have performed could not have been done by previously known partial evaluation techniques., Comment: 74 pages. To appear in: Higher-Order and Symbolic Computation (Special Issue in Honor of Bob Paige)

We present a program synthesis method based on unfold/fold transformation rules which can be used for deriving terminating definite logic programs from formulas of the Weak Monadic Second Order theory of one successor (WS1S). This synthesis method can also be used as a proof method which is a decision procedure for closed formulas of WS1S. We apply our synthesis method for translating CLP(WS1S) programs into logic programs and we use it also as a proof method for verifying safety properties of infinite state systems., Comment: 25 pages. Full version of a paper that appears in: M. Leuschel (ed.) Proceedings of LOPSTR'02, Twelfth International Workshop on Logic-based Program Development and Transformation, Madrid, Spain, 17-20 Sept. 2002. Lecture Notes in Computer Science 2664. Springer-Verlag Berlin Heidelberg, 2003, pp. 160-181

We consider a simple extension of logic programming where variables may range over goals and goals may be arguments of predicates. In this language we can write logic programs which use goals as data. We give practical evidence that, by exploiting this capability when transforming programs, we can improve program efficiency. We propose a set of program transformation rules which extend the familiar unfolding and folding rules and allow us to manipulate clauses with goals which occur as arguments of predicates. In order to prove the correctness of these transformation rules, we formally define the operational semantics of our extended logic programming language. This semantics is a simple variant of LD-resolution. When suitable conditions are satisfied this semantics agrees with LD-resolution and, thus, the programs written in our extended language can be run by ordinary Prolog systems. Our transformation rules are shown to preserve the operational semantics and termination., Comment: 51 pages. Full version of a paper that will appear in Theory and Practice of Logic Programming, Cambridge University Press, UK

The system PL permits the translation of abstract proofs of program correctness into programs in a variety of programming languages. A programming language satisfying certain axioms may be the target of such a translation. The system PL also permits the construction and proof of correctness of programs in an abstract programming language, and permits the translation of these programs into correct programs in a variety of languages. The abstract programming language has an imperative style of programming with assignment statements and side-effects, to allow the efficient generation of code. The abstract programs may be written by humans and then translated, avoiding the need to write the same program repeatedly in different languages or even the same language. This system uses classical logic, is conceptually simple, and permits reasoning about nonterminating programs using Scott-Strachey style denotational semantics., Comment: Internal report

The so called ``cogen approach'' to program specialisation, writing a compiler generator instead of a specialiser, has been used with considerable success in partial evaluation of both functional and imperative languages. This paper demonstrates that the cogen approach is also applicable to the specialisation of logic programs (also called partial deduction) and leads to effective specialisers. Moreover, using good binding-time annotations, the speed-ups of the specialised programs are comparable to the speed-ups obtained with online specialisers. The paper first develops a generic approach to offline partial deduction and then a specific offline partial deduction method, leading to the offline system LIX for pure logic programs. While this is a usable specialiser by itself, it is used to develop the cogen system LOGEN. Given a program, a specification of what inputs will be static, and an annotation specifying which calls should be unfolded, LOGEN generates a specialised specialiser for the program at hand. Running this specialiser with particular values for the static inputs results in the specialised program. While this requires two steps instead of one, the efficiency of the specialisation process is improved in situations where the same program is specialised multiple times. The paper also presents and evaluates an automatic binding-time analysis that is able to derive the annotations. While the derived annotations are still suboptimal compared to hand-crafted ones, they enable non-expert users to use the LOGEN system in a fully automated way. Finally, LOGEN is extended so as to directly support a large part of Prolog's declarative and non-declarative features and so as to be able to perform so called mixline specialisations., Comment: 52 pages, to appear in the journal "Theory and Practice of Logic Programming"

A typed model of strategic term rewriting is developed. The key innovation is that generic traversal is covered. To this end, we define a typed rewriting calculus S'_{gamma}. The calculus employs a many-sorted type system extended by designated generic strategy types gamma. We consider two generic strategy types, namely the types of type-preserving and type-unifying strategies. S'_{gamma} offers traversal combinators to construct traversals or schemes thereof from many-sorted and generic strategies. The traversal combinators model different forms of one-step traversal, that is, they process the immediate subterms of a given term without anticipating any scheme of recursion into terms. To inhabit generic types, we need to add a fundamental combinator to lift a many-sorted strategy $s$ to a generic type gamma. This step is called strategy extension. The semantics of the corresponding combinator states that s is only applied if the type of the term at hand fits, otherwise the extended strategy fails. This approach dictates that the semantics of strategy application must be type-dependent to a certain extent. Typed strategic term rewriting with coverage of generic term traversal is a simple but expressive model of generic programming. It has applications in program transformation and program analysis., Comment: 85 pages, submitted for publication to the Journal of Logic and Algebraic Programming

We present a new characterization of termination of general logic programs. Most existing termination analysis approaches rely on some static information about the structure of the source code of a logic program, such as modes/types, norms/level mappings, models/interargument relations, and the like. We propose a dynamic approach which employs some key dynamic features of an infinite (generalized) SLDNF-derivation, such as repetition of selected subgoals and recursive increase in term size. We also introduce a new formulation of SLDNF-trees, called generalized SLDNF-trees. Generalized SLDNF-trees deal with negative subgoals in the same way as Prolog and exist for any general logic programs., Comment: To appear in ACM TOCL

We study program refactoring while considering the language or even the programming paradigm as a parameter. We use typed functional programs, namely Haskell programs, as the specification medium for a corresponding refactoring framework. In order to detach ourselves from language syntax, our specifications adhere to the following style. (I) As for primitive algorithms for program analysis and transformation, we employ generic function combinators supporting generic traversal and polymorphic functions refined by ad-hoc cases. (II) As for the language abstractions involved in refactorings, we design a dedicated multi-parameter class. This class can be instantiated for abstractions as present in various languages, e.g., Java, Prolog or Haskell.

Program specialisation aims at improving the overall performance of programs by performing source to source transformations. A common approach within functional and logic programming, known respectively as partial evaluation and partial deduction, is to exploit partial knowledge about the input. It is achieved through a well-automated application of parts of the Burstall-Darlington unfold/fold transformation framework. The main challenge in developing systems is to design automatic control that ensures correctness, efficiency, and termination. This survey and tutorial presents the main developments in controlling partial deduction over the past 10 years and analyses their respective merits and shortcomings. It ends with an assessment of current achievements and sketches some remaining research challenges., Comment: To appear in Theory and Practice of Logic Programming

Recent years have seen an increasing need of high-level specification languages and tools generating code from specifications. In this paper, we introduce a specification language, {\splname}, which is tailored to the writing of syntactic theories of language semantics. More specifically, the language supports specifying primitive notions such as dynamic constraints, contexts, axioms, and inference rules. We also introduce a system which generates interpreters from {\splname} specifications. A prototype system is implemented and has been tested on a number of examples, including a syntactic theory for Verilog., Comment: Accepted in Rule-based Programming Workshop, 2000, 16 pages

We introduce a set of eight universal Rules of Inference by which computer programs with known properties (axioms) are transformed into new programs with known properties (theorems). Axioms are presented to formalize a segment of Number Theory, DataBase retrieval and Computability Theory. The resulting Program Calculus is used to generate programs to (1) Determine if one number is a factor of another. (2) List all employees who earn more than their manager. (3) List the set of programs that halt no on themselves, thus proving that it is recursively enumerable. The well-known fact that the set of programs that do not halt yes on themselves is not recursively enumerable is formalized as a program requirement that has no solution, an Incompleteness Axiom. Thus, any axioms (programs) which could be used to generate this program are themselves unattainable. Such proofs are presented to formally generate several additional theorems, including (4) The halting problem is unsolvable. Open problems and future research is discussed, including the use of temporary sort files, programs that calculate statistics (such as counts and sums), the synthesis of programs to solve other well-known problems from Number Theory, Logic, DataBase retrieval and Computability Theory, application to Programming Language Semantics, and the formalization of incompleteness results from Logic and the semantic paradoxes., Comment: for submission to Journal of the ACM