18,452 research outputs found
Incremental and Modular Context-sensitive Analysis
Context-sensitive global analysis of large code bases can be expensive, which
can make its use impractical during software development. However, there are
many situations in which modifications are small and isolated within a few
components, and it is desirable to reuse as much as possible previous analysis
results. This has been achieved to date through incremental global analysis
fixpoint algorithms that achieve cost reductions at fine levels of granularity,
such as changes in program lines. However, these fine-grained techniques are
not directly applicable to modular programs, nor are they designed to take
advantage of modular structures. This paper describes, implements, and
evaluates an algorithm that performs efficient context-sensitive analysis
incrementally on modular partitions of programs. The experimental results show
that the proposed modular algorithm shows significant improvements, in both
time and memory consumption, when compared to existing non-modular, fine-grain
incremental analysis techniques. Furthermore, thanks to the proposed
inter-modular propagation of analysis information, our algorithm also
outperforms traditional modular analysis even when analyzing from scratch.Comment: 56 pages, 27 figures. To be published in Theory and Practice of Logic
Programming. v3 corresponds to the extended version of the ICLP2018 Technical
Communication. v4 is the revised version submitted to Theory and Practice of
Logic Programming. v5 (this one) is the final author version to be published
in TPL
A generic framework for context-sensitive analysis of modular programs
Context-sensitive analysis provides information which is potentially more accurate than that provided by context-free analysis. Such information can then be applied in order to validate/debug the program and/or to specialize the program obtaining important improvements. Unfortunately, context-sensitive analysis of modular programs poses important theoretical and practical problems. One solution, used in several proposals, is to resort to context-free analysis. Other proposals do address
context-sensitive analysis, but are only applicable when the description domain used satisfies rather restrictive properties. In this paper, we argĂŒe that a general framework for context-sensitive analysis of modular programs, Le., one that allows using all the domains which have proved useful in practice in the non-modular setting, is indeed feasible and very useful. Driven by our experience in the design and implementation of analysis and specialization techniques in the context of CiaoPP, the Ciao
system preprocessor, in this paper we discuss a number of design goals for context-sensitive analysis of modular programs as well as the problems which arise in trying to meet these goals. We also provide a high-level description of a framework for analysis of modular programs which does
substantially meet these objectives. This framework is generic in that it can be instantiated in different ways in order to adapt to different contexts. Finally, the behavior of the different instantiations w.r.t. the design goals that motivate our work is also discussed
An integration of partial evaluation in a generic abstract interpretation framework
Information generated by abstract interpreters has long been
used to perform program specialization. Additionally, if the
abstract interpreter generates a multivariant analysis, it is also possible to perform mĂșltiple specialization. Information about valĂșes of variables is propagated by simulating program execution and performing fixpoint computations for recursive calis. In contrast, traditional partial evaluators (mainly) use unfolding for both propagating valĂșes of variables and transforming the program. It is known that abstract interpretation is a better technique for propagating success valĂșes than unfolding. However, the program transformations induced by unfolding may lead to important optimizations which are not directly achievable in the existing frameworks for mĂșltiple specialization based on abstract interpretation. The aim of this work is to devise a specialization framework which integrates the better information propagation of abstract interpretation with the powerful program transformations performed by partial evaluation, and which can be implemented via small modifications to existing generic abstract interpreters. With this aim, we will relate top-down abstract interpretation with traditional concepts in partial evaluation and sketch how the sophisticated techniques developed for controlling partial evaluation can be adapted to the proposed specialization framework. We conclude that there can be both practical and conceptual advantages in the proposed integration of partial evaluation
and abstract interpretation
A generic framework for the analysis and specialization of logic programs
The relationship between abstract interpretation and partial
deduction has received considerable attention and (partial) integrations have been proposed starting from both the partial deduction and abstract interpretation perspectives. In this work we present what we argĂŒe is the first fully described generic algorithm for efñcient and precise integration of abstract interpretation and partial deduction. Taking as starting point state-of-the-art algorithms for context-sensitive, polyvariant abstract interpretation and (abstract) partial deduction, we present
an algorithm which combines the best of both worlds. Key ingredients include the accurate success propagation inherent to abstract interpretation and the powerful program transformations achievable by partial deduction. In our algorithm, the calis which appear in the analysis graph
are not analyzed w.r.t. the original definition of the procedure but w.r.t. specialized definitions of these procedures. Such specialized definitions are obtained by applying both unfolding and abstract executability. Our framework is parametric w.r.t. different control strategies and abstract domains. Different combinations of such parameters correspond to existing algorithms for program analysis and specialization. Simultaneously, our approach opens the door to the efñcient computation of strictly more
precise results than those achievable by each of the individual techniques.
The algorithm is now one of the key components of the CiaoPP analysis
and specialization system
A Practical Type Analysis for Verification of Modular Prolog Programs
Regular types are a powerful tool for computing very precise descriptive types for logic programs. However, in the context of real life, modular Prolog programs, the accurate results obtained by regular types often come at the price of efficiency. In this paper we propose a combination of techniques aimed at improving analysis efficiency in this context. As a first technique we allow optionally reducing the accuracy of inferred types by using only the types defined by the user or present in the libraries. We claim that, for the purpose of verifying type signatures given in the form of assertions the precision obtained using this approach is sufficient, and show that analysis times can be reduced significantly. Our second technique is aimed at dealing with situations where we would like to limit the amount of reanalysis performed, especially for library modules. Borrowing some ideas from polymorphic type systems, we show how to solve the problem by admitting parameters in type specifications. This allows us to compose new call patterns with some pre computed analysis info without losing any information. We argue that together these two techniques contribute to the practical and scalable analysis and verification of types in Prolog programs
Program development using abstract interpretation (and the ciao system preprocessor)
The technique of Abstract Interpretation has allowed the development of very sophisticated global program analyses which are at the same time provably correct and practical. We present in a tutorial fashion a novel program development framework which uses abstract interpretation
as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system librarles), to genĂ©rate and simplify run-time tests, and to perform high-level program transformations such as mĂșltiple abstract specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, non-failure, and bounds on resource consumption (time or space cost). CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements the described functionality, will be used to illustrate the fundamental ideas
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