16 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
Incremental analysis of logic programs
Global analyzers traditionally read and analyze the entire program at once, in a non-incremental way. However, there are many situations which are not well suited to this simple model and which instead require reanalysis of certain parts of a program which has already been analyzed. In these cases, it appears inefficient to perform the analysis of the program again from scratch, as needs to be done with current systems. We describe how the fixpoint algorithms in current generic analysis engines can be extended to support incremental analysis. The possible changes to a program are classified into three types: addition, deletion, and arbitrary change. For each one of these, we provide one or more algorithms for identifying the parts of the analysis that must be recomputed and for performing the actual recomputation. The potential benefits and drawbacks of these algorithms are discussed. Finally, we present some experimental results obtained with an implementation of the algorithms in the PLAI generic abstract interpretation framework. The results show significant benefits when using the proposed incremental analysis algorithms
A Systematic Approach to Constructing Incremental Topology Control Algorithms Using Graph Transformation
Communication networks form the backbone of our society. Topology control
algorithms optimize the topology of such communication networks. Due to the
importance of communication networks, a topology control algorithm should
guarantee certain required consistency properties (e.g., connectivity of the
topology), while achieving desired optimization properties (e.g., a bounded
number of neighbors). Real-world topologies are dynamic (e.g., because nodes
join, leave, or move within the network), which requires topology control
algorithms to operate in an incremental way, i.e., based on the recently
introduced modifications of a topology. Visual programming and specification
languages are a proven means for specifying the structure as well as
consistency and optimization properties of topologies. In this paper, we
present a novel methodology, based on a visual graph transformation and graph
constraint language, for developing incremental topology control algorithms
that are guaranteed to fulfill a set of specified consistency and optimization
constraints. More specifically, we model the possible modifications of a
topology control algorithm and the environment using graph transformation
rules, and we describe consistency and optimization properties using graph
constraints. On this basis, we apply and extend a well-known constructive
approach to derive refined graph transformation rules that preserve these graph
constraints. We apply our methodology to re-engineer an established topology
control algorithm, kTC, and evaluate it in a network simulation study to show
the practical applicability of our approachComment: This document corresponds to the accepted manuscript of the
referenced journal articl
Incrementalizing Lattice-Based Program Analyses in Datalog
Program analyses detect errors in code, but when code changes frequently as in an IDE, repeated re-analysis from-scratch is unnecessary: It leads to poor performance unless we give up on precision and recall. Incremental program analysis promises to deliver fast feedback without giving up on precision or recall by deriving a new analysis result from the previous one. However, Datalog and other existing frameworks for incremental program analysis are limited in expressive power: They only support the powerset lattice as representation of analysis results, whereas many practically relevant analyses require custom lattices and aggregation over lattice values. To this end, we present a novel algorithm called DRedL that supports incremental maintenance of recursive lattice-value aggregation in Datalog. The key insight of DRedL is to dynamically recognize increasing replacements of old lattice values by new ones, which allows us to avoid the expensive deletion of the old value. We integrate DRedL into the analysis framework IncA and use IncA to realize incremental implementations of strong-update points-to analysis and string analysis for Java. As our performance evaluation demonstrates, both analyses react to code changes within milliseconds
Incremental graph pattern matching
Graph pattern matching has become a routine process in emerging applications such as social networks. In practice a data graph is typically large, and is frequently updated with small changes. It is often prohibitively expensive to recom-pute matches from scratch via batch algorithms when the graph is updated. With this comes the need for incremental algorithms that compute changes to the matches in response to updates, to minimize unnecessary recomputation. This paper investigates incremental algorithms for graph pattern matching defined in terms of graph simulation, bounded sim-ulation and subgraph isomorphism. (1) For simulation, we provide incremental algorithms for unit updates and certain graph patterns. These algorithms are optimal: in linear time in the size of the changes in the input and output, which characterizes the cost that is inherent to the problem itself. For general patterns we show that the incremental match-ing problem is unbounded, i.e., its cost is not determined by the size of the changes alone. (2) For bounded simula-tion, we show that the problem is unbounded even for unit updates and path patterns. (3) For subgraph isomorphism, we show that the problem is intractable and unbounded for unit updates and path patterns. (4) For multiple updates, we develop an incremental algorithm for each of simulation, bounded simulation and subgraph isomorphism. We exper-imentally verify that these incremental algorithms signifi-cantly outperform their batch counterparts in response to small changes, using real-life data and synthetic data. Categories and Subject Descriptors: F.2 [Analysis of algorithms and problem complexity]: Nonnumerical algo-rithms and problems[pattern matching
Effectful program distancing
International audienceSemantics is traditionally concerned with program equivalence, in which all pairs of programs which are not equivalent are treated the same, and simply dubbed as incomparable. In recent years, various forms of program metrics have been introduced such that the distance between non-equivalent programs is measured as an element of an appropriate quantale. By letting the underlying quantale vary as the type of the compared programs become more complex, the recently introduced framework of differential logical relations allows for a new contextual form of reasoning. In this paper, we show that all this can be generalised to effectful higher-order programs, in which not only the values , but also the effects computations produce can be appropriately distanced in a principled way. We show that the resulting framework is flexible, allowing various forms of effects to be handled, and that it provides compact and informative judgments about program differences