We consider the problem of inferring a grammar describing the output of a functional program given a grammar describing its input. Solutions to this problem are helpful for detecting bugs or proving safety properties of functional programs, and several rewriting tools exist for solving this problem. However, known grammar inference techniques are not able to take evaluation strategies of the program into account. This yields very imprecise results when the evaluation strategy matters. In this work, we adapt the Tree Automata Completion algorithm to approximate accurately the set of terms reachable by rewriting under the innermost strategy. We formally prove that the proposed technique is sound and precise w.r.t. innermost rewriting. We show that those results can be extended to the leftmost and rightmost innermost case. The algorithms for the general innermost case have been implemented in the Timbuk reachability tool. Experiments show that it noticeably improves the accuracy of static analysis for functional programs using the call-by-value evaluation strategy.
Data flow analysis of higher-order functional programs is a long standing and very active research topic @cite_21 @cite_5 @cite_24 . Used techniques range from tree grammars to specific formalisms: HORS, PMRS or ILTGs and can deal with higher-order functions. We have shown, on an example, that defining an analysis taking the call-by-value evaluation strategy was also possible on higher-order functions. However, this has to be investigated more deeply. Application of innermost completion to higher order function would provide nice improvements on static analysis techniques. Indeed, state of the art techniques like @cite_21 @cite_5 @cite_24 do not take evaluation strategies into account, and analysis results are thus coarse when program execution relies on a specific strategy.
@cite_24 @cite_5 @cite_21
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Compare first-order functional programs with higher-order programs allowing functions as function parameters. Can the the first program class solve fewer problems than the second? The answer is no: both classes are Turing complete, meaning that they can compute all partial recursive functions. In particular, higher-order values may be first-order simulated by use of the list constructor ‘cons’ to build function closures. This paper uses complexity theory to prove some expressivity results about small programming languages that are less than Turing complete. Complexity classes of decision problems are used to characterize the expressive power of functional programming language features. An example: second-order programs are more powerful than first-order, since a function f of type lBoolr-〉Bool is computable by a cons-free first-order functional program if and only if f is in PTIME, whereas f is computable by a cons-free second-order program if and only if f is in EXPTIME. Exact characterizations are given for those problems of type lBoolr-〉Bool solvable by programs with several combinations of operations on data: presence or absence of constructors; the order of data values: 0, 1, or higher; and program control structures: general recursion, tail recursion, primitive recursion. In standard control-flow analyses for higher-order languages, a single abstract binding for a variable represents a set of exact bindings, and a single abstract reference cell represents a set of exact reference cells. While such analyses provide useful may-alias information, they are unable to answer mustalias questions about variables and cells, as these questions ask about equality of specific bindings and references.In this paper, we present a novel program analysis for higher-order languages that answers must-alias questions. At every program point, the analysis associates with each variable and abstract cell a cardinality, which is either single or multiple. If variable x is single at program point p, then all bindings for x in the heap reachable from the environment at p hold the same value. If abstract cell r is single at p, then at most one exact cell corresponding to r is reachable from the environment at p.Must-alias information facilitates various program optimizations such as lightweight closure conversion [19]. In addition, must-alias information permits analyses to perform strong updates [3] on abstract reference cells known to be single. Strong updates improve analysis precision for programs that make significant use of state.A prototype implementation of our analysis yields encouraging results. Over a range of benchmarks, our analysis classifies a large majority of the variables as single. Existing program analysis tools that implement abstraction rely on saturating procedures to compute over-approximations of fixpoints. As an alternative, we propose a new algorithm to compute an over-approximation of the set of reachable states of a program by replacing loops in the control flow graph by their abstract transformer. Our technique is able to generate diagnostic information in case of property violations, which we call leaping counterexamples. We have implemented this technique and report experimental results on a set of large ANSI-C programs using abstract domains that focus on properties related to string-buffers. Any static, global analysis of the expression and data relationships in a program requires a knowledge of the control flow of the program. Since one of the primary reasons for doing such a global analysis in a compiler is to produce optimized programs, control flow analysis has been embedded in many compilers and has been described in several papers. An early paper by Prosser [5] described the use of Boolean matrices (or, more particularly, connectivity matrices) in flow analysis. The use of “dominance” relationships in flow analysis was first introduced by Prosser and much expanded by Lowry and Medlock [6]. References [6,8,9] describe compilers which use various forms of control flow analysis for optimization. Some recent developments in the area are reported in [4] and in [7]. The underlying motivation in all the different types of control flow analysis is the need to codify the flow relationships in the program. The codification may be in connectivity matrices, in predecessor-successor tables, in dominance lists, etc. Whatever the form, the purpose is to facilitate determining what the flow relationships are; in other words to facilitate answering such questions as: is this an inner loop?, if an expression is removed from the loop where can it be correctly and profitably placed?, which variable definitions can affect this use? In this paper the basic control flow relationships are expressed in a directed graph. Various graph constructs are then found and shown to codify interesting global relationships.