970 research outputs found
Inferring pure context-free languages from positive data
We study the possibilities to infer pure context-free languages from positive data. We can show that while the whole class of pure context-free languages is not inferable from positive data, it has interesting subclasses which have the desired inference property. We study uniform pure languages, i.e., languages generated by pure grammars obeying restrictions on the length of the right hand sides of their productions, and pure languages generated by deterministic pure grammars
Inductive inference of formal languages from positive data
We consider inductive inference of formal languages, as defined by Gold (1967), in the case of positive data, i.e., when the examples of a given formal language are successive elements of some arbitrary enumeration of the elements of the language. We prove a theorem characterizing when an indexed family of nonempty recursive formal languages is inferrable from positive data. From this theorem we obtain other useful conditions for inference from positive data, and give several examples of their application. We give counterexamples to two variants of the characterizing condition, and investigate conditions for inference from positive data that avoids “overgeneralization.
JWalk: a tool for lazy, systematic testing of java classes by design introspection and user interaction
Popular software testing tools, such as JUnit, allow frequent retesting of modified code; yet the manually created test scripts are often seriously incomplete. A unit-testing tool called JWalk has therefore been developed to address the need for systematic unit testing within the context of agile methods. The tool operates directly on the compiled code for Java classes and uses a new lazy method for inducing the changing design of a class on the fly. This is achieved partly through introspection, using Java’s reflection capability, and partly through interaction with the user, constructing and saving test oracles on the fly. Predictive rules reduce the number of oracle values that must be confirmed by the tester. Without human intervention, JWalk performs bounded exhaustive exploration of the class’s method protocols and may be directed to explore the space of algebraic constructions, or the intended design state-space of the tested class. With some human interaction, JWalk performs up to the equivalent of fully automated state-based testing, from a specification that was acquired incrementally
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Combining Static and Dynamic Analysis for Bug Detection and Program Understanding
This work proposes new combinations of static and dynamic analysis for bug detection and program understanding. There are 3 related but largely independent directions: a) In the area of dynamic invariant inference, we improve the consistency of dynamically discovered invariants by taking into account second-order constraints that encode knowledge aboutinvariants; the second-order constraints are either supplied by the programmer or vetted by the programmer (among candidate constraints suggested automatically); b) In the area of testing dataflow (esp. map-reduce) programs, our tool, SEDGE, achieves higher testing coverage by leveraging existinginput data and generalizing them using a symbolic reasoning engine (a powerful SMT solver); c) In the area of bug detection, we identify and present the concept of residual investigation: a dynamic analysis that serves as theruntime agent of a static analysis. Residual investigation identifies with higher certainty whether an error reported by the static analysis is likely true
Really Natural Linear Indexed Type Checking
Recent works have shown the power of linear indexed type systems for
enforcing complex program properties. These systems combine linear types with a
language of type-level indices, allowing more fine-grained analyses. Such
systems have been fruitfully applied in diverse domains, including implicit
complexity and differential privacy. A natural way to enhance the
expressiveness of this approach is by allowing the indices to depend on runtime
information, in the spirit of dependent types. This approach is used in DFuzz,
a language for differential privacy. The DFuzz type system relies on an index
language supporting real and natural number arithmetic over constants and
variables. Moreover, DFuzz uses a subtyping mechanism to make types more
flexible. By themselves, linearity, dependency, and subtyping each require
delicate handling when performing type checking or type inference; their
combination increases this challenge substantially, as the features can
interact in non-trivial ways. In this paper, we study the type-checking problem
for DFuzz. We show how we can reduce type checking for (a simple extension of)
DFuzz to constraint solving over a first-order theory of naturals and real
numbers which, although undecidable, can often be handled in practice by
standard numeric solvers
Dynamic Protocol Reverse Engineering a Grammatical Inference Approach
Round trip engineering of software from source code and reverse engineering of software from binary files have both been extensively studied and the state-of-practice have documented tools and techniques. Forward engineering of protocols has also been extensively studied and there are firmly established techniques for generating correct protocols. While observation of protocol behavior for performance testing has been studied and techniques established, reverse engineering of protocol control flow from observations of protocol behavior has not received the same level of attention. State-of-practice in reverse engineering the control flow of computer network protocols is comprised of mostly ad hoc approaches. We examine state-of-practice tools and techniques used in three open source projects: Pidgin, Samba, and rdesktop . We examine techniques proposed by computational learning researchers for grammatical inference. We propose to extend the state-of-art by inferring protocol control flow using grammatical inference inspired techniques to reverse engineer automata representations from captured data flows. We present evidence that grammatical inference is applicable to the problem domain under consideration
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