Abstract:
Various technologies described herein pertain to executing a mixed query to search a database retained in a data repository. The mixed query includes a regular expression, which is a pattern of elements, and a semantic constraint. The elements in the regular expression include a first wildcard, where the semantic constraint restricts a meaning of the first wildcard. Moreover, the elements in the regular expression include explicit lexical constraint(s) and/or disparate wildcard(s). For instance, semantic constraint(s) can restrict meaning(s) of the disparate wildcard(s). The mixed query is executed to retrieve results that match the pattern of the elements in the regular expression and satisfy the semantic constraint(s).
Abstract:
A system that facilitates computing a symbolic bound with respect to a procedure that is executable by a processor on a computing device is described herein. The system includes a transition system generator component that receives the procedure and computes a disjunctive transition system for a control location in the procedure. A compute bound component computes a bound for the transition system, wherein the bound is expressed in terms of inputs to the transition system. The system further includes a translator component that translates the bound computed by the compute bound component such that the bound is expressed in terms of inputs to the procedure.
Abstract:
Generalization and/or specialization of code fragments is described, for example, as part of a tool for software developers. In an embodiment, a developer inserts natural language expressing a programming task into code he or she is developing in an integrated development environment; a program synthesizer obtains relevant (possibly non-compiling) code fragments for the task, merges those together to form a snippet, specializes the snippet for the context of the code and inserts the specialized snippet into the code. For example, a pair of code fragments are obtained from a search engine and are merged by discarding statements which are not common to each of the pair. In examples, pairs of code fragments are selected using search engine ranks, user input, or frequency. In embodiments, placeholders replace variable names in the merged fragments. An example takes a syntax tree of the code being developed and uses that to specialize snippets.
Abstract:
Described is a technology by which program analysis uses rich invariant templates that may specify an arbitrary Boolean combination of linear inequalities for program verification. Also described is choosing a cut-set that identifies program locations, each of which is associated with an invariant template. The verification generates second-order constraints, converts second-order logic formula based on those constraints into first-order logic formula, then converts the first-order logic formula into a quantifier-free formula, which is then converted into a Boolean satisfiability formula. Off-the-shelf constraint solvers may then be applied to the Boolean satisfiability formula to generate program analysis results. Various templates may be used to convert the second-order logic formula into the first-order logic formula. Further described are interprocedural analysis and the determination of weakest precondition and strongest postcondition with applications to termination analysis, timing bounds analysis, and generation of most-general counterexamples for both termination and safety properties.
Abstract:
A program generation system is described that generates a program based on a plurality of input-output examples. The input-output examples include input items and corresponding output items. The program generation system can include three component modules. A parsing module processes the input items and output items to provide a plurality of input parts and output parts, respectively. A transformation module determines, for each output part, whether the output part can be produced from a corresponding input part using one or more converter modules selected from a collection of candidate converter modules. A formatting module generates formatting instructions that transform selected output parts into a form specified by the output items. These three modules provide a generated program that embodies logic learned from the input-output examples; the generated program can be subsequently used to transform new input items into new respective output items.
Abstract:
Described is a technology by which program analysis uses rich invariant templates that may specify an arbitrary Boolean combination of linear inequalities for program verification. Also described is choosing a cut-set that identifies program locations, each of which is associated with an invariant template. The verification generates second-order constraints, converts second-order logic formula based on those constraints into first-order logic formula, then converts the first-order logic formula into a quantifier-free formula, which is then converted into a Boolean satisfiability formula. Off-the-shelf constraint solvers may then be applied to the Boolean satisfiability formula to generate program analysis results. Various templates may be used to convert the second-order logic formula into the first-order logic formula. Further described are interprocedural analysis and the determination of weakest precondition and strongest postcondition with applications to termination analysis, timing bounds analysis, and generation of most-general counterexamples for both termination and safety properties.
Abstract:
In one embodiment, a computer system performs a method for verifying the validity or invalidity of a software routine by learning appropriate invariants at each program point. A computer system chooses an abstract domain that is sufficiently precise to express the appropriate invariants. The computer system associates an inconsistency measure with any two abstract elements of the abstract domain. The computer system searches for a set of local invariants configured to optimize a total inconsistency measure which includes a sum of local inconsistency measures. The computer system optimizes the total inconsistency measure for all input/output pairs of the software routine. In one embodiment, the optimization of total inconsistency is achieved by the computer system which repeatedly replaces a locally inconsistent invariant with a new invariant, randomly selected among the possible invariants which are locally less inconsistent with the current invariants at the neighboring program points.
Abstract:
A program generation system is described that generates a program based on a plurality of input-output examples. The input-output examples include input items and corresponding output items. The program generation system can include three component modules. A parsing module processes the input items and output items to provide a plurality of input parts and output parts, respectively. A transformation module determines, for each output part, whether the output part can be produced from a corresponding input part using one or more converter modules selected from a collection of candidate converter modules. A formatting module generates formatting instructions that transform selected output parts into a form specified by the output items. These three modules provide a generated program that embodies logic learned from the input-output examples; the generated program can be subsequently used to transform new input items into new respective output items.
Abstract:
There is provided a method and system for generating a program. The method includes detecting a number of steps for performing a task on a computing device and detecting an example relating to each of the steps, wherein the example includes input data and corresponding output data relating to the step. The method also includes, for each example, determining a rule that transforms the input data to the corresponding output data based on cues including textual features within the input data and the corresponding output data. The method further includes generating a program for performing the task based on the rules.
Abstract:
Semantic entity manipulation technique embodiments are presented that generate a probabilistic program capable of manipulating character strings representing semantic entities based on input-output examples. The program can then be used to produce a desired output consistent with the input-output examples from inputs of a type included in the examples. The probabilistic program is generated based on the output of parsing, transform and formatting modules. The parsing module employs a probabilistic approach to parsing the input-output examples. The transform module identifies a weighted set of transforms that are capable of producing the output item from the input items of an input-output example to a likelihood specified by their assigned weight. The formatting module generates formatting instructions that transform selected output parts into a form specified by the output items in the input-output examples.