Abstract: Constraints are defined in view of a program implementation. Constraints check program state or variables to maintain data consistency. A constraint component determines a constraint's scope and variables upon which a constraint depends. Program flow is altered so constraints are checked whenever a variable upon which a constraint depends is updated. Optionally, program flow is altered dynamically to re-establish constraints whenever a variable upon which a constraint depends is updated. Re-establishing constraints provides efficiency, since a program flow is altered for a minimum cost based on a present evolving minimum set of active constraint-variable relationships.

Abstract: Techniques and tools for generating finite state machines (“FSMs”) for a software system with asynchronous callbacks are described. For example, method invocations in a model of the software system are partitioned into observable and controlled method invocations. The controlled method invocations are those which can be run from a test harness while the observed method invocations are those which are observed asynchronously as they are invoked in the system. An FSM is created with observation and control nodes such that observable transitions are found from observation nodes and controlled transitions are found from control nodes. If a state of the model contains both controlled and observable invocations, a timeout transition is added to the FSM to give an implementation time to come up with an observed method invocation before continuing to controlled invocations.

Abstract: A system for testing programs using a digital processor and programs in computer memory. A mock behavior generator identifies an interface indicated for mock behavior. The interface is identified as an input parameter of a parameterized unit test. The mock behavior generator creates a symbolic object with stubs to receive calls and mock behavior that returns symbolic values upon receiving a call to the stub. A symbolic executor, symbolically executes the parameterized unit test to obtain path constraints for an implementation under test, and at least one path constraint includes the symbol returned in response to the call to the stub. A constraint solver provides solutions for the paths including concrete values assigned to returned symbols. The mock behavior generator creates mock objects that return the concrete values when the implementation under test is executed.

Abstract: State spaces are traversed to produce test cases, or test coverage. Test coverage is a test suite of sequences. Accepting states are defined. Expected costs are assigned to the test graph states. Strategies are created providing transitions to states with lower expected costs. Linear programs and other approximations are discussed for providing expected costs. Strategies are more likely to provide access to an accepting state, based on expected costs. Strategies are used to append transitions to test segments such that the new test segment ends in an accepting state.

Abstract: Described herein is at least one implementation employing multiple self-describing software artifacts persisted on one or more computer-storage media of a software-based computer. In this implementation, each artifact is representative of at least part of the software components (e.g., load modules, processes, applications, and operating system components) of the computing system and each artifact is described by at least one associated “manifest,” which include metadata declarative descriptions of the associated artifact.

Abstract: A test domain configuration module generates graphical user interfaces for identifying information about desired tests such as data types and domain configurations, and collects information used by other modules to generate tests. The identified information may include, for example, an abstract syntax, a static semantic, max counts on instances of data types, or costs of field accesses or data types for max path costs or max expression costs. A test input generator, generates test input for the identified and configured data types. In one case, the generated test inputs are generated as tree data structures. A predicate determines whether a generated test input follows semantic conditions. A test input evaluator counts instances of data types in, sums paths through, or sums total costs of, the generated test inputs. A test acceptance module saves test inputs acceptable to the predicate and the test input evaluator.

Abstract: Exploration algorithms are relevant to the industrial practice of generating test cases from an abstract state machine whose runs define the predicted behavior of the software system under test. Here, a new exploration algorithm allows multiple state groupings to simultaneously guide the search for states that are interesting or relevant for testing. In some cases, the algorithm allows exploration to be optimized from exponential to linear complexity. An extended example is included that illustrates the use of the algorithm.

Abstract: A model composition environment can allow for description of fill or partial symbolic system behavior, as well as the combination of models of specific features into compound models. Compositional operators can include intersection, concatenation, substitution, alternating refinement, as well as a set of regular expression-like operators. Models called “action machines” can represent object-oriented, reactive programs, and an action machine may be composed with another action machine using a compositional operator. This can allow for testing of particular scenarios or behaviors.

Abstract: A finite domain approximation for symbolic terms of a symbolic state is derived, given some finite domains for basic terms of the symbolic state. A method is executed recursively for symbolic sub-terms of a symbolic term, providing a domain over-approximation that can then be provided to a solver for determining a more accurate domain. The method can be applied to a wide array of system terms, including, for example, object states, arrays, and runtime types.

Abstract: The technology contributes the inference of formal specifications automatically, which can increase the acceptance of specifications. The technology introduces the symbolic execution of a modifier method to explore its behavior and then summarizing the results of the exploration using observer methods. This often results in concise, understandable specifications, which are a prerequisite for human analysis. Optionally, a generated specification is deemed sound and or complete. The specifications are presented as traditional pre-/post-condition specifications or parameterized unit tests. The former often serve as inputs to a program verification system, whereas the latter often provide inputs for tools that generate test cases.

Abstract: Symbolic execution identifies possible execution paths of a computer program or method, each having certain constraints over the input values. The symbolic execution also records updates of memory locations, e.g. updates of the fields of symbolic objects in the heap of an object oriented program, involving a description of the previous heap, the updated symbolic object, a field identification, and a newly assigned symbolic value. The symbolic execution can also record calls to summarized methods, involving a description of previous calls, an identification of the summarized methods, and its symbolic arguments. The behavior of summarized methods can be expressed by axioms. Axioms describe the relationship between summarized methods under certain conditions. Axioms can be generated from parameterized unit tests. A parameterized unit test is a method with parameters which executes a sequence of calls to methods of an implementation under test; it asserts constraints over the inputs and outputs of the calls.

Abstract: A computer system provides a test program and one or more unit tests, such as a traditional unit test and or a parameterized unit test. The system also includes a constraint solver, a theorem prover, an implementation under test, a symbolic executor, a generalizor, and generated test cases. The generalizor receives a traditional unit tests as input, and modifies the traditional unit test into a parameterized unit test. The modification includes replacing plural concrete values in the traditional unit test with symbols, and exporting the symbols into a signature of the parameterized unit test. A symbolic executor identifies constraints while symbolically executing the created parameterized unit test of the implementation under test. A constraint solver and or theorem prover generates a set of test cases by solving for values that satisfy the series of constraints. The test program executes the automatically generated test cases.

Abstract: Separation of parameterized unit tests from specific test cases supports many benefits including automated test case generation. Symbolic execution assigns symbolic input variables to parameters of a parameterized unit test. Path constraints of an implementation under test (IUT) are identified during symbolic execution. A constraint solver automatically generates test cases by determining the test inputs that satisfy one of more paths, each described by constraints, through the IUT. Parameterized unit tests are used to populate behavioral summaries. Behavioral summaries are used later in future symbolic executions to emulate summarized methods. An intensional heap is provided to represent state changes performed by summarized methods. The extensional heap is used to explicitly update memory locations, e.g. object fields or array elements.

Abstract: A system for testing programs using a digital processor and programs in computer memory. A mock behavior generator identifies an interface indicated for mock behavior. The interface is identified as an input parameter of a parameterized unit test. The mock behavior generator creates a symbolic object with stubs to receive calls and mock behavior that returns symbolic values upon receiving a call to the stub. A symbolic executor, symbolically executes the parameterized unit test to obtain path constraints for an implementation under test, and at least one path constraint includes the symbol returned in response to the call to the stub. A constraint solver provides solutions for the paths including concrete values assigned to returned symbols. The mock behavior generator creates mock objects that return the concrete values when the implementation under test is executed.

Abstract: Techniques and tools for generating test cases for methods or programs with input preconditions are described. For example, after finding feasible control flow paths for a tested method along with each path's associated input conditions, a new program is created which tests these conditions along with the precondition. By analyzing this new program's control flow graph, a class of test cases is found while avoiding inefficiencies created by doing complete searches of paths through the combined control flow graph of the precondition and the method. Additional efficiencies are introduced by partitioning a control flow graph for the precondition into branched and straight sections.

Abstract: Techniques and tools for generating finite state machines (“FSMs”) for a software system with asynchronous callbacks are described. For example, method invocations in a model of the software system are partitioned into observable and controlled method invocations. The controlled method invocations are those which can be run from a test harness while the observed method invocations are those which are observed asynchronously as they are invoked in the system. An FSM is created with observation and control nodes such that observable transitions are found from observation nodes and controlled transitions are found from control nodes. If a state of the model contains both controlled and observable invocations, a timeout transition is added to the FSM to give an implementation time to come up with an observed method invocation before continuing to controlled invocations.

Abstract: Techniques and tools for testing multi-threaded or distributed software systems are described. For example, a multi-threaded system is instrumented and executed to produce logs of events that are performed by each of its agents. The agent logs contain a totally ordered series of events per agent, as well as information about accesses to resources shared between the agents. With this information, a partial ordering of the events performed by all the agents is described for the execution. The agent logs are then multiplexed into one or more serialized event orderings, which can then be compared to a specification of the system in a conformance testing engine.

Abstract: State spaces are traversed to produce test cases, or test coverage. Test coverage is a test suite of sequences. Accepting states are defined. Expected costs are assigned to the test graph states. Strategies are created providing transitions to states with lower expected costs. Linear programs and other approximations are discussed for providing expected costs. Strategies are more likely to provide access to an accepting state, based on expected costs. Strategies are used to append transitions to test segments such that the new test segment ends in an accepting state.

Abstract: A method and apparatus to generate a test suite from an abstract state machine (ASM) involves generating a finite state machine (FSM) for the ASM, and generating the test suite from the FSM. An initial state of the ASM is selected. A first encoding having a plurality of bits is generated to represent the initial state. Each bit of the first encoding represents a result of an evaluation of a non-trivial guard condition of the ASM. The initial state is associated with the first encoding in the data structure for the FSM. At least one other states of the ASM are identified that result from applying at least one actions of the ASM to the initial state.

Abstract: Exploration algorithms are relevant to the industrial practice of generating test cases from an abstract state machine whose runs define the predicted behavior of the software system under test. Here, a new exploration algorithm allows multiple state groupings to simultaneously guide the search for states that are interesting or relevant for testing. In some cases, the algorithm allows exploration to be optimized from exponential to linear complexity. An extended example is included that illustrates the use of the algorithm.