Abstract: A method for reducing the number of neurons in a trained deep neural network (DNN) includes classifying layer types in a plurality of hidden layers; evaluating the accuracy of the DNN using a validation set of data; and generating a layer specific ranking of neurons, wherein the generating includes: analyzing, using the validation set of data for one or more of the plurality of hidden layers, the activation function for each neuron in the analyzed layers to determine an activation score for each neuron; and ranking, on a layer type basis, each neuron in the analyzed layers based on the neuron's activation score to generate a layer specific ranking of neurons. The method further includes removing a number of lower ranked neurons from the DNN that does not result in the DNN after the removal of selected lower ranked neurons to fall outside of an accuracy threshold limit.

Abstract: A crowdsourced virtual sensor generator is provided which could be generated by a vehicle or provided to the vehicle as, for example, a service. The crowdsourced virtual sensor generator may include, but is not limited to, a communication system configured to receive contributing vehicle sensor data from one or more contributing vehicles, a location of the one or more contributing vehicles and a target vehicle, and a processor, the processor configured to filter the received contributing vehicle sensor data based upon the location of the one or more contributing vehicles and the location of the target vehicle, aggregate the filtered contributing vehicle sensor data into at least one of a data-specific dataset and an application-specific data set, and generate a virtual sensor for the target vehicle, the virtual sensor processing the filtered and aggregated contributing vehicle sensor data to generate output data relative to the location of the target vehicle.

Abstract: A crowdsourced virtual sensor generator is provided which could be generated by a vehicle or provided to the vehicle as, for example, a service. The crowdsourced virtual sensor generator may include, but is not limited to, a communication system configured to receive contributing vehicle sensor data from one or more contributing vehicles, a location of the one or more contributing vehicles and a target vehicle, and a processor, the processor configured to filter the received contributing vehicle sensor data based upon the location of the one or more contributing vehicles and the location of the target vehicle, aggregate the filtered contributing vehicle sensor data into at least one of a data-specific dataset and an application-specific data set, and generate a virtual sensor for the target vehicle, the virtual sensor processing the filtered and aggregated contributing vehicle sensor data to generate output data relative to the location of the target vehicle.

Abstract: A system, for use in reverse-engineering initial input initial code to a high-level equivalent model. The system includes a hardware-based processing unit and a non-transitory computer-readable storage component including a function-extraction module that, when executed by the hardware-based processing unit (i) generates, based on the input initial code and an input variable list, a list of output and state transition functions per task; and (ii) generates, based on an input task table, a scheduler-automaton structure. The storage component also includes a function-modeling module that, when executed, generates, using the scheduler automaton and the list of output and state transition functions per task, the high-level equivalent model of the input initial code. Various aspects of the present technology includes the non-transitory computer-readable storage devices configured to perform the operations described, and processes including the operations performed by these systems, storage devices, and algorithms.

Abstract: A system for use in analyzing software code using a symbolic-execution technique. The system includes a hardware-based processing unit, and a non-transitory computer-readable storage component including a (i) concurrency-analysis module, (ii) a lightweight-analysis module, and (iii) a heavyweight-analysis module. The concurrency-analysis module, when executed by the hardware-based processing unit receives initial code and generates a potential interference matrix using the initial code. The lightweight-analysis module, when executed by the hardware-based processing unit, generates a final interface matrix using the potential interference matrix. The heavyweight-analysis module, when executed by the hardware-based processing unit, generates one or more test cases using the potential interference matrix.

Abstract: A method of generating ontology models from requirement documents and software and performing consistency checks among requirement documents and software code utilizing ontology models. Terms in the plurality of requirement documents obtained from a database are identified. A processor assigns a part-of-speech tag to each term. The part-of-speech tag indicates a grammatical use of each term in the requirement documents. The processor classifies each term based on the part-of-speech tags. The classification identifies whether the each term is a part, symptom, action, event, or failure mode to constitute an ontology. The processor constructs an ontology-based consistency engine as a function of the ontologies. A consistency check is performed by applying the ontology-based consistency engine between ontologies extracted from two context documents. Inconsistent terms are identified between the context documents. At least one of the context documents having inconsistent terms is corrected.

Abstract: A method of establishing traceability for embedded software systems. A design code database is provided for an embedded software system. A test suite database including a plurality of test cases is structured for testing design code of the embedded software system. The structuring of the test cases provides a correspondence from a respective test case to a respective portion of the design code. A processor receives a design code modification to the embedded software. An associated test case is identified for testing the modified design code being based on traceability data. The associated test case is revised to accommodate the modified design code. The modified test cases are integrated into the test suite. A traceability database establishes a one-to-one correspondence between the modified design coder and the modified test case is updated.

Abstract: A method of automatic identifying linking relationships of requirements in a plurality of requirement documents. Terms in the plurality of requirement documents are identified. A part-of-speech tag is assigned to each term. Each identified term is selected as a focal term. Co-occurring terms within a predetermined distance of the selected focal term are determined. A linking relationship probability is calculated for each co-occurring term associated with the selected focal term. The selected focal terms and associated co-occurring terms between the plurality of requirement documents are compared. A degree of linking relationship is identified between two requirements as a function of a comparison between selected focal terms and the associated co-occurring terms between the plurality of requirement documents. An analysis report identifying the degree of linking relationships between two respective requirements is output.

Abstract: A method of establishing traceability for embedded software systems. A design code database is provided for an embedded software system. A test suite database including a plurality of test cases is structured for testing design code of the embedded software system. The structuring of the test cases provides a correspondence from a respective test case to a respective portion of the design code. A processor receives a design code modification to the embedded software. An associated test case is identified for testing the modified design code being based on traceability data. The associated test case is revised to accommodate the modified design code. The modified test cases are integrated into the test suite. A traceability database establishes a one-to-one correspondence between the modified design coder and the modified test case is updated.

Abstract: A method of automatic identifying linking relationships of requirements in a plurality of requirement documents. Terms in the plurality of requirement documents are identified. A part-of-speech tag is assigned to each term. Each identified term is selected as a focal term. Co-occurring terms within a predetermined distance of the selected focal term are determined. A linking relationship probability is calculated for each co-occurring term associated with the selected focal term. The selected focal terms and associated co-occurring terms between the plurality of requirement documents are compared. A degree of linking relationship is identified between two requirements as a function of a comparison between selected focal terms and the associated co-occurring terms between the plurality of requirement documents. An analysis report identifying the degree of linking relationships between two respective requirements is output.

Abstract: Systems and methods for generating formal software requirements using an informal requirements document having informal requirements and annotations associated with the informal requirements. The systems and methods extract syntax from the annotations and generate artifacts as a function of the syntax.

Abstract: A method allows for testing software under test (SUT) with respect to a partial design model (PDM) having a boundary which differs from a boundary of the SUT. The method includes recording input information including the SUT, the PDM, and coverage criteria defining a required number of the test cases. Variables in the SUT are identified that correspond to boundary signals for the PDM. Test cases are extracted meeting the coverage criteria. The method may include generating additional test cases at the PDM level and mapping the additional cases with corresponding constraint functions to the boundary of the SUT using a forward/backward propagation and/or heuristics guided technique. A system for testing the SUT includes a host machine and memory. The host machine executes process instructions from memory to identify variables in the SUT that correspond to boundary signals for the PDM, and extracts test cases meeting the coverage criteria.

Abstract: A computer-implemented method for evaluating a machine-executable software code specification includes using the computer to generate a system dependence graph corresponding to the software code specification. The system dependence graph includes elements including nodes and edges. The computer evaluates the system dependence graph including selecting a variable modified in the software code specification, providing a control operation node of the system dependence graph corresponding to a control statement in the software code specification with a preferred calibration state, traversing to selected elements of the system dependence graph wherein the selected elements are associated with the selected variable and the preferred calibration state of the control operation node, evaluating only the selected elements of the system dependence graph, and identifying ones of the selected elements whereat a state of the selected variable is modified.

Abstract: A computer-implemented method for evaluating a machine-executable software code specification includes using the computer to generate a system dependence graph corresponding to the software code specification. The system dependence graph includes elements including nodes and edges, wherein the computer evaluates the system dependence graph. The evaluation of the system dependence graph includes selecting a variable modified in the software code specification, traversing to selected elements of the system dependence graph, the selected elements associated with the selected variable, evaluating only the selected elements of the system dependence graph, and identifying ones of the selected elements whereat a state of the selected variable is modified.

Abstract: Systems and methods for generating formal software requirements using an informal requirements document having informal requirements and annotations associated with the informal requirements. The systems and methods extract syntax from the annotations and generate artifacts as a function of the syntax.

Abstract: A method and tools for providing precise timing analysis scalable to industrial case studies with large numbers of tasks and messages are provided, including the capability to model and analyze task and message response times; ECU usage; bus usage; end-to-end latency of task/message chains; and timing synchronization problems in task/message graphs. System tasks and messages are modeled in a formalism known as calendar automaton. Models are written in a modeling language such as Promela and instrumented with code specific to the analysis specification. Models and instrumentation are automatically generated from the system description and analysis specification. The system model is subjected to exhaustive state space exploration by a compatible model checker, such as SPIN. During exploration, the instrumented code produces results for different timing analyses.

Abstract: A system and method for automatic formal verification of an executable model includes an assertion monitor configured to verify a system against an assertion in a specification. The assertion monitor includes a parser configured to generate a propositional formula representing the assertion in the specification using Boolean propositions, a filter configured to generate a run of the system using truth assignments for the propositional symbols, and a trace verifier configured to verify the assertion using the run of the system using truth assignments for the propositional symbols and the propositional formula.

Abstract: A method allows for testing software under test (SUT) with respect to a partial design model (PDM) having a boundary which differs from a boundary of the SUT. The method includes recording input information including the SUT, the PDM, and coverage criteria defining a required number of the test cases. Variables in the SUT are identified that correspond to boundary signals for the PDM. Test cases are extracted meeting the coverage criteria. The method may include generating additional test cases at the PDM level and mapping the additional cases with corresponding constraint functions to the boundary of the SUT using a forward/backward propagation and/or heuristics guided technique. A system for testing the SUT includes a host machine and memory. The host machine executes process instructions from memory to identify variables in the SUT that correspond to boundary signals for the PDM, and extracts test cases meeting the coverage criteria.

Abstract: A method for providing writing requirements for a structured transition system employing state machines. The requirements employ a plurality of structuring mechanisms, namely, sub-state based structuring, abstraction based structuring and partial behavior structuring that uses event sequences. The sub-state based structuring has to do with the hierarchical requirements of the state machines, abstraction based structuring provides an abstraction of the state machines that reduces the number of states, and partial behavior structuring looks at certain states to predict how those states will affect other states.

Abstract: A system and method for providing control timing for a vehicle system at the design level. The method includes defining component timing specifications in a parametric form at a system level and at a sub-system level; mathematically representing the timing specifications in a system model; providing a constraint extraction algorithm that extracts timing constraints from the mathematical representations; using the constraint extraction algorithm to generate a plurality of linear equations that define the constraints; solving for real time constraint ranges from parameters in the linear equations; and selecting values from the real time constraint ranges to be used in the mathematical representations. In non-limiting embodiments, the constraint extraction algorithm can be a boundary discovery algorithm or a proof-tree.