WO2007005586A1

WO2007005586A1 - System and method for using model analysis to generate directed test vectors

Info

Publication number: WO2007005586A1
Application number: PCT/US2006/025539
Authority: WO
Inventors: Thomas Gudette
Original assignee: The Mathworks, Inc.
Priority date: 2005-06-30
Filing date: 2006-06-30
Publication date: 2007-01-11
Also published as: US7970594B2; US20070016394A1; EP1899876B1; US8090565B2; US20080109194A1; DE602006017698D1; ATE485564T1; EP1899876A1

Abstract

A mechanism for exploiting the data gathered about a system model during the system design phase to aid the identification of errors subsequently detected in a deployed system based on the system model is disclosed. The present invention utilizes the coverage analysis from the design phase that is originally created to determine whether the system model as designed meets the specified system requirements. Included in the coverage analysis report is the analysis of which sets of test vectors utilized in simulating the system model excited individual components and sections of the system model. The present invention uses the information associated with the test vectors to select appropriate test vectors to use to perform directed testing of the deployed system so as to confirm a suspected fault.

Description

SYSTEM AND METHOD FOR USING MODEL ANALYSIS TO GENERATE DIRECTED TEST VECTORS

Related Application

This application claims the benefit of U.S. Patent Application Serial No.

11/173977, filed June 30, 2005, the contents of which are hereby incorporated by reference.

Field of the Invention

The illustrative embodiment of the present invention relates generally to model analysis and more particularly to the use of data obtained during model analysis to subsequently generate directed test vectors to identify errors in a deployed system.

Background

The overall life-cycle of engineered systems typically starts with a requirements capture for a system model being designed and continues to the maintenance of the deployed real-life system based on the model. Frequently, the maintenance of the deployed system costs as much as the initial design of the system with the result that good maintenance technologies have taken on an increased importance.

Much of the present day repair aspect of maintenance schemes relies on the use of measurement trees. Measurement trees are a hierarchical tree-like arrangement of acceptable operating parameters for different components in a system. Starting from a root diagnosis as to which component may be faulty in a system, a selection of measurements can be made on the system that is behaving erratically. Each of the measurements rules out a number of branches on the tree which relate to different subcomponents of the suspected faulty component. As a path along the tree is traversed by making additional measurements, a detected failure can ultimately be pinpointed to a replaceable component. Conventionally problems with deployed systems based on a computer designed model have been diagnosed using two types of mechanisms. Diagnosis of problems has been made through the use of heuristics or the previous experiences of the user tasked with identifying the cause of the system fault. Some analysis is performed through a combination of the mechanisms. Unfortunately these approaches fail to take advantage of the information gathered during the design phase of the system when the system model undergoes testing to make sure it satisfies the set of requirements for the system design.

Brief Summary

The illustrative embodiment of the present invention exploits the data gathered about a system model during the system design phase to aid the identification of errors subsequently detected in a deployed system based on the system model. The present invention utilizes the coverage analysis from the design phase that is originally created to determine whether the system model as designed meets the specified system requirements. Included in the coverage analysis report is the analysis of which sets of test vectors utilized in simulating the system model excited individual components and sections of the system model. The present invention uses the information associated with the test vectors to select appropriate test vectors to use to perform directed testing of the deployed system so as to confirm a suspected fault.

In one embodiment, a method of identifying errors in a deployed system in a technical computing environment is performed and includes the step of testing a system model being designed with multiple sets of test vectors. The testing identifies a degree of model capture associated with each set of the multiple sets of test vectors. The multiple sets of test vectors are saved following testing. The method further includes the step of identifying an error in the functioning of a deployed system based on the system model using the test vectors from the previous analysis.

In another embodiment in an electronic device with a technical computing environment, a system for detecting errors in deployed systems based on system models includes a system model for a real system being designed in the technical computing environment. The system also includes a test system for testing the system model. The test system simulates the execution of the system model with multiple sets of test vectors. The multiple sets of test vectors and their associated data are saved following testing. The system additionally includes a coverage analysis report of the results of a simulation of the system model with the test system. The coverage analysis report is subsequently used to select at least one of the multiple sets of test vectors to perform directed testing to identify an error reported in a deployed system based on the system model.

In one embodiment, a method of identifying errors in a deployed system in a technical computing environment is performed and includes the step of providing a model coverage analysis report for a model identifying a degree of model capture associated with each of multiple sets of vectors. The method additionally includes the step of receiving a notification of an error in the functioning of a deployed system based on the system model. The method further includes the step of identifying an error in the functioning of a deployed system based on the system model using the model coverage analysis report and at least one of the multiple sets of vectors.

Brief Description of the Drawings

Figure 1 depicts an environment suitable for practicing the illustrative embodiment of the present invention;

Figure 2 depicts an alternate distributed environment suitable for practicing the illustrative embodiment of the present invention; Figure 3 depicts a block diagram view of a test system used to generate sets of test vectors for a system being tested;

Figure 4 depicts a screenshot of a signal range analysis report; Figure 5A depicts a screenshot of a block diagram including a lookup table block; Figure 5B depicts a screenshot of a lookup table analysis report;

Figure 6 depicts a screenshot of a decision analysis report; Figure 7 depicts a screenshot of a condition analysis report; and Figure 8 depicts a screenshot of a modified condition decision coverage analysis report;

Figure 9 is a flow chart of the sequence of steps illustrated by the illustrative embodiment of the present invention to perform model verification; and Figure 10 is a flow chart of the sequence of steps followed by the illustrative embodiment of the present invention to use the results of a previously performed model verification to generate directed test vectors to identify reported faults.

Detailed Description

The present invention takes advantage of the increased data gathered about systems during their design in technical computing environments in order to perform subsequent fault identification and validate repairs to the system. The increasing use of computer modeling in the different aspects of the design process has led to the growing availability of high fidelity models that capture different characteristics of the system that was designed. Additionally the use of automatic code generation provides a very close link between model and implementation. The use of the data gathered from the test of the system model thus enables subsequent operations performed on deployed systems to be conducted in a more rapid and efficient manner than was possible with conventional methods used to diagnose faults.

Figure 1 depicts an environment suitable for practicing the illustrative embodiment of the present invention. An electronic device 2 hosts a technical computing environment 4. The technical computing environment 4 such as MATLAB or SBVIULINK both from The MathWorks, Inc. of Natick, Massachusetts, includes a system model 6 of a system being designed. The technical computing environment 4 may also include a test system 8 able to execute the system model 6. Alternatively, the test system may be located outside the technical computing environment. Additionally, the technical computing environment 4 may include a set of requirements for the model 10. The set of requirements for model 10 includes a set of benchmarks that the completed model, when executed, should be to satisfy. Also included in the technical computing environment 4 is a plurality of sets of test vectors 12. The plurality of sets of test vectors are different test parameters used to test the system while being designed in order to determine whether or not the design system satisfies the sets of requirements for the model 10. A display device 20 in communication with the electronic device 2 may include a block diagram view 22 of the system model being designed.

Block diagrams are used to model real-world systems. Historically, engineers and scientists have utilized time-based block diagram models in numerous scientific areas to study, design, debug, and refine dynamic systems. Dynamic systems, which are characterized by the fact that their behaviors change over time, are representative of many real-world systems. A dynamic system (either natural or man-made) is a system whose response at any given time is a function of its input stimuli, its current state, and the current time. A block diagram model of a dynamic system is represented schematically as a collection of blocks interconnected by lines that represent signals. A signal represents the input and output of a dynamic system. Each block represents an elemental dynamic system. A line emanating at one block and terminating at another signifies that the output of the first block is an input to the second block. Each distinct input or output on a block is referred to as a port. Signals correspond to the time- varying quantities represented by each line connection and are assumed to have values at each time instant at which the connecting blocks are enabled. The source block of a signal writes to the signal at a given time instant when its system equations are solved. The destination blocks of this signal read from the signal when their system equations are being solved. The user 24 who is accessing the electronic device 2 may view the results of a simulation of the system model in the block diagram view 22 generated on the display 20. Upon successful completion of the design of the system model the system model frequently will be the basis of a real-life deployed system 30.

The electronic device 2 may be any of a number of different types of computing devices equipped with a processor and able to execute computer instructions. For example, the electronic device 2 may be a server, client, workstation, laptop, mainframe, PDA, or other type of device equipped with a processor. The display 20, rather than displaying a block diagram view 22, may display some other type of user interface showing the results of the modeling of the system that is being performed to determine the degree to which the design system satisfies the set of requirements for the model 10. The plurality of sets of test vectors 12 may correspond to the various requirements for the model 10 and are used as inputs to the design system during the test of the system in order to determine how much of the model design is excited by the use of the different test vectors.

Figure 2 depicts an alternate distributed architecture environment suitable for practicing the illustrative embodiment of the present invention. A server 80 hosts a technical computing environment 82. The technical computing environment 82 includes a system model 84, test system 86, set of requirements for the model 88 and a plurality of sets of test vectors 90. A client 40 communicates over a network 70 with the server 80. The client 40 may also include a technical computing environment 42 or may be a thin client receiving the results of the operations performed in the technical computing environment 82 on the server 80. Also communicating with the client 40 is the display 50 which may show a block diagram view 52 of the model undergoing a test. A user 44 accessing the client 40 monitors the results of the model test via the display 50. Upon successful design of the system a deployed real life system 60 may be generated. The network 70 may be an internet, intranet, local area network (LAN), wide area network (WAN), the Internet or some other type of network. Those skilled in the art will recognize that the various components that are depicted as being included within the technical computing environment 82 may be located remotely from the technical computing environment 82 in a location that is accessible over the network 70.

The coverage analysis performed during system design is performed to check whether the functionality embodied by the current state of the designed model satisfies the requirements set forth for the system. To perform the coverage analysis, a number of test vectors that may or may not correspond to requirement scenarios are used as input to the designed system and an analysis shows whether and how much of the design is excited/activated by the use of those particular test vectors. This information can be exploited to do directed testing when a fault has occurred as the functionality that is suspected to be affected by the fault can be optimally tested.

In addition to modeling the actual system, engineers have the ability to model the test system used to test the actual part. By using this model of the test system users can develop test vectors to exercise parts of the model. The present invention thus may include two types of models: one model is the system under test called the DUT (DUT) and one model for the test system itself. The user can then exercise the DUT by producing different test vectors. Each test vector will test different requirements and scenarios of the DUT.

Figure 3 illustrates the ability of the present invention to use a test system to utilize a set of test vectors to simulate the execution of a system/ DUT. A block diagram view 92 shows a tester system 94 and a system being designed 104. The tester system 94 includes outputs 95, 96, 97, 98, 99 and 100. The system being designed 104 includes inputs 105, 106, 107, 108, 109 and 110. A set of test vectors is used in tester system 94 to produce output values transmitted through the outputs 95, 96, 97, 98, 99 and 100 which are in turn received via the inputs 105, 106, 107, 108, 109 and 110 of the system being designed. The system being designed 104 is executed using the received values. The system being designed 104 also includes an output 111 which is used to transmit the values resulting from the execution of the model using the set of test vectors back to the input 101 of the tester system. The execution path of the DUT is recorded when executing each set of test vectors. The illustrative embodiment of the present invention records which components are executed in the DUT and for what portion of the total execution time.

The technical computing environment 4, 42, or 82 generates a model coverage analysis report for each of these sets of test vectors created in the combined model (the test system and the DUT) based on the recorded detail. Sample model coverage analysis reports are discussed below. These coverage analysis reports describe what parts of the DUT were tested for each test vector and the testing results. The results in the coverage analysis report description may be returned as a percentage of time active. The results inform the user what parts of the model are being tested with each set of test vectors. Following the receipt of an error in a deployed system (an indication that the system is not working as expected and/or designed) based on the system model, the illustrative embodiment of the present invention uses the coverage analysis report of the DUT to determine which sets of test vectors are necessary to do a directed test for the suspected failed model component. Errors may be faults of different types such as abrupt and incipient faults. The following table is an example of the coverage analysis report generated for each set of test vectors. The left column is a list of components found in the DUT and each column after that is the coverage number for a given test vector input to the test system.

Using these numbers the user can now understand what test to run to find the problem in a more rapid manner. If the device comes in for maintenance and the suspected problem is BlockC then the user will know to run test vector 3 because the coverage for BlockC is higher for this test vector (75%). In comparison, for the above test vectors, running test vector 1 results in blockC being activated only 25% of the time. Similarly, running test vector 2 results in blockC being activated only 50% of the time. If the execution of test vector 3 fails to diagnose the problem, the user may run Test vector 2 and Test vector 1 until the problem is found. The coverage analysis report thus allows the test programs to direct the test sequence based on which test vectors are more likely to exercise the area of concern.

The coverage analysis reports may include coverage for many different types of areas including lookup tables, whether particular states were entered into in a STATEFLOW diagram, ranges of signal and types of transitions for a switch. For example, if signal range analysis is selected as a coverage selection, then the coverage analysis report will include the maximum and minimum signal values at each block in the model measured during simulation. Figure 4 depicts a screenshot 120 of a signal range analysis report. The signal range analysis report includes a hierarchical listing of blocks 122 with child blocks being listed under parent blocks. Each block has a corresponding minimum value 124 and maximum value 126. Thus, for the depicted switch block 127 the minimum value 128 is listed as 0.180421 during simulation and the maximum value 129 is listed as 12. The coverage analysis reports may also include lookup table coverage. Lookup table coverage analysis examines blocks that output the result of looking up one or more inputs in a table of inputs and outputs, interpolating between or extrapolating from table entries as necessary. Lookup table coverage records the frequency that table lookups use each interpolation interval. A test case achieves full coverage if it executes each interpolation and extrapolation interval at least once. For each lookup table block in the model, the coverage report may display a colored map of the lookup table indicating where each interpolation was performed. For example, Figure 5B depicts a screenshot 138 of an exemplary lookup table 139 represented by a lookup table block 133 in Figure 5A. Figure 5A is a screenshot 130 of a block diagram showing Sine Wave 1 block 131, Sine Wave 2 block 132, the lookup table block 133 and a scope block 134. The lookup table 139 is 10 x10 elements filled with random values that are accessed with x and y indices generated from the two Sine Wave blocks 131 and 132. The Sine Wave 2 block 132 is out of phase with the Sine Wave 1 block 131 by pi/2 radians. The execution of the Sine Wave blocks 131 and 132 generates x and y numbers for the edge of a circle which become apparent when examining the Lookup table coverage report in Figure 5B. An individual cell 140 in the lookup table report may be selected by the user and the selection results in the generation of a display of the index location of the cell and the exact number of execution counts generated for the cell during testing. Different levels of shading 141 may be used to represent ranges 142 of execution counts.

The coverage analysis report may also analyze Cyclomatic complexity, decision coverage, condition coverage, and modified condition/decision coverage (MC/DC).

Cyclomatic complexity analysis measures the structural complexity of a model.

It approximates the McCabe complexity measure for code generated from the model. In general, the McCabe complexity measure is slightly higher because of error checks that the model coverage analysis does not consider. Model coverage uses the following formula to compute the cyclomatic complexity of an object (block, chart, state, etc.)

N

* where N is the number of decision points that the object represents and O_n is the number of outcomes for the nth decision point. The tool adds 1 to the complexity number computed by this formula for atomic subsystems and Stateflow charts.

Decision coverage analysis examines items that represent decision points in a model, such as a Switch block or Stateflow states. For each item, decision coverage determines the percentage of the total number of simulation paths through the item that the simulation actually traversed. A screenshot 145 of a decision analysis report is depicted in Figure 6.

Condition coverage analysis examines blocks that output the logical combination of their inputs (for example, the Logic block), and Stateflow transitions. A test case achieves full coverage if it causes each input to each instance of a logic block in the model and each condition on a transition to be true at least once during the simulation and false at least once during the simulation. Condition coverage analysis reports for each block in the model whether the test case fully covered the block. A screenshot 147 of a condition coverage analysis report is depicted in Figure 7.

Modified condition/decision coverage (MC/DC) analysis examines blocks that output the logical combination of their inputs (for example, the Logic block), and Stateflow transitions to determine the extent to which the test case tests the independence of logical block inputs and transition conditions. A test case achieves full coverage for a block if, for every input, there is a pair of simulation times when changing that input alone causes a change in the block's output. A test case achieves full coverage for a transition if, for each condition on the transition, there is at least one time when a change in the condition triggers the transition. A screenshot 149 of a modified condition/decision analysis report is depicted in Figure 8.

It will be appreciated that multiple types of reports may be combined in order to display data to a user. For example, a model coverage report for a SIMULINK model may include signal range analysis, Lookup table analysis, decision analysis, condition analysis, MC/DC analysis and state-transition analysis (for an embedded STATEFLOW block). The data may be cross-linked so that the user may easily navigate from one set ofdata to the next.

In addition to providing information about the DUT, the coverage analysis report also highlights areas in the Test system connected to the DUT that are used. The coverage analysis report further indicates the resources in the test system that are not used in the testing of the DUT.

Figure 9 depicts the sequence of steps followed by the illustrative embodiment of the present invention to initially validate a model using a plurality of sets of test vectors. The sequence begins with the provision of a set of requirements for the system model (step 150). For example, the set of requirements may indicate that a certain component be active at least seventy five per cent of the time in order to ensure efficient operation of the system. Similarly, the set of requirements may indicate that a particular component be active no more than ten percent of the time in order to prevent component overuse. Those skilled in the art will recognize that the particulars of the set of requirements will vary depending upon the purpose and functionality of the system being designed. The user then designs a system model in an attempt to meet the set of requirements (step 152). The system model is then tested using the plurality of sets of test vectors (step 154) with the results of each set of test vectors including the degree of model capture being saved (step 156). For example, referring back to Figure 3, each set of test vectors may provide different output values 95, 96, 97, 98, 99 and 100 for the test system and correspondingly different input values 105, 106, 107, 108, 109 and 110 for the DUT 104 which result in a different execution path in the DUT and a different output value. Alternatively, the same components may be executed within the DUT 104 with different sets of test vectors but the different sets of test vectors may result in different components being executed for differing percentages of time. Returning to Figure 9, upon determining a degree of model capture that satisfies the set of requirements for the system model, a deployed system is built based on the model (step 158). The results of the tests of the system model are saved in a report indicating which model components are excited by the use of a particular set of test vectors. The model may also indicate the percentage of the overall model that is excited by a particular set of test vectors and other similar data. Figure 10 depicts the sequence of steps followed by the illustrative embodiment of the present invention to use the results of the test validation that are performed during the initial system design in a subsequent effort to identify a failed component in a deployed system. The sequence begins with the user receiving a notification of an error in a deployed system (step 160). The results of the previous testing that was performed on the system during the model design are then retrieved (step 162). The error notification is then compared to the testing report to identify a possible cause of the failure (step 164). For example, the error notification may suggest that a particular component in the deployed system has failed and the testing results report may indicate which sets of test vectors ordinarily would cause that particular component to be excited during a simulation. Once the comparison has been made, directed testing of the system model using the saved test vectors from the model design may be used so that the test vectors are targeted to the particular suspected failed component (step 166). It will be appreciated by those skilled in the art that the previous testing results using the test vectors may be used in combination with other reliability data. Thus directed testing could be performed towards a particular component that past reliability data indicates is most likely to fail even if the coverage report indicates another component is slightly more likely to be the cause of the failure. In such a scenario, the coverage report generated from the plurality of test vectors is a factor in the determining what directed testing to perform but not the sole factor.

The information in the coverage analysis report from the design phase may be retrieved in a number of different ways, hi one implementation, the results contained in the coverage analysis report are presented to a user who makes a manual selection of which set of test vectors to use to verify a suspected fault in a deployed system. Li another implementation, the process is automated with the set of test vectors with the greatest degree of model capture for the suspected fault being automatically selected and provided to the test system. In a different implementation, the set of test vectors with the greatest likelihood of exciting the suspected failed component is automatically selected and presented to a user for manual confirmation. Those skilled in the art will recognize that other implementations combining different automatic and manual selections are also possible within the scope of the present invention. In one aspect of the illustrative embodiment, the coverage analysis report is linked to the block diagram view being shown to the user. A selection of a component in the coverage analysis report causes the associated component in the block diagram view to become highlighted or otherwise visibly identifiable so as to provide a visual cue to the user regarding the particular portion of the model in question. The block diagram view may be used to show the execution path of the model during a simulation performed using a selected set of test vectors. Similarly, the present invention may be configured so that a selection in the block diagram view results in a highlighting/visual identification of the information regarding the component in the coverage analysis report.

The illustrative embodiment of the present invention may also be used to analyze a computer designed model of a deployed system following the initial design phase. Although the examples contained herein have described the use of a model coverage analysis report that is generated during the design phase to determine compliance with a set of requirements, it will be appreciated that the model coverage analysis report may be generated following the initial design phase and then serve as the basis for fault identification in the deployed system using the mechanisms described above. As long as the model coverage analysis report is generated before the deployed system generates a fault, it provides a baseline which may be used for future fault analysis.

Although the descriptions contained herein have made reference to a block diagram view being used to present the coverage analysis and other information to the user, it will be appreciated by those skilled in the art that other types of user interfaces may be utilized without departing from the scope of the present invention.

The present invention may be provided as one or more computer-readable programs embodied on or in one or more mediums. The mediums may be a floppy disk, a hard disk, a compact disc, a digital versatile disc, a flash memory card, a PROM, a RAM, a ROM, or a magnetic tape. In general, the computer-readable programs may be implemented in any programming language. Some examples of languages that can be used include C, C++, C#, or JAVA. The software programs may be stored on or in one or more mediums as object code. Since certain changes may be made without departing from the scope of the present invention, it is intended that all matter contained in the above description or shown in the accompanying drawings be interpreted as illustrative and not in a literal sense. Practitioners of the art will realize that the sequence of steps and architectures depicted in the figures may be altered without departing from the scope of the present invention and that the illustrations contained herein are singular examples of a multitude of possible depictions of the present invention.

Claims

We Claim:

1. A method of identifying errors in a deployed system, comprising; testing in a technical computing environment a system model being designed with a plurality of sets of test vectors, the testing identifying a degree of model capture associated with each set of test vectors in the plurality of test vectors, the plurality of sets of test vectors saved following testing; and identifying an error in the functioning of a deployed system based on the system model using the test vectors previously used during the testing of the system design.

2. The method of claim 1, further comprising: presenting a testing results report to a user showing the degree of model capture associated with each set of test vectors.

3. The method of claim 2 wherein the testing results report further identifies whether an individual component in the system model is executed by a particular test vector.

4. The method of claim 2 wherein the testing results report indicates the percentage of time a particular component in the system model was active during the execution of the model with a given test vector.

5. The method of claim 1 wherein the step of identifying further comprises; performing directed testing based on the identity of error reported, the degree of model capture, and the testing results report identifying the individual components executed by a given test vector.

6. The method of claim 1, further comprising: providing a block diagram view of the system model being tested; and tracing the execution path of the model during execution of a particular test vector.

7. The method of claim 6, further comprising: providing a report linked to a block diagram view of the model.

8. The method of claim 1 wherein more than one of the plurality of sets of test vectors is executed in order to identify the reported error.

9. The method of claim 1, further comprising: presenting, for each set of test vectors, a signal range analysis report to the user indicating the maximum and minimum signal values for each component in the model measured during simulation of the model.

10. The method of claim 1, further comprising: presenting, for each set of test vectors, at least one of the group of a lookup table analysis report, a state transition analysis report, a decision analysis report , a condition analysis report, and a modified condition/decision coverage analysis report to a user.

11. The method of claim 1 wherein the testing is performed to determine compliance with a set of requirements for the system model.

12. The method of claim 1 wherein the error is identified using reliability data in addition to the plurality of test vectors.

13. hi a computing device with a technical computing environment, a system for detecting errors in deployed systems based on system models, the system comprising: a system model for a system being designed in the technical computing environment; a test system for testing the system model, the test system simulating the execution of the system model with a plurality of sets of test vectors, at least some of the plurality of sets of test vectors being saved following testing; and a coverage analysis report of the results of a simulation of the system model with the test system, the coverage analysis report subsequently used to select at least one of the plurality of sets of test vectors to perform directed testing to identify an error reported in a deployed system based on the system model.

14. The system of claim 13 wherein the technical computing environment generates a block diagram view of the system model.

15. The system of claim 13 wherein the coverage analysis report indicates at least one component in the system model executed by the use of each of the plurality of test vectors.

16. The system of claim 13 wherein the coverage analysis report indicates one of the percentage of the entire system model executed by the use of each of the plurality of test vectors and the percentage of time a particular component in the model was executed by each of the plurality of test vectors.

17. Amethod of identifying errors in a deployed system, comprising; providing a model coverage analysis report for a model identifying a degree of model capture associated with each of a plurality of sets of vectors generated during the design of the model in a technical computing environment; receiving a notification of an error in the functioning of a deployed system, the deployed system based on the system model; and identifying an error in the functioning of a deployed system based on the system model using the model coverage analysis report and at least one of the plurality of sets of vectors.

18. The medium of claim 17, the instructions further comprising: presenting a testing results report to a user showing the degree of model capture associated with each set of test vectors.

19. The medium of claim 18 wherein the testing results report further identifies whether an individual component in the system model is executed by a particular test vector.

20. The medium of claim 17 wherein the testing results report indicates the percentage of time a particular component in the system model was active during the execution of the model with a given test vector.

21. The medium of claim 17 wherein the instructions for the step of identifying further comprises; performing directed testing based on the identity of error reported, the degree of model capture, and the testing results report identifying the individual components executed by a given test vector.

22. The medium of claim 17, the instructions further comprising: providing a block diagram view of the system model being tested; and tracing the execution path of the model during execution of a particular test vector.

23. The medium of claim 22, the instructions further comprising: providing a report linked to a block diagram view of the model.

24. The medium of claim 17 wherein more than one of the plurality of sets of test vectors is executed in order to identify the reported error.

25. In a technical computing environment, a computer-readable medium holding computer-executable instructions for a method of identifying errors in a deployed system, the instructions comprising the steps of: providing a set of requirements during the design of a system model; testing the system model being designed with a plurality of sets of test vectors to determine compliance with the set of requirements, the testing identifying the degree of model capture associated with each set of test vectors in the plurality of test vectors, the plurality of sets of test vectors saved following testing; receiving a notification of an error in the functioning of a deployed system, the deployed system based on the system model; and identifying the error using the test vectors previously used to determine compliance with the set of requirements.

26. The method of claim 25, further comprising; performing directed testing based on an identity of the error, and the model coverage analysis report, the model coverage analysis report identifying at least one individual component executed by at least one of the plurality of sets of vectors.