1. A method comprising:
receiving, by a computing device comprising at least one processor, input data;
determining, by the computing device and based at least in part on the input data, a plurality of visualizations, each of the plurality of visualizations representing at least a portion of the input data;
determining, by the computing device, a respective score for each of the plurality of visualizations, the respective score for each visualization from the plurality of visualizations being based at least in part on a comparison of the corresponding visualization to one or more visual patterns;
determining, by the computing device and based at least in part on the respective score for each of the plurality of visualizations, an ordering of the plurality of visualizations; and
outputting, by the computing device and for display, at least one visualization from the plurality of visualizations in accordance with the ordering.
2. The method of claim 1,
wherein each visualization from the plurality of visualizations comprises a set of data defining the corresponding visualization, and
wherein each visual pattern from the one or more visual patterns comprises a set of data defining the corresponding visual pattern.
3. The method of claim 1, wherein a visual pattern from the one or more visual patterns comprises a user-defined visual pattern.
4. The method of claim 3, wherein determining the ordering of the plurality of visualizations comprises:
determining a first ordering of a first group of visualizations from the plurality of visualizations, wherein each visualization from the first group of visualizations has a respective similarity to the user-defined visual pattern that exceeds a defined similarity threshold; and
determining a second ordering of a second group of visualizations from the plurality of visualizations, wherein each visualization from the second group of visualizations does not have a respective similarity to the user-defined visual pattern that exceeds the defined similarity threshold.
5. The method of claim 1, wherein determining the respective score for each of the plurality of visualizations comprises determining the respective score for each of the plurality of visualizations based on at least one focus area, the at least one focus area defining one or more data measures.
6. The method of claim 5, wherein the at least one focus area comprises a user-defined focus area.
7. The method of claim 1, wherein determining the respective score for each of the plurality of visualizations comprises:
sending, by the computing device, one or more requests to compare the corresponding visualization to each visual pattern from the one or more visual patterns;
responsive to sending the one or more requests, receiving, by the computing device, one or more sub-scores representing a similarity between the corresponding visualization and each visual pattern from the one or more visual patterns; and
determining the respective score for the corresponding visualization based at least in part on the one or more sub-scores.
8. A computing system comprising:
at least one processor; and
at least one module operable by the at least one processor to:
receive input data;
determine, based at least in part on the input data, a plurality of visualizations, each of the plurality of visualizations representing at least a portion of the input data;
determine a respective score for each of the plurality of visualizations, the respective score for each visualization from the plurality of visualizations being based at least in part on a comparison of the corresponding visualization to one or more visual patterns;
determine, based at least in part on the respective score for each of the plurality of visualizations, an ordering of the plurality of visualizations; and
output, for display, at least one visualization from the plurality of visualizations in accordance with the ordering.
9. The computing system of claim 8,
wherein each visualization from the plurality of visualizations comprises a set of data defining the corresponding visualization, and
wherein each visual pattern from the one or more visual patterns comprises a set of data defining the corresponding visual pattern.
10. The computing system of claim 8, wherein a visual pattern from the one or more visual patterns comprises a user-defined visual pattern.
11. The computing system of claim 10, wherein the at least one module operable to determine the ordering of the plurality of visualizations is operable, by the at least one processor, to:
determine a first ordering of a first group of visualizations from the plurality of visualizations, wherein each visualization from the first group of visualizations has a respective similarity to the user-defined visual pattern that exceeds a defined similarity threshold; and
determine a second ordering of a second group of visualizations from the plurality of visualizations, wherein each visualization from the second group of visualizations does not have a respective similarity to the user-defined visual pattern that exceeds the defined similarity threshold.
12. The computing system of claim 8, wherein the at least one module operable to determine the respective score for each of the plurality of visualizations is operable, by the at least one processor, to determine the respective score for each of the plurality of visualizations based on at least one focus area, the at least one focus area defining one or more data measures.
13. The computing system of claim 12, wherein the at least one focus area comprises a user-defined focus area.
14. The computing system of claim 8, wherein the at least one module operable to determine the respective score for each of the plurality of visualizations is operable, by the at least one processor, to:
send one or more requests to compare the corresponding visualization to each visual pattern from the one or more visual patterns;
responsive to sending the one or more requests, receive one or more sub-scores representing a similarity between the corresponding visualization and each visual pattern from the one or more visual patterns; and
determine the respective score for the corresponding visualization based at least in part on the one or more sub-scores.
15. A computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to:
receive input data;
determine, based at least in part on the input data, a plurality of visualizations, each of the plurality of visualizations representing at least a portion of the input data;
determine a respective score for each of the plurality of visualizations, the respective score for each visualization from the plurality of visualizations being based at least in part on a comparison of the corresponding visualization to one or more visual patterns;
determine, based at least in part on the respective score for each of the plurality of visualizations, an ordering of the plurality of visualizations; and
output, for display, at least one visualization from the plurality of visualizations in accordance with the ordering.
16. The computer program product of claim 15,
wherein each visualization from the plurality of visualizations comprises a set of data defining the corresponding visualization, and
wherein each visual pattern from the one or more visual patterns comprises a set of data defining the corresponding visual pattern.
17. The computer program product of claim 15, wherein a visual pattern from the one or more visual patterns comprises a user-defined visual pattern.
18. The computer program product of claim 17, wherein the program code executable by the at least one processor to determine the ordering of the plurality of visualizations comprises program code executable by the at least one processor to:
determine a first ordering of a first group of visualizations from the plurality of visualizations, wherein each visualization from the first group of visualizations has a respective similarity to the user-defined visual pattern that exceeds a defined similarity threshold; and
determine a second ordering of a second group of visualizations from the plurality of visualizations, wherein each visualization from the second group of visualizations does not have a respective similarity to the user-defined visual pattern that exceeds the defined similarity threshold.
19. The computer program product of claim 15, wherein the program code executable by the at least one processor to determine the respective score for each of the plurality of visualizations comprises program code executable by the at least one processor to determine the respective score for each of the plurality of visualizations based on at least one focus area, the at least one focus area defining one or more data measures.
20. The computer program product of claim 19, wherein the at least one focus area comprises a user-defined focus area.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method of adjusting a strobe timing used for a function test wherein a test pattern is input to a semiconductor device, and an output pattern output from the semiconductor device is compared with an expected result pattern at the strobe timing to determine a passfail result of the semiconductor device, comprising the steps of:
a first step of setting a default value of the strobe timing;
a second step of comparing the output pattern output from the semiconductor device in response to the test pattern with the expected result pattern at the default value of the strobe timing;
a third step of setting an initial value of the strobe timing different from the default value set in the second step to a minimum value of the strobe timing in a variation range thereof when the output pattern is not matched to the expected result pattern;
a fourth step of comparing the output pattern in response to the test pattern with the expected result pattern at the strobe timing set in the third step, storing the strobe timing when the output pattern is matched to the expected result pattern, and determining whether the strobe timing exceeds a maximum value in the variation range;
a fifth step of increasing the initial value of the strobe timing set in the third step by a specific value when the strobe timing does not exceed the maximum value, and repeating the fourth step until the strobe timing exceeds the maximum value;
a sixth step of setting the default value of the strobe timing for a next test pattern based on the strobe timing stored in the fourth step, and for repeating a process from the second step; and
a seventh step of calculating a new strobe timing according to the minimum value and the maximum value when there is the stored strobe timing so that the new strobe timing is used as the default value for the next test pattern.
2. A function testing apparatus for a semiconductor device, comprising:
test pattern inputting means for inputting a test pattern to the semiconductor device;
expected result pattern outputting means for outputting an expected result pattern corresponding to the test pattern; and
passfail determining means for performing a first process of setting a default value of the strobe timing;
a second process of comparing the output pattern output from the semiconductor device in response to the test pattern with the expected result pattern at the default value of the strobe timing;
a third process of setting an initial value of the strobe timing different from the default value set in the second process to a minimum value of the strobe timing in a variation range thereof when the output pattern is not matched to the expected result pattern;
a fourth process of comparing the output pattern in response to the test pattern with the expected result pattern at the strobe timing set in the third process, storing the strobe timing when the output pattern is matched to the expected result pattern, and determining whether the strobe timing exceeds a maximum value in the variation range;
a fifth process of increasing the initial value of the strobe timing set in the third process by a specific value when the strobe timing does not exceed the maximum value, and repeating the fourth process until the strobe timing exceeds the maximum value;
a sixth process of setting the default value of the strobe timing for a next test pattern based on the strobe timing stored in the fourth step, and for repeating a process from the second process; and
a seventh process of calculating a new strobe timing according to the minimum value and the maximum value when there is the stored strobe timing so that the new strobe timing is used as the default value for the next test pattern.