Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (6): 2144-2153.doi: 10.13229/j.cnki.jdxbgxb20200690

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Fail data reduction algorithm combining configuration checking with local search

Dan-tong OUYANG1,2(),Bi-ge ZHANG1,2,Nai-yu TIAN1,2,Li-ming ZHANG1,2()   

  1. 1.College of Computer Science and Technology,Jilin University,Changchun 130012,China
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering,Ministry of Education,Jilin University,Changchun 130012,China
  • Received:2020-09-11 Online:2021-11-01 Published:2021-11-15
  • Contact: Li-ming ZHANG E-mail:ouyangdantong@163.com;limingzhang@jlu.edu.cn

Abstract:

Aiming at the problem of redundant fail data in the existing N-cover fail data reduction method, a fail data reduction method combining pattern detection and local search is proposed. A local search method combining configuration checking strategy was proposed. By analyzing the logical relationship between fail data and the coverage time of ports, the concepts of fail frequency and defect coverage degree were proposed. Meanwhile, the configuration checking strategy is combined to avoid visiting the same solution space, and the search breadth is increased by reducing redundant iterations. The experimental results for benchmark circuits demonstrate that, compared with existing methods, the proposed method effectively reduces the amount of fail data and improves efficiency while ensuring the quality of diagnosis.

Key words: atificial intelligence, fault diagnosis, circuit test, fail data, local search, configuration checking

CLC Number: 

  • TN407

Fig.1

N-cover target times curves"

Table 1

Example of fail data"

故障数据故障位
z1z2z3z4z5z6z7z8z9z10
tar(zi1311333221
d1XXXXX
d2XXXXXX
d3XX
d4XXXXX
d5XXXXXXX
d6XX
d7XXXX
d8XX
d9XXXXX
d10XXXX
d11XXXX
d12XXXX

Table 2

Process of N-cover"

自变量zi(tar(zi))
z1(1)z2(3)z3(1)z4(1)z5(3)z6(3)z7(3)z8(2)z9(2)z10(1)
COVdiffDcand={}1311333221
Dcand=d50211222111
Dcand=d5d20111111001
Dcand=d5d2d10000010001
Dcand={d5d2d1d90-100-10-10-11
Dcand=d5d2d1d9d40-200-2-1-20-10

Table 3

Process of FDRLS"

迭代过程数值类型自变量zi(tar(zi))
整体过程局部过程z1(1)z2(3)z3(1)z4(1)z5(3)z6(3)z7(3)z8(2)z9(2)z10(1)
第1轮迭代Hzi09/9009/69/59/89/39/20
Dtemp={d5,d1,d4tar(zi)-num(zi0000010110
Dtemp={d5,d1,d4,d20-100-10-1000
Dtemp={d5d4d20011000000
Dtemp={d5d20111111001
Dtemp={d5d2d900110000-11
第2轮迭代Hzi09/9119/69/59/89/39/21
Dtemp={d5d2tar(zi)-num(zi0111111001
Dtemp={d5d2d40011000000
????

Table 4

Experimental results and comparison of ISCAS85"

namegoldnewintFail data/ytes

o_time

/ms

r_time

/ms

DADDRFSRDS
beforeafterRLSCovHSRLSCovHSRLSCovHSRLSCovHS
ave-------79.379.782.820.921.715.055.446.931.757.952.330.9
c1733.62.752214924.19.290.0100.066.730.066.7071.571.514.261.858.133.3
c432171698 3862 187133.323.152.952.970.641.241.25.973.973.917.482.783.422.1
c4992221219 6104 805727.7343.195.595.586.40050.050.037.537.552.943.232.0
c88025212115 5417 0641 026.1422.184.084.084.0012.012.054.550.045.558.957.139.1
c13551922.616.99 0104 805769.3352.388.989.594.730.031.615.846.746.733.354.254.332.3
c19081922176 7302 884338.4126.189.578.910026.35.3057.142.928.662.755.126.0
c26702533.520.585 75739 30537 279.418 153.482.080.084.052.020.012.054.247.927.151.345.822.4
c354035252121 3977 7261 116.0360.760.054.371.411.411.417.163.950.044.567.760.641.9
c531520201992 15150 925112 953.661 555.995.0100.095.05.05.010.044.736.844.745.538.043.9
c62882920164 2242 640456.5267.555.262.175.913.824.127.637.512.525.041.427.816.1

Table 5

Experimental results and comparison of ISCAS89"

namegoldnewintFail data/bytes

o_time

/ms

r_time

/ms

DADDRFSRDS
beforeafterRLSCovHSRLSCovHSRLSCovHSRLSCovHS
ave-------79.277.981.015.617.611.763.560.154.867.065.258.3
s2777.161 05642250.512.285.757.1100.015.714.328.660.060.040.175.872.937.0
s208_13026.81910 7642 5741 134.8180.263.363.396.726.030.030.076.171.745.684.184.245.7
s2986159.348.313 3974 9854 864.81512.379.265.670.518.018.08.262.860.553.568.968.552.4
s3442115.314.314 5525 8215 987.72032.768.166.766.74.84.84.860.054.351.466.160.649.3
s34931312814 9684 9896 866.22 062.390.374.293.59.79.76.566.761.150.070.064.148.3
s38255393518 6426 77911 001.04 574.263.670.970.97.35.55.563.656.863.658.463.062.7
s3869478.577.519 0405 6008 610.12 011.182.494.779.81.17.419.170.669.470.676.674.568.4
s40068524620 3378 05012 597.84 125.267.667.647.18.88.813.260.456.366.767.362.666.9
s420423429.532 70111 48913 791.44 692.270.269.076.210.733.311.964.962.240.566.063.939.7
s420_13040.428.238 3408 94614 840.92 892.994.090.010040.740.023.376.774.444.480.581.040.3
s4444757.14318 6428 05014 471.76 245.691.591.593.630.025.517.056.847.765.956.848.665.1
s5106759.95222 5316 11512 784.73 752.377.671.689.511.810.411.972.968.631.470.771.427.2
s5265151.147.335 59011 86342 205.612 005.592.792.282.37.511.8066.765.565.571.671.670.0
s526n5265.347.633 47113 55834 293.513 883.691.598.194.234.036.57.759.555.763.359.556.592.9
s64116141465 91432 17253 264.523 353.787.587.587.500051.248.870.256.254.379.4
s7137565.951.365 91429 03381 307.534 366.568.481.365.319.517.39.356.054.866.757.758.276.4
s820134858454 47219 986164 508.356 617.562.761.967.20.717.9063.363.355.465.665.651.2
s832171168.7120.455 64820 770155 147.357 119.670.469.659.128.259.124.062.756.354.963.258.260.2
s838242424105 72236 342221 197.272 192.2100100.0100.0004.265.664.132.867.466.962.9
s838_14849.133.9150 66041 309323 591.377 246.470.668.810031.731.322.972.669.446.876.174.252.0
s9535644.939.948 25618 625259 815.588 192.471.371.482.18.912.51.861.460.553.566.168.034.0
s11965550.246.696 76836 352613 460.5218 912.484.785.590.96.51.89.162.458.760.364.361.482.3
s14238889.881.5147 27360 5601364 428.9572 061.592.690.9839.410.24.558.953.359.858.158.870.3
s1488258217.9210.956 17923 517642 157.7226 094.081.782.262.42.73.51.658.155.259.364.863.161.5
s1494246320.5181.154 87322 210619 257.5214 570.873.676.867.556.731.729.359.555.458.365.458.363.2
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