Siebel Correlation with custom code in LoadRunner

“Siebel _Star_Array” is a function generated by LoadRunner for correlating Siebel field values separated by Token "*". A sample format of the server response is given below and these field values are required to pass to the subsequent requests.

"1*N9*1-18506217*GEOFFTH4*Open10*Unassigned3*OEM12*Hardware-OEM14*Administrative1*01*010*Symptom X18*MSE-Perf7*GEOFFTH1*112*Geoff Thomas1*10*6* Normal19*10/16/2002 03:40:127*Sev - 47*1-13NY5"

Last Character/s (Highlighted in Red) in each of the fields denotes the length of the next field values. Because of which left and right boundaries will be dynamic and difficult to correlate.

The below "siebel _Star_Array” function provided with LoadRunner has some limitations and does not work at all times. It is it is tough to debug the errors without the source code

web_reg_save_param("WCSParam96",
"LB/IC=`ValueArray`",
"RB/IC=`",
"Ord=10",
"Search=Body",
"RelFrameId=1",
"AutoCorrelationFunction=flCorrelationCallbackParseStarArray",
LAST);

The sample code I had created can be used to parse the response and use it for correlation when compared to the Loadrunner Automactic Correlation

vuser_init()
{

char str[1024];
char separators[] = "*";
char *token;
char arrValues[50][20];
char arrFinalValues[50][20];
int i;
int j;
int length_old;
int length_new;
char newlength[2];
char actualValue[20];

/****************** Sample Text format****************************** */

strcpy(str, "1*N9*1-18506217*GEOFFTH4*Open10*Unassigned3*OEM12*Hardware-OEM14*Administrative1*01*010*Symptom X18*MSE-Perf7*GEOFFTH1*112*Geoff Thomas1*10*3 - Normal19*10/16/2002 03:40:127*Sev - 47*1-13NY5");

lr_output_message("%s",str);

/***** The following code will be used to split the text into strings based on the token *******/
token = (char *)strtok(str, separators); /* Get the first token */
if(!token) {
lr_output_message("No tokens found in string!");
return( -1 );
}

i=0;
while( token != NULL ) { /* While valid tokens are returned */
strcpy(arrValues[i],token);
lr_output_message("Initial array values is %s",arrValues[i]);
token = (char *)strtok(NULL, separators); /* Get the next token */
i++;
}

/*******************************************************************/
/*************** To remove Trail charecters ***********************/

for (j=0; j less than i; j++) //use lessthan sysmbol
{
if (j==0) {
strcpy(arrFinalValues[j],arrValues[j]);
length_old=strlen(arrValues[j]);
}
else{
length_new=strlen(arrValues[j]);
strncpy(arrFinalValues[j], arrValues[j], length_old);
if(length_new>length_old+1){
sprintf(newlength,"%c%c",arrValues[j][length_old],arrValues[j][length_old+1]);
length_old=atoi(newlength);
}
else{
sprintf(newlength,"%c",arrValues[j][length_old]);
length_old=atoi(newlength);
}//End of Else
}//End of Out Else
}//End of For

/* Final Data in the Array are */
for (j=0; j less than i; j++)//less than symbol
{
lr_output_message("Values after removing tail charecters %s",arrFinalValues[j]);
}
return 0;
}

How to determine two performance runs are statistically different?

Performance tests are good examples of Normal Distribution. A normal distribution of data means that most of the examples in a set of data are close to the "average," while relatively few examples tend to one extreme or the other.

According to statisticians, Two tests are considered statistically different if it is unlikely to have occurred by chance. we can be 99% sure that averages from two runs are really different only when the other average is more than 2.57 standard deviations away.

The same principle can also be applied to performance tests to determine results obtained are statiscally different.

For example, If the Average response time for transaction A is 1 sec during the first run with the standard deviation of 0.2 and for the second run average response time for the same transaction A is 1.2 sec. The difference between the two transaction response time is 1.2 – 1.0 =0.2, which is 1 standard deviations away (Standard deviation observed during the first run is 0.2) on the average. If the difference is 2.57 standard deviations away(we are 99% sure) then the results are statistically different.
Conversely we can even calculate the statistical limits using the formula below
If R1 is average response time and SD is the standard deviation for a performance run then the average response time for the second run should not exceed R1 + (SD* 2.33) or should not be less than R1 - (SD* 2.33). For the above example response time for the second run should not exceed 1 + (0.2* 2.33) =1.466 on the positive side of the average and 1 –(0.2*2.33) = 0.534 on the negative side of the average

Welcome to Performance testing blog

I work in the performance testing domain for some years and have experience in various technologies and tools. I have always been passionate about learning new concepts and share my experiences with others. I decided to start this blog to share my experiences with the rest of the performance testing community.

So please keep reading and share your valuable inputs.

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