Topic: Statistical Quality Assurance !!!!!

Message

   Statistical Quality Assurance
Statistical
quality assurance reflects a growing trend throughout industry to
become more quantitative about quality. For software, statistical
quality assurance implies the following steps
•   Information about software defects is collected and categorized
•   An attempt is made to trace each defect to its underlying cause
•   Using Pareto principle (80% of the defects can be traced to 20% of all possible causes), isolate the 20% (the "vital few")
•   Once the vital few causes have been identified, move to correct the problems that have caused the defects.

This
relatively simple concept represents an important step toward the
creation of an adaptive software engineering process in which changes
are made to improve those elements of the process that introduce
errors. To illustrate the process, assume that a software development
organization collects information on defects for a period of one year.
Some errors are uncovered as software is being developed. Other defects
are encountered after the software has been released to its end user.
Although hundreds of errors are uncovered all can be tracked to one of the following causes.

   Incomplete or erroneous specification (IES)

   Misinterpretation of customer communication (MCC)

   Intentional deviation from specification (IDS)

   Violation of programming standards ( VPS )

   Error in data representation (EDR)

       Inconsistent module interface (IMI)

   Error in design logic (EDL)

   Incomplete or erroneous testing (IET)

   Inaccurate or incomplete documentation (IID)

   Error in programming language translation of design (PLT)

   Ambiguous or inconsistent  human-computer interface (HCI)

   Miscellaneous (MIS)

To
apply statistical SQA table 1 is built. Once the vital few causes are
determined, the software development organization can begin corrective
action.
After analysis, design, coding, testing, and release, the following data are gathered.

Ws, Wm, Wt = weighting factors for serious, moderate and trivial errors where recommended values are Ws = 10, Wm = 3, Wt = 1.
The
weighting factors for each phase should become larger as development
progresses. This rewards an organization that finds errors early.

At each step in the software engineering process, a phase index, PIi, is computed
PIi  = Ws (Si/Ei)+Wm (Mi/Ei)+Wt (Ti/Ei)

The
error index EI ids computed by calculating the cumulative effect or
each PIi, weighting errors encountered later in the software
engineering process more heavily than those encountered earlier.  
EI    =? (i x PIi)/PS
   = (PIi+2PI2   +3PI3 +iPIi)/PS   
The
error index can be used in conjunction with information collected in
table to develop an overall indication of improvement in software
quality.

Author	Message
tanushree Senior Member Joined: 04Apr2007 Online Status: Offline Posts: 2160	Topic: Statistical Quality Assurance !!!!! Posted: 15Oct2007 at 3:01am
	Statistical Quality Assurance Statistical quality assurance reflects a growing trend throughout industry to become more quantitative about quality. For software, statistical quality assurance implies the following steps • Information about software defects is collected and categorized • An attempt is made to trace each defect to its underlying cause • Using Pareto principle (80% of the defects can be traced to 20% of all possible causes), isolate the 20% (the "vital few") • Once the vital few causes have been identified, move to correct the problems that have caused the defects. This relatively simple concept represents an important step toward the creation of an adaptive software engineering process in which changes are made to improve those elements of the process that introduce errors. To illustrate the process, assume that a software development organization collects information on defects for a period of one year. Some errors are uncovered as software is being developed. Other defects are encountered after the software has been released to its end user. Although hundreds of errors are uncovered all can be tracked to one of the following causes. Incomplete or erroneous specification (IES) Misinterpretation of customer communication (MCC) Intentional deviation from specification (IDS) Violation of programming standards ( VPS ) Error in data representation (EDR) Inconsistent module interface (IMI) Error in design logic (EDL) Incomplete or erroneous testing (IET) Inaccurate or incomplete documentation (IID) Error in programming language translation of design (PLT) Ambiguous or inconsistent human-computer interface (HCI) Miscellaneous (MIS) To apply statistical SQA table 1 is built. Once the vital few causes are determined, the software development organization can begin corrective action. After analysis, design, coding, testing, and release, the following data are gathered. Ws, Wm, Wt = weighting factors for serious, moderate and trivial errors where recommended values are Ws = 10, Wm = 3, Wt = 1. The weighting factors for each phase should become larger as development progresses. This rewards an organization that finds errors early. At each step in the software engineering process, a phase index, PIi, is computed PIi = Ws (Si/Ei)+Wm (Mi/Ei)+Wt (Ti/Ei) The error index EI ids computed by calculating the cumulative effect or each PIi, weighting errors encountered later in the software engineering process more heavily than those encountered earlier. EI =? (i x PIi)/PS = (PIi+2PI2 +3PI3 +iPIi)/PS The error index can be used in conjunction with information collected in table to develop an overall indication of improvement in software quality. Post Resume: Click here to Upload your Resume & Apply for Jobs

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