p268-mendis - A CM'81 November 9-11 1981 Reviewed Paper...

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ACM '81, November 9-11, 1981 Reviewed Paper QUANTIFYING SOFTWARE QUALITY Kenneth S. Hendis Raytheon Company Submarine Signal Div. P.O. Box 360 Portsmouth, R.I. 02871 ABSTRACT In Software Quality Assurance the quality of the program must be defined in practical and measurable terms. This is accomplished by first standardizing the error reporting technique, identifying the types and frequency of occurrence of software errors experienced, and finally, pre- dicting the error arrival rate of the software programs. This paper details a practical approach to quantifying software quality by investigating empirical software error data. Software failure trend analysis and residual error predictions are the by-products of this technique. Successfully applied to software engineering projects, the author reports on the findings and details an implementation plan. Basis for Quantification Many of you may be aware that the Depart- ment of Defense (DOD) is now using incentive award fee type contracts for developing quality software. A number of factors have motivated DOD to do this. For the developer this has meant using existing software quality metrics to evaluate the software quality being developed. The paper outlines a metrix and shows how it can be used to access the quality of the program being de- veloped. It must be pointed out that the metrix was found to be practical and can be easily applied with very little guesswork. The factors used are the following: Error by Category Grouping of errors by category will allow for easy focusing (i.e., What causes an error and why?). Several major categories are dis- cussed in this paper with each category lending itself to a unique cause for error introduction. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. @1981 AOM 0-89791-049-4/81/1100-0268 $00.75 Error Density Error density is a measure of the number of errors to the size of the program. This metrix permits quantification. The metrix allows for easy comparison to other programs. Error by Type Allows for the broad grouping into their type and frequency of occurrence. Quantification along these lines also allows for identifying the type of errors most frequently observed. Error by Severity Classification by severity is a measure of how severe the errors are when discovered and it also is a measure that can be used to determine priorities. That is, where do we concentrate on the quality assurance effort? Such a classifica- tion scheme was found to be most beneficial when investigating error reports from a remote facility. Error Arrival Rate This is a measure of the Mean Time to Failure of a software error (i.e., When can we expect the next error to occur?). The following Quality Factor t!atrices were
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This note was uploaded on 04/12/2010 for the course CIS 635 taught by Professor Mcintyre during the Spring '10 term at Cleary University.

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p268-mendis - A CM'81 November 9-11 1981 Reviewed Paper...

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