Improving Performance Requirements Specifications from Field Failure Reports

Performance requirements can be specified qualitatively or quantitatively. Quantitative specifications are usually preferred because they are measurable and testable. Basili and Musa advocate that quantitative specification for the attributes of a final software product will lead to better software quality [3]. For
performance requirements, Nixon suggests that both qualitative and quantitative specifications are needed, but different aspects are emphasized at different stages of development [9]. Our procedure may generate both qualitative and quantitative requirements, depending on the information available from the field
failures and existing requirements.

If sufficient information is available from the field failures and existing requirements, quantitative requirements are generated. Otherwise, our procedure produces qualitative requirements, indicating the unknown factors that need to be addressed from requirements elicitation. A performance meta-model describes how to present a performance concept. Cortellessa [5] provides
an overview of three performance meta-models, including UML-SPT, Core Scenario Model [11], and Software Performance Engineering meta-model [12].
The performance information extraction in our procedure is based on UML-SPT. Another performance meta-model may be used, although the procedure described in this paper may need to change accordingly.

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Customer-reported field failures provide valuable
information for the requirements of the next release.
Without a systematic approach, the requirements of the
next release may not address the field failures, and the
same problems may reoccur. In this paper, we propose
a procedure for improving performance requirements
based on a retrospective analysis of field failures
reports and original requirements. In our procedure,
the performance information from field failure
reports and original requirements specifications is
extracted based on a performance meta-model. The
extracted information is used to construct new and
revised performance requirements, following the Performance
Refinement and Evolution Model. We applied
this procedure on the requirements specifications
and field failure reports for a commercial distributed
software system. The results from our case study
demonstrate that the resulting requirements integrate
the information found in the field failure reports.

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