Software projects requiring satisfaction assessment are often large scale systems containing hundreds of requirements and design elements. These projects may exist within a high assurance domain where human lives and millions of dollars are at stake. Satisfaction assessment can help identify unsatisfied requirements early in the software development lifecycle, when issues can be corrected with less impact and lower cost. Manual satisfaction assessment is expensive both in terms of human effort and project cost. Automated satisfaction assessment assists requirements analysts during the satisfaction assessment process to more quickly determine satisfied requirements and to reduce the satisfaction assessment search space. This paper introduces two new automated satisfaction assessment techniques and empirically demonstrates their effectiveness, as well as validates two previously existing automated satisfaction assessment techniques. Validation shows that automatically generated satisfaction assessments have high accuracy, thus reducing the workload of the analyst in the satisfaction assessment process.
|Number of pages||38|
|Journal||Empirical Software Engineering|
|State||Published - Feb 2013|
Bibliographical noteFunding Information:
Acknowledgments This work is funded in part by the National Science Foundation under NSF grant CCF-0811140. This work was partially sponsored by NASA under grant NNG05GQ58G. We thank David Pruett and the other evaluators for their help. We thank Hakim Sultanov and Bill Kidwell. Thanks to Stephanie Ferguson, Marcus Fisher, Ken McGill, Tim Menzies, Lisa Montgomery, and everyone at the NASA IV&V facility. Thanks also to fellow graduate students Jody Larsen, Senthil Sundaram, Liming Zhao, and Sravanthi Vadlamudi. We thank statistics professor Dr. Arnold Stromberg.
- Methods for SQA and V&V
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