Software defects can frequently be traced to poorly-specified requirements. Many software teams manage their requirements using tools such as checklists and databases, which lack a formal semantic mapping to system behavior. Such a mapping can be especially helpful for safety-critical systems. Another limitation of many requirements analysis methods is that much of the analysis must still be done manually. We propose techniques that automate portions of the requirements analysis process, as well as clarify the syntax and semantics of requirements models using a variety of methods, including machine learning tools and our own tool, VeriCCM. The machine learning tools used help us identify potential model elements and verify their correctness. VeriCCM, a formalized extension of the causal component model (CCM), uses formal methods to ensure that requirements are well-formed, as well as providing the beginnings of a full formal semantics. We also explore the use of statecharts to identify potential abnormal behaviors from a given set of requirements. At each stage, we perform empirical studies to evaluate the effectiveness of our proposed approaches.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1404542 |
| Date | 12 1900 |
| Creators | Gaither, Danielle |
| Contributors | Bryant, Barrett R., Do, Hyunsook, Bryce, Renee, Gray, Jeff |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | viii, 105 pages, Text |
| Rights | Public, Gaither, Danielle, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
Page generated in 0.002 seconds