Independent verification of specification models for large software systems at the early phases of development lifecycle

One of the major challenges facing the software industry, in general and IV&V (Independent Verification and Validation) analysts in particular, is to find ways for analyzing dynamic behavior of requirement specifications of large software systems early in the development lifecycle. Such analysis can significantly improve the performance and reliability of the developed systems. This dissertation addresses the problem of developing an IV&V framework for extracting semantics of dynamic behavior from requirement specifications based on: (1) SART (Structured Analysis with Realtime) models, and (2) UML (Unified Modeling Language) models.;For SART, the framework presented here shows a direct mapping from SART specification models to CPN (Colored Petrinets) models. The semantics of the SART hierarchy at the individual levels are preserved in the mapping. This makes it easy for the analyst to perform the analysis and trace back to the corresponding SART model. CPN was selected because it supports rigorous dynamic analysis. A large scale case study based on a component of NASA EOS system was performed for a proof of the concept.;For UML specifications, an approach based on metamodels is presented. A special type of metamodel, called dynamic metamodel (DMM), is introduced. This approach holds several advantages over the direct mapping of UML to CPN. The mapping rules for generating DMM are not CPN specific, hence they would not change if a language other than CPN is used. Also it makes it more flexible to develop DMM because other types of models can be added to the existing UML models. A simple example of a pacemaker is used to illustrate the concepts of DMM

Risk Assessment and Collaborative Information Awareness for Plan Execution

Joint organizational planning and plan execution in risk-prone environment, has seen renewed research interest given its potential for agility and cost reduction. The participants are often asked to quickly plan and execute tasks in partially known or hostile environments. This requires advanced decision support systems for situational response whereby state-of-the-art technologies can be used to handle issues such as plan risk assessment, appropriate information exchange, asset localization and adaptive planning with risk mitigation. Toward this end, this thesis contributes innovative approaches to address these issues, focusing on logistic support over risk-prone transport network as many organizational plans have key logistic components. Plan risk assessment involves property evaluation for vehicle risk exposure, cost bounds and contingency options assessment. Appropriate information exchange involves participant specific shared information awareness under unreliable communication. Asset localization mandates efficient sensor network management. Adaptive planning with risk mitigation entails limited risk exposure replanning, factoring potential vehicle and cargo loss. In this pursuit, this thesis first investigates risk assessment for asset movement and contingency valuation using probabilistic model-checking and decision trees, followed by elaborating a gossip based protocol for hierarchy-aware shared information awareness, also assessed via probabilistic model-checking. Then, the thesis proposes an evolutionary learning heuristic for efficiently managing sensor networks constrained in terms of sensor range, capacity and energy use. Finally, the thesis presents a learning based heuristic for cost effective adaptive logistic planning with risk mitigation. Instructive case studies are also provided for each contribution along with benchmark results evaluating the performance of the proposed heuristic techniques