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System complexities continue to expand in the unfolding technology revolution, as does the array of user needs, interactions and quality concerns. Human-centered aspects are increasingly missed during product development, leading to poorly designed products and rework. Modern requirement methods are evolving in response to these trends and to improve the user experience of new products. This thesis proposes a method for the early incorporation of User Requirements (URs) into the engineering development process. It attempts to synergistically harness the benefits of human-centered design, requirements engineering, and modeling. The framework leverages work from ISO standards, along with a focus on pragmatic application and best practices in model-based systems engineering. Its ultimate intention is to provide an effective means to capture and meet user needs during the development of an interactive system. Research on the proposed method was performed within a major automotive company. Towards understanding the impressions of URs in practice, a survey was distributed to 59 engineers at the organization on their impressions of the proposed URs approach. In parallel, URs were incorporated into three advanced technology projects at during a mid-cycle design iteration, followed by a study of the subsequent changes made to the system requirements and test plans in response to the inclusion of URs. The survey results revealed the UR proposal was well received, with an overall 96% reporting favorable expectations for a system development project. The case studies supported these results by revealing a direct impact to design functionalities, quality aspects, and validation activities. System requirements increased by 15%, and validation attributes by 38%.

The objective of this research is to explore and develop new methodologies and techniques to improve UGV mission reliability. This dissertation focuses on two research issues that are critical in the following UGV deployment phases: (1) prior to field deployment to remove design deficiencies; and (2) during field usage to prevent mission failures. Four specific research topics are accomplished. The first topic focuses on simulation-based acceptance testing. A general framework is proposed to integrate dynamic and static simulations. Statistical hypothesis testing is used to compare static and dynamic simulations to determine when a simple static simulation can be used to replace the complex dynamic simulation. Results show that the static simulation can be used when a failure mechanism is not significantly affected by the dynamic characteristics of the vehicle. The remaining research topics aim at prevention of operational failures due to unexpected energy depletion. A model-based Bayesian prediction framework integrated with a dynamic vehicle model is proposed in the second research topic, which improves traditional approaches for estimation and prediction. The Bayesian framework combines mission prior knowledge with real-time measurements for adaptive prediction of end-of-mission energy requirement. Experimental studies were conducted, which validated and demonstrated the advantages of the framework on roads with different surface types and grades. The third research topic, entitled real-time energy reliable path planning, builds upon the above mentioned prediction framework to identify the most energy reliable path in a stochastic network with unknown and correlated arc lengths. Since traditional sequential optimization techniques cannot be directly applied to this problem, a heuristic approach based on two stage exploration/exploitation is proposed to identify the most reliable path. The framework, which minimizes the cost of exploration, outperforms traditional path planning approaches. In the final research topic, the impact of operator driving style on mission energy requirements is investigated using statistical response surface. While the previous topics help with overall mission planning regardless of the operator’s driving style, here, improving the driving style to increase energy availability is studied. The optimal drive cycle that minimizes energy consumption and procedures for reduction of energy consumption are proposed.

Abstract: A new generation of complex interactive dynamic web applications has emerged with the introduc- tion of Web 2.0 technologies and development frameworks. The characteristics of dynamic web applications such as runtime DOM structure and content updates introduced new challenges in the understanding, maintenance and testing of this type of web applications. In this work we address two important challenges in the field of web application maintenance. The first challenge is that of modelling web application behaviour. To solve this task we develop an automatic method for reverse engineering the features of dynamic web applications by applying a hierarchical clustering algorithm based on a novel composite-tree-edits-aware distance metric between DOM tree instances of a web application. The proposed distance metric recognizes simple and composite structural changes in a DOM tree. We have evaluated our method on three real-world web applications. The evaluation results demonstrated that the proposed distance metric produces a number of clusters that is close to the actual number of features and, also, classifies DOM trees into feature clusters more accurately than other traditional distance metrics. The second challenge is that of systematic acceptance (and regression) testing at the user-interface level, which we address by developing a tool, CrawlScripter, for performing automated acceptance testing of JavaScript web applications. CrawlScripter allows to create easy-to-understand acceptance tests using the provided library of high-level instructions. The ability of CrawlScripter to create automated acceptance tests for different test scenarios was evaluated on both pedagogical and real-world dynamic web applications.