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Search operations can be described by the interaction between three entities: the target, the sensor, and the environment. Past treatments of the search problem have focused primarily on the interaction between the sensor and the target. The effects that the environment has on the target and sensor have been greatly simplified or ignored completely. The wilderness search and rescue scenario is one case in which these interactions cannot be safely ignored. Using the wilderness search and rescue problem as our motivating example, we develop an algorithm for planning search paths for a small unmanned aerial vehicle (UAV) over rough terrain environments that provide complete coverage of the specified terrain region while minimizing effort wasted on duplicate coverage. The major components of this algorithm include 1) breaking the search region into smaller sub-regions that are easier to deal with, and 2) planning the search for each of these sub-regions. The original contributions of this thesis focus on the latter of these two components. We use a method based on the directional offset of terrain contours to produce paths on the terrain for the sensor to observe as the UAV follows the flight path. We then employ directional-offset methods again by moving in the direction along the terrain normal from the sensor path to generate a flight path that lies in the air a specified distance away from the points on the terrain that are to be observed. These two paths are linked in a way that provides the sensor with an ample viewing opportunity of the terrain regions below. We implement this planning algorithm in software with Matlab, and provide a complete simulation of a UAV that follows the planned search pattern. Our planning algorithm produced search paths that were 94 to 100 percent complete in test scenarios for several rough-terrain regions. Missed regions for these plans were near the search boundaries, and coverage could easily be provided by subsequent plans. We recommend the study of region segmentation, with careful consideration of planning algorithms as the major focus of future work.

How can you tell whether your test suites are reliable? This is often done through the use of coverage criterion that would define a set of requirements that the test suites need to fulfill in order to be considered reliable. The most widely used criterion is those referred to as code coverage, where the degree to which the code base is covered is used as an arbitrary measure of how good the test suites are. Achieving high coverage would indicate an adequate test suite i.e. reliable according to the standards of code coverage. However, covering a line of code does not necessarily mean that it has been tested. Thus, code coverage can only tell you what parts of the code base have not been tested, opposed to what have been tested. Mutation testing on the other hand is an approach to evaluate the adequacy of test suites through their fault detection ability, rather than how much of the code base they cover.   This thesis performs mutation analysis on a project with inadequate code coverage. The present testing effort on unit level is evaluated and the cost and benefits of adopting mutation testing as a testing method is evaluated. / Hur vet man när tester är tillförlitliga? Ofta använder man sig av täckningskriterium som definierar en uppsättning krav som tester måste uppfylla för att betraktas som pålitlig. Det mest använda kriterier är de som kallas kodtäckning, där graden till vilken kodbasen är täckt används som ett mått av pålitlighet av tester. Hög täckning indikerar adekvat tester, dvs pålitlig enligt kodtäckning. Men täckning av en kodlinje betyder inte nödvändigtvis att den har testats. Koddekning kan således bara visa vilka delar av kodbasen som inte har testats, snarare än vad som har testats. Mutation testing å andra hand är ett sätt att utvärdera testers effektivitet genom deras felsökningsförmåga, snarare än hur mycket av kodbasen de täcker.   Denna examensarbete utför mutationsanalys på ett projekt med otillräcklig koddekning. Kvalite av nuvarande tester på enhetsnivå utvärderas och kostnaden och fördelar för att anta mutation testning som en testmetod utforskas.

The ability to test software is invaluable in all areas of computer science, but it is often neglected in computer science curricula. Test adequacy criteria (TAC), tools that measure the effectiveness of a test suite, have been used as aids to improve software testing teaching practices, but little is known about how students respond to them. Studies have examined the cognitive processes of students programming and professional developers writing tests, but none have investigated how student testers test with TAC. If we are to improve how they are used in the classroom, we must start by understanding the different ways that they affect students’ thought processes as they write tests. In this thesis, we take a grounded theory approach to reveal the underlying cognitive processes that students utilize as they test under no feedback, condition coverage, and mutation analysis. We recorded 12 students as they thought aloud while creating test suites under these feedback mechanisms, and then we analyzed these recordings to identify the thought processes they used. We present our findings in the form of the phenomena we identified, which can be further investigated to shed more light on how different TAC affect students as they write tests. i

The promotion of Electric Vehicles (EV) has become a key measure of the governments to reduce greenhouse gas emissions. However, range anxiety is a big barrier for drivers to choose EVs over traditional vehicles. Installing more charging stations in appropriate locations can relieve EV drivers’ range anxiety. To help decide the location and number of public charging stations, we propose two optimization models for two different charging modes - fast and slow charging, which aim at minimizing the total cost while satisfying certain spatial coverage goals. Instead of using discrete points we employ network and polygons to represent charging demands. Importantly, we resolve the partial coverage problem (PCP) by segmenting the geometric objects into smaller ones using Geographic Information System (GIS) functions. We compare the geometric segmentation method (GS) and the complementary partial coverage method (CP) developed by Murray (2005) to solve the PCP. After applying the models to Greater Toronto and Hamilton Area (GTHA) and to Downtown Toronto, we show that that the proposed models are practical and effective in determining the locations and number of required charging stations. Moreover, comparison of the two methods shows that GS can fully eliminate PCP and provide much more accurate result than CP. / Thesis / Master of Science (MSc)

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