Cause-e ect graphing is a well-known requirement based testing technique. However, since it was i... more Cause-e ect graphing is a well-known requirement based testing technique. However, since it was introduced by Myers in 1979, there seems not to have been any su ciently comprehensive studies to generate test cases from these graphs. This paper proposes to convert cause-e ect graphs into Boolean expressions and nd out the test cases using test input generation techniques for Boolean expressions, such as MI, MAX-A and CUTPNFP. Mutation analysis is used to compare the fault detection capabilities of these techniques and the results are also compared to the Myers' original test generation technique.
Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2016Includes bibliog... more Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2016Includes bibliographical references (leaves: 41-44)Text in English; Abstract: Turkish and Englishx, 52 leavesCause-effect graphing is a well-known requirement based testing technique. However, since it was introduced by Myers in 1979, there seems not to have been any sufficiently comprehensive studies to generate test cases from these graphs. Yet there are several methods introduced to generate test cases from Boolean expressions. This thesis proposes to convert cause-effect graphs into Boolean expressions and find out the test sets using test input generation techniques for Boolean expressions, such as MI, MAX-A, CUTPNFP, MUMCUT, Unique MC/DC and Masking MC/DC. Generated test sets are compared by using mutation analysis according to their fault detection capabilities. Myers’ original test generation technique is also implemented and included in the mutation analysis. A tool is created which allows to g...
Cause–effect graphing is a well-known requirement-based and systematic testing method with a heur... more Cause–effect graphing is a well-known requirement-based and systematic testing method with a heuristic approach. Since it was introduced by Myers in 1979, there have not been any sufficiently comprehensive studies to generate test inputs from these graphs. However, there exist several methods for test input generation from Boolean expressions. Cause–effect graphs can be more convenient for a wide variety of users compared to Boolean expressions. Moreover, they can be used to enforce common constraints and rules on the system variables of different expressions of the system. This study proposes a new mutant-based test input generation method, Spectral Testing for Boolean specification models based on spectral analysis of Boolean expressions using mutations of the original expression. Unlike Myers’ method, Spectral Testing is an algorithmic and deterministic method, in which we model the possible faults systematically. Furthermore, the conversion of cause–effect graphs between Boolean...
Fast, efficient and robust algorithms are needed for real-time visual tracking that could also ru... more Fast, efficient and robust algorithms are needed for real-time visual tracking that could also run smoothly on the airborne embedded systems. Flux tensor can be used to provide motion-based cues in visual tracking. In order to use any object motion detection on a raw image sequence captured by a moving platform, the motion caused by the camera movement must be stabilized first. Using feature points to estimate the homography matrix between the frames is a simple registration method that can be used for the stabilization. In order to have a good homography estimation, most of the feature points should lay on the same plane in the images. However, when the scene has complex structures it becomes very challenging to estimate a good homography. In this work, we propose a robust video stabilization algorithm which allows the flux motion detection to efficiently identify moving objects. Our experiments show satisfactory results when other methods shown to fail on the same type of raw videos.
Unmanned Aerial Vehicle (UAV) systems with high-resolution video cameras are used for many operat... more Unmanned Aerial Vehicle (UAV) systems with high-resolution video cameras are used for many operations such as aerial imaging, search and rescue, and precision agriculture. Multi-drone systems operating in Flying Ad Hoc Networks (FANETS) are inherently insecure and require efficient security schemes to defend against cyber-attacks such as e.g., Man-in-the-middle, Replay and Denial of Service attacks. In this paper, we propose a cloud-based, end-to-end security framework viz., "DroneNet-Sec" that provides secure network-edge connectivity, and computation security for drone video analytics to defend against common attack vectors in UAV systems. The DroneNet-Sec features a dynamic security scheme that uses machine learning to detect anomaly events and adopts countermeasures for computation security of containerized video analytics tasks. The security scheme comprises of a custom secure packet designed with MAVLink protocol for ensuring data privacy and integrity, without high ...
TEST CASE GENERATION FROM CAUSE EFFECT GRAPHS Cause-effect graphing is a well-known requirement b... more TEST CASE GENERATION FROM CAUSE EFFECT GRAPHS Cause-effect graphing is a well-known requirement based testing technique. However, since it was introduced by Myers in 1979, there seems not to have been any sufficiently comprehensive studies to generate test cases from these graphs. Yet there are several methods introduced to generate test cases from Boolean expressions. This thesis proposes to convert cause-effect graphs into Boolean expressions and find out the test sets using test input generation techniques for Boolean expressions, such as MI, MAX-A, CUTPNFP, MUMCUT, Unique MC/DC and Masking MC/DC. Generated test sets are compared by using mutation analysis according to their fault detection capabilities. Myers’ original test generation technique is also implemented and included in the mutation analysis. A tool is created which allows to generate test cases by using the implemented algorithms. The tool gets a “.graphml” file representing a causeeffect graph as an input and gives t...
2020 IEEE International Conference on Image Processing (ICIP), 2020
RTip is a tool to quantify plant root growth velocity using high resolution microscopy image sequ... more RTip is a tool to quantify plant root growth velocity using high resolution microscopy image sequences at sub-pixel accuracy. The fully automated RTip tracker is designed for high-throughput analysis of plant phenotyping experiments with episodic perturbations. RTip is able to auto-skip past these manual intervention perturbation activity, i.e. when the root tip is not under the microscope, image is distorted or blurred. RTip provides the most accurate root growth velocity results with the lowest variance (i.e. localization jitter) compared to six tracking algorithms including the top performing unsupervised Discriminative Correlation Filter Tracker and the Deeper and Wider Siamese Network. RTip is the only tracker that is able to automatically detect and recover from (occlusion-like) varying duration perturbation events.
Cause-e ect graphing is a well-known requirement based testing technique. However, since it was i... more Cause-e ect graphing is a well-known requirement based testing technique. However, since it was introduced by Myers in 1979, there seems not to have been any su ciently comprehensive studies to generate test cases from these graphs. This paper proposes to convert cause-e ect graphs into Boolean expressions and nd out the test cases using test input generation techniques for Boolean expressions, such as MI, MAX-A and CUTPNFP. Mutation analysis is used to compare the fault detection capabilities of these techniques and the results are also compared to the Myers' original test generation technique.
Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2016Includes bibliog... more Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2016Includes bibliographical references (leaves: 41-44)Text in English; Abstract: Turkish and Englishx, 52 leavesCause-effect graphing is a well-known requirement based testing technique. However, since it was introduced by Myers in 1979, there seems not to have been any sufficiently comprehensive studies to generate test cases from these graphs. Yet there are several methods introduced to generate test cases from Boolean expressions. This thesis proposes to convert cause-effect graphs into Boolean expressions and find out the test sets using test input generation techniques for Boolean expressions, such as MI, MAX-A, CUTPNFP, MUMCUT, Unique MC/DC and Masking MC/DC. Generated test sets are compared by using mutation analysis according to their fault detection capabilities. Myers’ original test generation technique is also implemented and included in the mutation analysis. A tool is created which allows to g...
Cause–effect graphing is a well-known requirement-based and systematic testing method with a heur... more Cause–effect graphing is a well-known requirement-based and systematic testing method with a heuristic approach. Since it was introduced by Myers in 1979, there have not been any sufficiently comprehensive studies to generate test inputs from these graphs. However, there exist several methods for test input generation from Boolean expressions. Cause–effect graphs can be more convenient for a wide variety of users compared to Boolean expressions. Moreover, they can be used to enforce common constraints and rules on the system variables of different expressions of the system. This study proposes a new mutant-based test input generation method, Spectral Testing for Boolean specification models based on spectral analysis of Boolean expressions using mutations of the original expression. Unlike Myers’ method, Spectral Testing is an algorithmic and deterministic method, in which we model the possible faults systematically. Furthermore, the conversion of cause–effect graphs between Boolean...
Fast, efficient and robust algorithms are needed for real-time visual tracking that could also ru... more Fast, efficient and robust algorithms are needed for real-time visual tracking that could also run smoothly on the airborne embedded systems. Flux tensor can be used to provide motion-based cues in visual tracking. In order to use any object motion detection on a raw image sequence captured by a moving platform, the motion caused by the camera movement must be stabilized first. Using feature points to estimate the homography matrix between the frames is a simple registration method that can be used for the stabilization. In order to have a good homography estimation, most of the feature points should lay on the same plane in the images. However, when the scene has complex structures it becomes very challenging to estimate a good homography. In this work, we propose a robust video stabilization algorithm which allows the flux motion detection to efficiently identify moving objects. Our experiments show satisfactory results when other methods shown to fail on the same type of raw videos.
Unmanned Aerial Vehicle (UAV) systems with high-resolution video cameras are used for many operat... more Unmanned Aerial Vehicle (UAV) systems with high-resolution video cameras are used for many operations such as aerial imaging, search and rescue, and precision agriculture. Multi-drone systems operating in Flying Ad Hoc Networks (FANETS) are inherently insecure and require efficient security schemes to defend against cyber-attacks such as e.g., Man-in-the-middle, Replay and Denial of Service attacks. In this paper, we propose a cloud-based, end-to-end security framework viz., "DroneNet-Sec" that provides secure network-edge connectivity, and computation security for drone video analytics to defend against common attack vectors in UAV systems. The DroneNet-Sec features a dynamic security scheme that uses machine learning to detect anomaly events and adopts countermeasures for computation security of containerized video analytics tasks. The security scheme comprises of a custom secure packet designed with MAVLink protocol for ensuring data privacy and integrity, without high ...
TEST CASE GENERATION FROM CAUSE EFFECT GRAPHS Cause-effect graphing is a well-known requirement b... more TEST CASE GENERATION FROM CAUSE EFFECT GRAPHS Cause-effect graphing is a well-known requirement based testing technique. However, since it was introduced by Myers in 1979, there seems not to have been any sufficiently comprehensive studies to generate test cases from these graphs. Yet there are several methods introduced to generate test cases from Boolean expressions. This thesis proposes to convert cause-effect graphs into Boolean expressions and find out the test sets using test input generation techniques for Boolean expressions, such as MI, MAX-A, CUTPNFP, MUMCUT, Unique MC/DC and Masking MC/DC. Generated test sets are compared by using mutation analysis according to their fault detection capabilities. Myers’ original test generation technique is also implemented and included in the mutation analysis. A tool is created which allows to generate test cases by using the implemented algorithms. The tool gets a “.graphml” file representing a causeeffect graph as an input and gives t...
2020 IEEE International Conference on Image Processing (ICIP), 2020
RTip is a tool to quantify plant root growth velocity using high resolution microscopy image sequ... more RTip is a tool to quantify plant root growth velocity using high resolution microscopy image sequences at sub-pixel accuracy. The fully automated RTip tracker is designed for high-throughput analysis of plant phenotyping experiments with episodic perturbations. RTip is able to auto-skip past these manual intervention perturbation activity, i.e. when the root tip is not under the microscope, image is distorted or blurred. RTip provides the most accurate root growth velocity results with the lowest variance (i.e. localization jitter) compared to six tracking algorithms including the top performing unsupervised Discriminative Correlation Filter Tracker and the Deeper and Wider Siamese Network. RTip is the only tracker that is able to automatically detect and recover from (occlusion-like) varying duration perturbation events.
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